From 9e979c39a499ebe1a2adadcedb9c4ffd32e56ef1 Mon Sep 17 00:00:00 2001 From: 4R5T Date: Sat, 4 Oct 2025 21:47:59 -0500 Subject: [PATCH 01/14] Support gpt5 --- tiny_scientist/utils/llm.py | 72 +++++++++++++++++++++++++++------ tiny_scientist/utils/pricing.py | 1 + 2 files changed, 61 insertions(+), 12 deletions(-) diff --git a/tiny_scientist/utils/llm.py b/tiny_scientist/utils/llm.py index 7f4d0ba..33a319e 100644 --- a/tiny_scientist/utils/llm.py +++ b/tiny_scientist/utils/llm.py @@ -30,6 +30,7 @@ "gpt-4o-mini-2024-07-18", "gpt-4o-2024-05-13", "gpt-4o-2024-08-06", + "gpt-5", "o1-preview-2024-09-12", "o1-mini-2024-09-12", "o1-2024-12-17", @@ -142,6 +143,28 @@ def get_batch_responses_from_llm( output_tokens = getattr(response.usage, "completion_tokens", 0) if cost_tracker is not None: cost_tracker.add_cost(model, input_tokens, output_tokens, task_name) + elif model.startswith("gpt-5"): + new_msg_history = msg_history + [{"role": "user", "content": msg}] + response = client.chat.completions.create( + model=model, + messages=[ + {"role": "system", "content": system_message}, + *new_msg_history, + ], + max_completion_tokens=MAX_NUM_TOKENS, + n=n_responses, + stop=None, + seed=0, + ) + content = [r.message.content for r in response.choices] + new_msg_history = [ + new_msg_history + [{"role": "assistant", "content": c}] for c in content + ] + if hasattr(response, "usage"): + input_tokens = getattr(response.usage, "prompt_tokens", 0) + output_tokens = getattr(response.usage, "completion_tokens", 0) + if cost_tracker is not None: + cost_tracker.add_cost(model, input_tokens, output_tokens, task_name) elif any( model.startswith(prefix) for prefix in ["meta-llama/", "Qwen/", "deepseek-ai/", "mistralai/"] @@ -282,6 +305,24 @@ def get_response_from_llm( if hasattr(response, "usage"): input_tokens = getattr(response.usage, "prompt_tokens", 0) output_tokens = getattr(response.usage, "completion_tokens", 0) + elif model.startswith("gpt-5"): + new_msg_history = msg_history + [{"role": "user", "content": msg}] + response = client.chat.completions.create( + model=model, + messages=[ + {"role": "system", "content": system_message}, + *new_msg_history, + ], + max_completion_tokens=MAX_NUM_TOKENS, + n=1, + stop=None, + seed=0, + ) + content = response.choices[0].message.content + new_msg_history = new_msg_history + [{"role": "assistant", "content": content}] + if hasattr(response, "usage"): + input_tokens = getattr(response.usage, "prompt_tokens", 0) + output_tokens = getattr(response.usage, "completion_tokens", 0) elif model in ["o1-preview-2024-09-12", "o1-mini-2024-09-12"]: new_msg_history = msg_history + [{"role": "user", "content": msg}] response = client.chat.completions.create( @@ -461,20 +502,27 @@ def get_batch_responses_from_llm_with_tools( "gpt" in model or model in ["o1-preview-2024-09-12", "o1-mini-2024-09-12"] ): new_msg_history = msg_history + [{"role": "user", "content": msg}] + kwargs = { + "model": model, + "messages": [ + {"role": "system", "content": system_message}, + *new_msg_history, + ], + "tools": tools, + "tool_choice": "auto", + "n": n_responses, + "stop": None, + "seed": 0, + } + if model.startswith("gpt-5"): + kwargs["max_completion_tokens"] = MAX_NUM_TOKENS + else: + kwargs["temperature"] = temperature + kwargs["max_tokens"] = MAX_NUM_TOKENS + try: response = client.chat.completions.create( # type: ignore[call-overload] - model=model, - messages=[ - {"role": "system", "content": system_message}, - *new_msg_history, - ], - tools=tools, - tool_choice="auto", # Or specify a tool like {"type": "function", "function": {"name": "my_function"}} - temperature=temperature, - max_tokens=MAX_NUM_TOKENS, - n=n_responses, - stop=None, - seed=0, # Seed might not be available for all models or with tool use + **kwargs ) # Extract token usage for OpenAI diff --git a/tiny_scientist/utils/pricing.py b/tiny_scientist/utils/pricing.py index 1fc0a14..4dd5f3d 100644 --- a/tiny_scientist/utils/pricing.py +++ b/tiny_scientist/utils/pricing.py @@ -7,6 +7,7 @@ "gpt-3.5-turbo": (0.5, 1.5), "gpt-4o-mini": (0.15, 0.6), "gpt-4o": (2.5, 10), + "gpt-5": (1.25, 10), "o1-preview": (15, 60), "o1-mini": (1.1, 4.4), "o1": (15, 60), From e334e517a3e83454cdce7f5186d4678976c72656 Mon Sep 17 00:00:00 2001 From: 4R5T Date: Tue, 21 Oct 2025 00:38:06 -0500 Subject: [PATCH 02/14] fix coder --- examples/demo.py | 64 ++++++++++------------ tiny_scientist/coder.py | 48 +++++++++++++--- tiny_scientist/mcp/docker_runner_server.py | 20 +++---- tiny_scientist/prompts/coder_prompt.yaml | 58 ++++++++++++++------ tiny_scientist/prompts/thinker_prompt.yaml | 31 ++++++++++- tiny_scientist/thinker.py | 8 +++ tiny_scientist/utils/llm.py | 11 ++++ 7 files changed, 164 insertions(+), 76 deletions(-) diff --git a/examples/demo.py b/examples/demo.py index a8a785e..e01e394 100644 --- a/examples/demo.py +++ b/examples/demo.py @@ -1,6 +1,5 @@ import argparse import json -import os from tiny_scientist.scientist import TinyScientist @@ -10,7 +9,7 @@ def main() -> None: parser.add_argument( "--output_dir", type=str, - default="experiments/demo_3", + default="demo_test", help="Base output directory", ) parser.add_argument( @@ -42,7 +41,7 @@ def main() -> None: ) args = parser.parse_args() - ideas = [ + ideas = [ # noqa: F841 "Adaptive Confidence-Guided Prompting for Improved Factuality in Large Language Models", "Adaptive Contextual Pruning: Improving Relevance and Conciseness in Long-Form Generation", "Adaptive Prompt Decomposition for Coherent Long-Range Code Generation", @@ -64,44 +63,39 @@ def main() -> None: "Temporal Bias Decay Simulation: Reducing Social Biases in Large Language Models through Evolutionary Prompting", "Temporal Dependency Unfolding: Improving Code Generation for Complex Stateful Systems", ] - for idx, idea in enumerate(ideas): - safe_idea = idea.replace(" ", "_") - args.output_dir = f"experiments/non_bio_{safe_idea}" - os.makedirs(args.output_dir, exist_ok=True) - - # Instantiate TinyScientist and run pipeline - scientist = TinyScientist( - model=args.model, - output_dir=args.output_dir, - prompt_template_dir=args.prompt_template_dir, - template=args.template, - enable_safety_check=args.enable_safety_check, - budget=args.budget, - ) - - idea = scientist.think( - intent="Adaptive Confidence-Guided Prompting for Improved Factuality in Large Language Models" - ) + # Instantiate TinyScientist and run pipeline + scientist = TinyScientist( + model=args.model, + output_dir=args.output_dir, + prompt_template_dir=args.prompt_template_dir, + template=args.template, + enable_safety_check=args.enable_safety_check, + budget=args.budget, + ) - with open(args.output_dir + "/idea.json", "w") as f: - json.dump(idea, f, indent=2) + idea = scientist.think( + intent="Adaptive Prompt Decomposition for Coherent Long-Range Code Generation", + ) - status, experiment_dir = scientist.code(idea=idea) + with open(args.output_dir + "/idea.json", "w") as f: + json.dump(idea, f, indent=2) + status, experiment_dir = scientist.code(idea=idea) - if status is True: - pdf_path = scientist.write(idea=idea, experiment_dir=experiment_dir) + if status is True: + print(f"✅Experiment code generated at: {experiment_dir}") + # pdf_path = scientist.write(idea=idea, experiment_dir=experiment_dir) - # Display total cost summary - scientist.get_total_cost() + # # Display total cost summary + # scientist.get_total_cost() - tex_path = args.output_dir + "/latex/acl_latex.tex" - review = scientist.review( - tex_path=tex_path, - ) - print(review) - with open(args.output_dir + "/review.json", "w") as f: - json.dump(review, f, indent=2) + # tex_path = args.output_dir + "/latex/acl_latex.tex" + # review = scientist.review( + # tex_path=tex_path, + # ) + # print(review) + # with open(args.output_dir + "/review.json", "w") as f: + # json.dump(review, f, indent=2) if __name__ == "__main__": diff --git a/tiny_scientist/coder.py b/tiny_scientist/coder.py index 3aa745c..d76f952 100644 --- a/tiny_scientist/coder.py +++ b/tiny_scientist/coder.py @@ -133,10 +133,14 @@ def run( return True, self.output_dir, None def _format_experiment_for_prompt( - self, exp: Dict[str, str] - ) -> Tuple[str, str, str]: + self, exp: Dict[str, Any] + ) -> Tuple[str, str, str, str, str, str]: + model_section = self._serialize_for_prompt(exp.get("Model", "")) + dataset_section = self._serialize_for_prompt(exp.get("Dataset", "")) + metric_section = self._serialize_for_prompt(exp.get("Metric", "")) + llm_prompt = self.prompts.experiment_keyword_prompt.format( - model=exp["Model"], dataset=exp["Dataset"], metric=exp["Metric"] + model=model_section, dataset=dataset_section, metric=metric_section ) llm_output, _ = get_response_from_llm( @@ -164,7 +168,23 @@ def _format_experiment_for_prompt( dataset_kw = ", ".join(keyword_info.get("dataset", [])) metric_kw = ", ".join(keyword_info.get("metric", [])) - return model_kw, dataset_kw, metric_kw + return ( + model_kw, + dataset_kw, + metric_kw, + model_section, + dataset_section, + metric_section, + ) + + @staticmethod + def _serialize_for_prompt(section: Any) -> str: + if isinstance(section, (dict, list)): + try: + return json.dumps(section, indent=2, ensure_ascii=False) + except TypeError: + return str(section) + return str(section) def _summarize_to_bullets(self, paragraph: str) -> str: # Simple sentence-splitting bullet conversion @@ -178,17 +198,27 @@ def _run_experiment_loop( current_iter = 0 run_time = 1 - # Initial prompt - model, dataset, metric = self._format_experiment_for_prompt(idea["Experiment"]) + experiment_spec = idea["Experiment"] + ( + model_kw, + dataset_kw, + metric_kw, + model_text, + dataset_text, + metric_text, + ) = self._format_experiment_for_prompt(experiment_spec) next_prompt = self.prompts.experiment_prompt.format( title=idea["Title"], problem=idea["Problem"], novelty=idea["NoveltyComparison"], approach=idea["Approach"], - model=model, - dataset=dataset, - metric=metric, + model_keywords=model_kw, + dataset_keywords=dataset_kw, + metric_keywords=metric_kw, + model_details=model_text, + dataset_details=dataset_text, + metric_details=metric_text, max_runs=self.max_runs, baseline_results=baseline_results, ) diff --git a/tiny_scientist/mcp/docker_runner_server.py b/tiny_scientist/mcp/docker_runner_server.py index e947246..8bd7664 100644 --- a/tiny_scientist/mcp/docker_runner_server.py +++ b/tiny_scientist/mcp/docker_runner_server.py @@ -348,18 +348,10 @@ def get_or_build_experiment_image(self, experiment_py_path: str) -> Optional[str ) try: # Build with detailed logging - build_logs = self.docker_client.images.build( - path=tmpdir, tag=image_name, rm=True, decode=True + image, build_logs = self.docker_client.images.build( + path=tmpdir, tag=image_name, rm=True ) - # Check for build errors - for log in build_logs: - if "error" in log: - print(f"[Docker] Build error: {log['error']}") - raise Exception( - f"Docker build failed: {log['error']}" - ) - elif "stream" in log: - print(f"[Docker] {log['stream'].strip()}") + print(f"[Docker] Successfully built image: {image_name}") except Exception as e: print(f"[Docker] Failed to build image {image_name}: {e}") # Fallback to base image @@ -444,6 +436,12 @@ def run_experiment_in_docker( shutil.copytree(src, dst, dirs_exist_ok=True) return (0, logs) else: + # Print error logs to stderr so they're visible + print( + f"[Docker] Container failed with logs:\n{logs}", + file=sys.stderr, + ) + # Check if it's a missing package error if "ModuleNotFoundError" in logs: missing_pkg = self.extract_missing_package(logs) diff --git a/tiny_scientist/prompts/coder_prompt.yaml b/tiny_scientist/prompts/coder_prompt.yaml index 4ee8f8b..b4a7030 100644 --- a/tiny_scientist/prompts/coder_prompt.yaml +++ b/tiny_scientist/prompts/coder_prompt.yaml @@ -30,11 +30,35 @@ experiment_prompt: | Proposed Approach: {approach} ## Experimental Setup - The following describes the experiment setup. You must base your implementation strictly on this structure: + The following describes the experiment setup. You must base your implementation strictly on this structure and keep it lightweight. - Models/Algorithms to use: {model} - Datasets involved: {dataset} - Evaluation metrics: {metric} + Models/Algorithms (key terms): {model_keywords} + Detailed model specification: + {model_details} + + Datasets (key terms): {dataset_keywords} + Detailed dataset specification (use this loader exactly — do not invent file paths): + {dataset_details} + + Evaluation metrics (key terms): {metric_keywords} + Detailed metric specification: + {metric_details} + + ## Hard Constraints + - Use only lightweight architectures: bag-of-words/logistic regression, shallow MLP (≤2 hidden layers, ≤128 units), shallow CNN (≤3 conv layers, ≤32 filters), linear SVM, single-layer GRU (≤64 hidden units). Total parameter count should stay under ~100k. + - ABSOLUTELY FORBIDDEN: importing `transformers`, `BertTokenizer`, any BERT/GPT/ViT class, graph neural networks, diffusion models, random feature generators (`torch.rand`, `np.random` placeholders for embeddings), manual placeholder paths, loading huge pretrained checkpoints. + - Dataset must be loaded via the provided command. If it fails, switch to one of: `datasets.load_dataset("ag_news")`, `datasets.load_dataset("imdb")`, `datasets.load_dataset("yelp_polarity")`, `datasets.load_dataset("cifar10")`, `datasets.load_dataset("mnist")`, `datasets.load_dataset("fashion_mnist")`, `datasets.load_dataset("amazon_polarity")`, or an equivalent auto-downloadable dataset. NEVER fall back to local files. For text, prefer simple vectorizers (`CountVectorizer`, `TfidfVectorizer`) over tokenizers. + - Always subsample large datasets: cap training examples at ≤5,000 and validation/test at ≤2,000 each (use `select(range(...))` or appropriate slicing). Document this in code comments. + + ## Required Script Structure + Your script MUST include the following components (you may adapt names, but all roles must be present): + 1. `load_data()` – downloads the dataset using the loader above, subsamples to the limits (≤5k train / ≤2k val/test), and converts examples into explicit tensors/arrays (`torch.tensor` or `numpy.array`). If using HuggingFace datasets, call `.set_format(type="torch", columns=[...])` or manually build tensors, then wrap them with `torch.utils.data.TensorDataset` + `DataLoader` (or sklearn splits if you stay in sklearn). Include a brief comment explaining why this preprocessing keeps the experiment lightweight. + 2. `build_model()` – constructs the lightweight model defined in the plan. Document input/output dimensions explicitly, and add a short justification for why this architecture is the simplest option that still addresses the task. + 3. `train(model, train_loader, optimizer, criterion, device)` – iterates over batches, moves tensors to `device`, computes loss, `loss.backward()`, `optimizer.step()`. Log the epoch count and explain in a comment why the number of epochs is sufficient. + 4. `evaluate(model, data_loader, device)` – returns a dict of metrics computed from real predictions vs ground truth. Include a comment explaining why the chosen metrics match the scientific goal. + 5. `main(out_dir)` – orchestrates the steps above, saves `final_info.json` with metric values (no placeholder numbers), and prints dataset sizes so we can verify subsampling. + + Keep training loops short (≤5 epochs, batch_size ≤64) so the experiment finishes quickly on CPU. If the dataset is extremely small, 3 epochs are usually enough. ## Execution Command (DO NOT MODIFY): You have {max_runs} runs to complete this experiment. For each run, the script will be executed using: @@ -42,15 +66,12 @@ experiment_prompt: | where `i` is the run number (`run_1`, `run_2`, etc.). ## YOU MUST ENSURE experiment.py: - 1. Parses the `--out_dir` argument. - 2. Creates the output directory using `os.makedirs(out_dir, exist_ok=True)`. - 3. **CRITICAL: Performs REAL model training and evaluation with actual data loading, model forward/backward passes, and gradient updates.** - - ❌ ABSOLUTELY FORBIDDEN: Using `np.random.random()`, `torch.rand()`, or any simulated/dummy results - - ❌ ABSOLUTELY FORBIDDEN: Hardcoded or placeholder results like `{{"accuracy": 0.85}}` - - ✅ REQUIRED: Load real datasets, define real model architectures, run actual training loops - - ✅ REQUIRED: All metrics must be computed from actual model predictions vs ground truth - 4. Implements evaluation metrics with real logic (e.g., actual accuracy/F1/loss computation). - 5. **Saves results as a dictionary in a file named `final_info.json` placed directly inside `out_dir`** — do **not** save into nested folders like `out_dir/variant_name/final_info.json`. + 1. Parses the `--out_dir` argument and calls `os.makedirs(out_dir, exist_ok=True)`. + 2. Loads the dataset using the specified loader (or the fallback whitelist above) with NO manual file paths. + 3. Builds a lightweight model that matches the dataset feature shape; document tensor dimensions in code comments when not obvious. No hidden “placeholder embeddings”. + 4. Performs REAL training with gradient updates (`optimizer.zero_grad()`, `loss.backward()`, `optimizer.step()`) on actual features (no zero/random stand-ins). + 5. Computes metrics from real predictions (e.g., accuracy/F1) using sklearn/torch utilities—no hardcoded results. + 6. Saves a dict of metrics to `{{out_dir}}/final_info.json` (no nested folders). ## Computational Constraints - Ensure the code is computationally affordable to run on a single GPU or CPU machine. @@ -61,11 +82,12 @@ experiment_prompt: | Do not add extra command-line arguments. **VERIFICATION CHECKLIST** (You MUST pass all checks): - - [ ] Does the code load a real dataset (not random data)? - - [ ] Does the code define a real model architecture (not a dummy class)? - - [ ] Does the code have a training loop with actual optimizer.step() calls? - - [ ] Are metrics computed from real predictions (not random numbers)? - - [ ] Does final_info.json contain results from actual execution? + - [ ] Dataset is downloaded via library loader (no placeholder paths, no mocks, no random tensors) and converted to tensors/arrays explicitly. Document any subsampling (≤5k train / ≤2k val/test). + - [ ] Model is a lightweight architecture (no transformer/graph/pretrained behemoth) and input/output shapes line up. Forbidden imports (`transformers`, BERT classes) are absent. + - [ ] Training loop performs `optimizer.zero_grad()`, `loss.backward()`, `optimizer.step()` on real batches (no synthetic placeholder features). + - [ ] Evaluation computes metrics from actual predictions vs labels (no dummy numbers). + - [ ] `final_info.json` is written in `out_dir` with real metric values. + - [ ] Sanity check: print or log dataset sizes and confirm they respect the limits; mention parameter count estimate in a comment. If your current experiment.py has placeholder code like `...`, replace them with runnable implementations. If any external functions like `compute_loss`, `evaluate_model`, or `log_results` are used, implement them too. diff --git a/tiny_scientist/prompts/thinker_prompt.yaml b/tiny_scientist/prompts/thinker_prompt.yaml index 6eae5e7..3a3669e 100644 --- a/tiny_scientist/prompts/thinker_prompt.yaml +++ b/tiny_scientist/prompts/thinker_prompt.yaml @@ -523,19 +523,44 @@ experiment_plan_prompt: | In , briefly discuss your reasoning behind the chosen approach. In , provide the plan in JSON format with these fields: - - "Model": Detailed description of model architecture with specific components - - "Dataset": Detailed description of dataset including size, split, preprocessing - - "Metric": Description of evaluation metrics with justification + - "Model": Detailed description of model architecture with specific components. You must stay within LIGHTWEIGHT architectures: + * Allowed families: bag-of-words/logistic regression, shallow MLP/FFN (≤2 hidden layers, ≤128 units), shallow CNN (≤3 convolutional layers, ≤32 filters), linear SVM, single-layer GRU (≤64 hidden units). + * Forbidden: Transformers (BERT, GPT, ViT, etc.), large pretrained backbones, graph neural networks, diffusion models, custom random feature generators. + * Document input/output dimensions so coder can wire tensors without guessing. Provide literal numeric values (e.g., use 3072 instead of `32 * 32 * 3`). + * Absolutely NO comments (`//` or `/* */`) inside JSON values. + * Keep parameter counts tiny: total hidden units per layer ≤ 128, total trainable parameters ≤ ~100k. + - "Dataset": Detailed description of dataset including: + * "Name": MUST be a real dataset available on HuggingFace Datasets (verify it exists!) + * "Size": Number of samples (limit each split to ≤ 5,000 training examples and ≤ 2,000 for validation/test in the initial plan to keep runtime low) + * "Splits": Train/validation/test split ratios (explicitly state how you will downsample large datasets to the limits above) + * "Preprocessing": Required preprocessing steps (e.g., lowercasing text, CountVectorizer/TF-IDF, image normalization). Specify resulting feature shapes. + * Absolutely NO comments (`//` or `/* */`) or inline expressions inside the JSON. + * "Load_Command": The exact Python call that loads the dataset programmatically (e.g., `datasets.load_dataset("ag_news")`, `datasets.load_dataset("imdb")`, `sklearn.datasets.load_iris()`). The identifier MUST be valid on the target library—generic names like `'coco'` or any path placeholders are forbidden. If a dataset needs manual downloads, select a different public dataset that can be fetched automatically. + * CRITICAL: Stay within this downloadable shortlist unless the intent explicitly demands otherwise: `{{"ag_news", "imdb", "yelp_polarity", "cifar10", "mnist", "fashion_mnist", "amazon_polarity"}}`. If none fit, clearly justify an alternative that is still auto-downloadable. + - "Metric": Description of evaluation metrics with justification (use key name "Metric", NOT "Evaluation_Metrics") + * Include a short self-check note confirming that dataset size limits, model simplicity, and lack of comments are satisfied. In , provide a DETAILED experimental plan as a Markdown table. This table MUST include the following columns and rows: - "Component": The part of the experiment - "Specification": A DETAILED description with specifics (not vague descriptions) - "Justification / Rationale": Why this choice is appropriate, citing relevant literature - "Status": Leave this column EMPTY + - Include an additional row titled **Sanity Checks** listing: + * Dataset subsampling strategy confirming ≤5,000 train / ≤2,000 val/test examples. + * Model parameter count estimate (≤100k parameters). + * Confirmation that JSON contains no inline comments or expressions. **REQUIRED ROWS** (at minimum): 1. **Model Architecture**: Specify exact architecture, layers, dimensions, parameters 2. **Dataset**: Name, size, splits (train/val/test), preprocessing steps + - **CRITICAL DATASET REQUIREMENT**: You MUST use a dataset that is: + a) Publicly available on HuggingFace Datasets Hub (https://huggingface.co/datasets) and can be loaded with `datasets.load_dataset()`, OR + b) A standard benchmark dataset with clear download instructions (e.g., via torchvision, tensorflow_datasets) + - ❌ DO NOT invent dataset names (e.g., "Gutenberg Dataset" does not exist on HuggingFace) + - ✅ Prefer the shortlist `ag_news`, `imdb`, `yelp_polarity`, `cifar10`, `mnist`, `fashion_mnist`, `amazon_polarity`. If intent requires a different dataset, confirm it is auto-downloadable and provide the exact `datasets.load_dataset` call. + - ❌ DO NOT use placeholder file paths such as `path/to/...`; rely on library loaders only + - ✅ Verify the dataset exists (including the config/split name) before specifying it + - Include the exact load command: e.g., `datasets.load_dataset("wikitext", "wikitext-2-raw-v1")` 3. **Baselines**: List 3-5 specific baseline methods to compare against (with citations) 4. **Training Setup**: Optimizer, learning rate, batch size, epochs, hardware 5. **Evaluation Metrics**: Primary and secondary metrics with justification diff --git a/tiny_scientist/thinker.py b/tiny_scientist/thinker.py index 2af13e1..7e65a16 100644 --- a/tiny_scientist/thinker.py +++ b/tiny_scientist/thinker.py @@ -152,6 +152,10 @@ def _process_single_idea( # Apply comprehensive safety check if enabled current_idea_final = self._safety_check(current_idea_final) + if not current_idea_final: + print("[Thinker] Safety check returned empty output; discarding this idea.") + return None + current_idea_dict = json.loads(current_idea_final) print( @@ -846,6 +850,10 @@ def _safety_check(self, idea_json: str) -> str: Returns: str: Modified idea JSON with enhanced safety """ + if idea_json is None: + print("⚠️ Safety check skipped: received empty idea JSON.") + return idea_json + if not self.enable_safety_check or not self.safety_checker: return idea_json diff --git a/tiny_scientist/utils/llm.py b/tiny_scientist/utils/llm.py index 7f4d0ba..54a5fa6 100644 --- a/tiny_scientist/utils/llm.py +++ b/tiny_scientist/utils/llm.py @@ -7,6 +7,7 @@ import backoff import openai import toml +import yaml from google.generativeai.types import GenerationConfig from tiny_scientist.budget_checker import BudgetChecker @@ -690,6 +691,16 @@ def fix_invalid_escapes(match): print("[SUCCESS] Fixed JSON parsing errors and parsed successfully") return parsed_json except json.JSONDecodeError as e2: + # Fallback: attempt to parse with YAML loader to tolerate minor JSON deviations + for candidate in (fixed_string, json_string): + try: + parsed_yaml = yaml.safe_load(candidate) + except yaml.YAMLError: + continue + if isinstance(parsed_yaml, dict): + print("[SUCCESS] Parsed JSON via YAML fallback") + return cast(Dict[str, Any], parsed_yaml) + # Log the error for debugging print(f"[ERROR] JSON parse error: {e}") print(f"[ERROR] After fixes, still failed: {e2}") From 46b65477cefa9d05c86cf202ee59195ca226255f Mon Sep 17 00:00:00 2001 From: 4R5T Date: Fri, 24 Oct 2025 08:31:24 -0500 Subject: [PATCH 03/14] update --- backend/app.py | 131 ++-- backend/tests/test_coder_demo.py | 76 +++ frontend/package.json | 1 - frontend/src/components/LogDisplay.jsx | 39 +- .../src/components/TreePlotVisualization.jsx | 637 +++++++++++++----- frontend/src/setupProxy.js | 12 + poetry.lock | 112 ++- pyproject.toml | 1 + tiny_scientist/mcp/docker_runner_server.py | 55 +- 9 files changed, 804 insertions(+), 260 deletions(-) create mode 100644 backend/tests/test_coder_demo.py create mode 100644 frontend/src/setupProxy.js diff --git a/backend/app.py b/backend/app.py index d6c6d37..45fc392 100644 --- a/backend/app.py +++ b/backend/app.py @@ -1,41 +1,74 @@ import builtins -import io +import logging import os import sys +import time from typing import Any, Dict, Optional, Union -from flask import Flask, Response, jsonify, request, send_file, session -from flask_cors import CORS -from flask_socketio import SocketIO +import eventlet + +eventlet.monkey_patch() + +from flask import Flask, Response, jsonify, request, send_file, session # noqa: E402 +from flask_cors import CORS # noqa: E402 +from flask_socketio import SocketIO # noqa: E402 project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) if project_root not in sys.path: sys.path.insert(0, project_root) + original_print = builtins.print -original_stdout = sys.stdout -# Buffer to store messages until socketio is ready -log_buffer = [] +_LOG_BUFFER: list[dict[str, Any]] = [] +_MAX_LOG_BUFFER_LENGTH = 500 -class WebSocketCapture(io.StringIO): - def write(self, text: str) -> int: - # Also write to original stdout - original_stdout.write(text) - # Store for WebSocket emission - if text.strip(): # Only non-empty messages - log_buffer.append(text.strip()) - return len(text) +def _push_log(message: str, level: str = "info") -> None: + message = message.strip() + if not message: + return + payload = { + "message": message, + "level": level, + "timestamp": time.time(), + } + _LOG_BUFFER.append(payload) + if len(_LOG_BUFFER) > _MAX_LOG_BUFFER_LENGTH: + del _LOG_BUFFER[0] + try: + if "socketio" in globals(): + socketio.emit("log", payload) + except Exception: + pass + + +class SocketIOLogHandler(logging.Handler): + def emit(self, record: logging.LogRecord) -> None: + try: + message = self.format(record) + except Exception: + message = record.getMessage() + _push_log(message, record.levelname.lower()) + + +def flush_log_buffer() -> None: + global _LOG_BUFFER + if not _LOG_BUFFER: + return + try: + for payload in _LOG_BUFFER: + socketio.emit("log", payload) + except Exception: + return + finally: + _LOG_BUFFER = [] def websocket_print(*args: Any, **kwargs: Any) -> None: - # Call original print - original_print(*args, **kwargs) - # Also emit via WebSocket in real-time message = " ".join(str(arg) for arg in args) - if message.strip(): - emit_log_realtime(message.strip()) + _push_log(message) + original_print(*args, **kwargs) # Override print globally before importing tiny_scientist modules @@ -53,41 +86,6 @@ def websocket_print(*args: Any, **kwargs: Any) -> None: pass -# Create a function to emit buffered logs when socketio is ready -def emit_buffered_logs() -> None: - global log_buffer - try: - for message in log_buffer: - socketio.emit( - "log", - { - "message": message, - "level": "info", - "timestamp": __import__("time").time(), - }, - ) - log_buffer = [] # Clear buffer after emitting - except Exception: - pass - - -# Create a function to emit logs in real-time -def emit_log_realtime(message: str, level: str = "info") -> None: - try: - # Check if socketio is available - if "socketio" in globals(): - socketio.emit( - "log", - { - "message": message, - "level": level, - "timestamp": __import__("time").time(), - }, - ) - except Exception: - pass - - from tiny_scientist.budget_checker import BudgetChecker # noqa: E402 from tiny_scientist.coder import Coder # noqa: E402 from tiny_scientist.reviewer import Reviewer # noqa: E402 @@ -129,7 +127,15 @@ def patch_module_print() -> None: "http://localhost:3000", ], ) -socketio = SocketIO(app, cors_allowed_origins="*") +socketio = SocketIO(app, cors_allowed_origins="*", async_mode="eventlet") +root_logger = logging.getLogger() +if not any(isinstance(handler, SocketIOLogHandler) for handler in root_logger.handlers): + socketio_handler = SocketIOLogHandler() + socketio_handler.setLevel(logging.INFO) + socketio_handler.setFormatter(logging.Formatter("%(message)s")) + root_logger.addHandler(socketio_handler) +if root_logger.level > logging.INFO: + root_logger.setLevel(logging.INFO) # Print override is now active print("🚀 Backend server starting with WebSocket logging enabled!") @@ -245,7 +251,7 @@ def configure() -> Union[Response, tuple[Response, int]]: @app.route("/api/generate-initial", methods=["POST"]) def generate_initial() -> Union[Response, tuple[Response, int]]: """Generate initial ideas from an intent (handleAnalysisIntentSubmit)""" - emit_buffered_logs() # Emit any buffered logs from module initialization + flush_log_buffer() # Emit any buffered logs from module initialization data = request.json if data is None: return jsonify({"error": "No JSON data provided"}), 400 @@ -523,7 +529,7 @@ def format_idea_content(idea: Union[Dict[str, Any], str]) -> str: @app.route("/api/code", methods=["POST"]) def generate_code() -> Union[Response, tuple[Response, int]]: """Generate code synchronously and return when complete""" - emit_buffered_logs() # Emit any buffered logs + flush_log_buffer() # Emit any buffered logs global coder if coder is None: @@ -624,9 +630,8 @@ def generate_paper() -> Union[Response, tuple[Response, int]]: experiment_dir = data.get("experiment_dir", None) s2_api_key = data.get("s2_api_key", None) - - if not s2_api_key: - return jsonify({"error": "Semantic Scholar API key is required"}), 400 + if isinstance(s2_api_key, str): + s2_api_key = s2_api_key.strip() or None if not idea_data: print("ERROR: No idea provided in request") @@ -653,6 +658,10 @@ def generate_paper() -> Union[Response, tuple[Response, int]]: ), ) print(f"Writer initialized for this request with model: {writer.model}") + if not s2_api_key: + print( + "Proceeding without Semantic Scholar API key; using fallback sources." + ) # Extract the original idea data if isinstance(idea_data, dict) and "originalData" in idea_data: diff --git a/backend/tests/test_coder_demo.py b/backend/tests/test_coder_demo.py new file mode 100644 index 0000000..c3d5be5 --- /dev/null +++ b/backend/tests/test_coder_demo.py @@ -0,0 +1,76 @@ +import json +import os +from pathlib import Path + +import pytest + +from backend.app import app + + +@pytest.fixture(scope="module") +def client(): + app.config["TESTING"] = True + with app.test_client() as client: + yield client + + +def _load_demo_idea() -> dict: + idea_path = Path(__file__).resolve().parents[2] / "demo_test" / "idea.json" + if not idea_path.exists(): + raise FileNotFoundError(f"Idea file not found: {idea_path}") + return json.loads(idea_path.read_text()) + + +def _configure_backend(client) -> None: + model = "gpt-4o" + api_key = os.environ.get("OPENAI_API_KEY") + if not api_key: + pytest.skip("OPENAI_API_KEY not set; skipping coder integration test") + + response = client.post( + "/api/configure", + json={ + "model": model, + "api_key": api_key, + "budget": 10.0, + "budget_preference": "balanced", + }, + ) + assert response.status_code == 200, response.get_data(as_text=True) + + +def test_coder_with_demo_idea(client): + """ + Integration test: run backend /api/code with the demo idea. + Ensures coder executes and produces experiment outputs. + """ + _configure_backend(client) + + idea_payload = _load_demo_idea() + response = client.post( + "/api/code", + json={"idea": {"originalData": idea_payload}}, + ) + + assert response.status_code == 200, response.get_data(as_text=True) + + data = response.get_json() + assert data is not None, "No JSON body returned" + assert data.get("success") is True, data + + experiment_dir = data.get("experiment_dir") + assert experiment_dir, data + + generated_base = Path(__file__).resolve().parents[2] / "generated" + abs_experiment_dir = generated_base / experiment_dir + assert abs_experiment_dir.exists(), f"Experiment dir missing: {abs_experiment_dir}" + + expected_files = { + "experiment.py", + "notes.txt", + "experiment_results.txt", + } + missing = [ + name for name in expected_files if not (abs_experiment_dir / name).exists() + ] + assert not missing, f"Missing files in {abs_experiment_dir}: {missing}" diff --git a/frontend/package.json b/frontend/package.json index bd5d8fa..076fb00 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -2,7 +2,6 @@ "name": "hypo-eval", "version": "0.1.0", "private": true, - "proxy": "http://localhost:5000", "dependencies": { "@monaco-editor/react": "^4.7.0", "@testing-library/jest-dom": "^5.17.0", diff --git a/frontend/src/components/LogDisplay.jsx b/frontend/src/components/LogDisplay.jsx index 5d22c2a..1a2f8be 100644 --- a/frontend/src/components/LogDisplay.jsx +++ b/frontend/src/components/LogDisplay.jsx @@ -1,15 +1,45 @@ -import React, { useState, useEffect, useRef } from 'react'; +import React, { useState, useEffect, useMemo, useRef } from 'react'; import { io } from 'socket.io-client'; +const resolveSocketBaseURL = () => { + if (typeof window === 'undefined') { + return 'http://localhost:5000'; + } + + const explicitBase = process.env.REACT_APP_SOCKET_BASE_URL; + if (explicitBase) { + return explicitBase; + } + + const { protocol, hostname, port } = window.location; + + // When running the dev server (port 3000) we need to talk to the backend port. + if (port === '3000') { + const backendPort = process.env.REACT_APP_SOCKET_PORT || '5000'; + return `${protocol}//${hostname}:${backendPort}`; + } + + return `${protocol}//${hostname}${port ? `:${port}` : ''}`; +}; + +const SOCKET_PATH = process.env.REACT_APP_SOCKET_PATH || '/socket.io'; + const LogDisplay = ({ isVisible, onToggle }) => { const [logs, setLogs] = useState([]); const [isConnected, setIsConnected] = useState(false); const socketRef = useRef(null); const logsEndRef = useRef(null); + const socketUrl = useMemo(resolveSocketBaseURL, []); useEffect(() => { - // Always maintain socket connection when component mounts - socketRef.current = io('http://localhost:5000'); + socketRef.current = io(socketUrl, { + path: SOCKET_PATH, + transports: ['websocket', 'polling'], + reconnection: true, + reconnectionDelay: 2000, + reconnectionDelayMax: 6000, + timeout: 10000, + }); socketRef.current.on('connect', () => { setIsConnected(true); @@ -26,8 +56,9 @@ const LogDisplay = ({ isVisible, onToggle }) => { return () => { socketRef.current?.disconnect(); + socketRef.current = null; }; - }, []); + }, [socketUrl]); useEffect(() => { // Auto-scroll to bottom when new logs arrive diff --git a/frontend/src/components/TreePlotVisualization.jsx b/frontend/src/components/TreePlotVisualization.jsx index dafc82d..4bf56ed 100644 --- a/frontend/src/components/TreePlotVisualization.jsx +++ b/frontend/src/components/TreePlotVisualization.jsx @@ -8,6 +8,110 @@ import IdeaCard from './IdeaCard'; import IdeaFactorsAndScoresCard from './IdeaFactorsAndScoresCard'; import LogDisplay from './LogDisplay'; +const normalizeExperimentDir = (dir) => { + if (!dir) return 'experiments'; + let sanitized = dir.trim(); + if (!sanitized) return 'experiments'; + sanitized = sanitized.replace(/\\/g, '/'); + sanitized = sanitized.replace(/^\.?\/+/, ''); + if (sanitized.startsWith('generated/')) { + sanitized = sanitized.slice('generated/'.length); + } + return sanitized || 'experiments'; +}; + +const buildExperimentFileUrl = (dir, fileName) => { + const safeDir = normalizeExperimentDir(dir); + const encodedDir = safeDir + .split('/') + .filter(Boolean) + .map(encodeURIComponent) + .join('/'); + return `/api/files/${encodedDir}/${encodeURIComponent(fileName)}`; +}; + +const DEFAULT_RUN_DISCOVERY_LIMIT = 10; + +const fetchTextFile = async (dir, filePath) => { + const url = buildExperimentFileUrl(dir, filePath); + try { + console.log(`Attempting to fetch ${filePath} from ${url}`); + const response = await fetch(url); + if (!response.ok) { + console.log(`Fetch skipped for ${filePath}: ${response.status} ${response.statusText}`); + return null; + } + const data = await response.json(); + if (typeof data?.content === 'string') { + return data.content; + } + } catch (err) { + console.error(`Error fetching ${filePath}:`, err); + } + return null; +}; + +const getRunLabel = (runName, index = 0) => { + if (!runName) { + return `Run ${index + 1}`; + } + if (runName.startsWith('run_')) { + const parts = runName.split('_'); + if (parts.length > 1 && parts[1]) { + return `Run ${parts[1]}`; + } + } + return runName; +}; + +const getFileIcon = (fileName = '') => { + if (fileName.endsWith('.py')) return '🐍'; + if (fileName.endsWith('.json')) return '📊'; + if (fileName.endsWith('.txt')) return '📄'; + return '📝'; +}; + +const normalizeCodeResult = (data) => { + if (!data || typeof data !== 'object') { + return data; + } + const safeDir = data.experiment_dir ? normalizeExperimentDir(data.experiment_dir) : null; + return { ...data, experiment_dir: safeDir }; +}; + +const formatMetricValue = (value) => { + if (typeof value === 'number' && Number.isFinite(value)) { + const fixed = value.toFixed(4); + return parseFloat(fixed).toString(); + } + return String(value); +}; + +const sanitizeS2ApiKey = (key) => (typeof key === 'string' ? key.trim() : ''); + +const getStoredS2ApiKey = () => { + if (typeof window === 'undefined') return ''; + try { + return window.localStorage.getItem('s2_api_key') || ''; + } catch (err) { + console.error('Failed to read Semantic Scholar key from storage:', err); + return ''; + } +}; + +const persistS2ApiKey = (key) => { + if (typeof window === 'undefined') return; + try { + if (key) { + window.localStorage.setItem('s2_api_key', key); + } else { + window.localStorage.removeItem('s2_api_key'); + } + } catch (err) { + console.error('Failed to persist Semantic Scholar key:', err); + } +}; + // Helper components defined outside the main component to preserve state const ContextAndGenerateCard = ({ @@ -398,7 +502,7 @@ const TreePlotVisualization = () => { const [isAddingCustom, setIsAddingCustom] = useState(false); const [customIdea, setCustomIdea] = useState({ title: '', content: '' }); // *** 新增：用于主界面模型选择和api-key输入 - const [selectedModel, setSelectedModel] = useState('deepseek-chat'); + const [selectedModel, setSelectedModel] = useState('gpt-4o'); const [apiKey, setApiKey] = useState(''); const [isConfigured, setIsConfigured] = useState(false); const [configError, setConfigError] = useState(''); @@ -438,7 +542,7 @@ const TreePlotVisualization = () => { const [codeFileName, setCodeFileName] = useState('experiment.py'); const [activeCodeTab, setActiveCodeTab] = useState('experiment.py'); const [experimentFiles, setExperimentFiles] = useState({}); - const [consoleOutput, setConsoleOutput] = useState(''); + const [experimentFileList, setExperimentFileList] = useState([]); const [experimentRuns, setExperimentRuns] = useState([]); const [isRunningExperiment, setIsRunningExperiment] = useState(false); const [pdfComments, setPdfComments] = useState([]); @@ -449,6 +553,36 @@ const TreePlotVisualization = () => { const [isReviewing, setIsReviewing] = useState(false); const [rightPanelTab, setRightPanelTab] = useState('comments'); // 'comments' or 'review' + const experimentFileCount = experimentFileList.length; + const hasExperimentFiles = experimentFileCount > 0; + + + const baseFileItems = experimentFileList.filter((item) => item.group === 'base'); + const runFileGroups = (() => { + const map = new Map(); + experimentFileList.forEach((item) => { + if (item.group !== 'run') { + return; + } + const key = item.runName || item.path; + if (!map.has(key)) { + map.set(key, { + runLabel: item.runLabel || getRunLabel(item.runName || '', map.size), + items: [], + }); + } + map.get(key).items.push(item); + }); + return Array.from(map.entries()); + })(); + + useEffect(() => { + const savedKey = sanitizeS2ApiKey(getStoredS2ApiKey()); + if (savedKey) { + setS2ApiKey(savedKey); + } + }, []); + // Track view changes const previousViewRef = useRef(currentView); // Initialize with current view @@ -515,9 +649,9 @@ const TreePlotVisualization = () => { }; // ============== 配置模型和API Key ============== const modelOptions = [ + { value: 'gpt-4o', label: 'GPT-4o' }, { value: 'deepseek-chat', label: 'DeepSeek Chat' }, { value: 'deepseek-reasoner', label: 'DeepSeek Reasoner' }, - { value: 'gpt-4o', label: 'GPT-4o' }, { value: 'o1-2024-12-17', label: 'GPT-o1' }, { value: 'claude-3-5-sonnet-20241022', label: 'Claude 3.5 Sonnet' }, ]; @@ -2223,46 +2357,177 @@ const TreePlotVisualization = () => { // Load generated files from public directory const loadGeneratedFiles = async (experimentDir) => { + const safeDir = normalizeExperimentDir(experimentDir); try { - const fileUrls = { - 'experiment.py': `/api/files/${experimentDir}/experiment.py`, - 'notes.txt': `/api/files/${experimentDir}/notes.txt`, - 'experiment_results.txt': `/api/files/${experimentDir}/experiment_results.txt`, - }; - const loadedFiles = {}; - for (const [fileName, url] of Object.entries(fileUrls)) { + const fileItems = []; + + const baseFiles = ['experiment.py', 'notes.txt', 'experiment_results.txt']; + for (const fileName of baseFiles) { + const content = await fetchTextFile(safeDir, fileName); + if (content !== null) { + loadedFiles[fileName] = content; + fileItems.push({ + path: fileName, + tabLabel: fileName, + sidebarLabel: fileName, + icon: getFileIcon(fileName), + group: 'base', + }); + } + } + + const resultContent = loadedFiles['experiment_results.txt'] || null; + let runNames = []; + if (resultContent) { try { - console.log(`Attempting to fetch: ${fileName} from ${url}`); - const response = await fetch(url); - console.log(`Response for ${fileName}:`, response.status, response.statusText); - - if (response.ok) { - const data = await response.json(); - if (data.content) { - loadedFiles[fileName] = data.content; - console.log(`Loaded ${fileName} successfully.`); + const parsed = JSON.parse(resultContent); + if (parsed && typeof parsed === 'object') { + runNames = Object.keys(parsed); + } + } catch (parseErr) { + console.error('Failed to parse experiment_results.txt:', parseErr); + } + } + if (runNames.length === 0) { + runNames = Array.from({ length: DEFAULT_RUN_DISCOVERY_LIMIT }, (_, i) => `run_${i + 1}`); + } + + const runMeta = new Map(); + await Promise.all( + runNames.map(async (runName, index) => { + if (!runName) return; + + const runLabel = getRunLabel(runName, index); + let meta = runMeta.get(runName); + if (!meta) { + meta = { runName, runLabel }; + runMeta.set(runName, meta); + } + + let fetchedContent = false; + + const finalInfoPath = `${runName}/final_info.json`; + const finalInfo = await fetchTextFile(safeDir, finalInfoPath); + if (finalInfo !== null) { + loadedFiles[finalInfoPath] = finalInfo; + fetchedContent = true; + try { + meta.finalInfo = JSON.parse(finalInfo); + } catch (err) { + console.error(`Failed to parse ${finalInfoPath}:`, err); + meta.finalInfo = null; } + meta.finalInfoPath = finalInfoPath; + fileItems.push({ + path: finalInfoPath, + tabLabel: `${runLabel} • final_info.json`, + sidebarLabel: 'final_info.json', + icon: getFileIcon(finalInfoPath), + group: 'run', + runName, + runLabel, + }); + } + + const runScriptPath = `${runName}.py`; + const runScript = await fetchTextFile(safeDir, runScriptPath); + if (runScript !== null) { + loadedFiles[runScriptPath] = runScript; + fetchedContent = true; + meta.scriptPath = runScriptPath; + const scriptFileName = runScriptPath.split('/').pop() || runScriptPath; + fileItems.push({ + path: runScriptPath, + tabLabel: `${runLabel} • ${scriptFileName}`, + sidebarLabel: scriptFileName, + icon: getFileIcon(runScriptPath), + group: 'run', + runName, + runLabel, + }); + } + + if (!fetchedContent) { + runMeta.delete(runName); + } + }) + ); + + setExperimentFiles(loadedFiles); + setExperimentFileList(fileItems); + + const runMetaMap = Object.fromEntries(runMeta.entries()); + + if (resultContent) { + try { + const parsed = JSON.parse(resultContent); + if (parsed && typeof parsed === 'object') { + const runs = Object.entries(parsed).map(([name, metrics], index) => { + const safeMetrics = typeof metrics === 'object' && metrics !== null ? metrics : {}; + const meta = runMetaMap[name] || { runName: name, runLabel: getRunLabel(name, index) }; + return { + name, + runLabel: meta.runLabel || getRunLabel(name, index), + metrics: safeMetrics, + success: Object.keys(safeMetrics).length > 0, + }; + }); + setExperimentRuns(runs); } else { - console.log(`Could not load ${fileName}, but this might be expected (e.g., no notes).`); + setExperimentRuns([]); } - } catch (err) { - console.error(`Error fetching ${fileName}:`, err); + } catch (parseErr) { + console.error('Failed to parse experiment_results.txt:', parseErr); + setExperimentRuns([]); } + } else if (Object.keys(runMetaMap).length > 0) { + const runs = Object.entries(runMetaMap).map(([name, meta], index) => { + return { + name, + runLabel: meta.runLabel || getRunLabel(name, index), + metrics: meta.finalInfo && typeof meta.finalInfo === 'object' ? meta.finalInfo : {}, + success: true, + }; + }); + setExperimentRuns(runs); + } else { + setExperimentRuns([]); } - // Update state all at once - setExperimentFiles(prev => ({ ...prev, ...loadedFiles })); - - // Set the code content for the editor if experiment.py was loaded if (loadedFiles['experiment.py']) { - setCodeContent(loadedFiles['experiment.py']); setActiveCodeTab('experiment.py'); + setCodeFileName('experiment.py'); + setCodeContent(loadedFiles['experiment.py']); + } else if (fileItems.length > 0) { + const firstItem = fileItems[0]; + setActiveCodeTab(firstItem.path); + setCodeFileName(firstItem.tabLabel); + setCodeContent(loadedFiles[firstItem.path] || ''); + } else { + setActiveCodeTab(''); + setCodeFileName(''); + setCodeContent( + `# Generated experiment code +# Files are being generated in: ${safeDir} + +# Please check the directory for the actual code files.` + ); } + return safeDir; } catch (err) { - console.log("Error loading generated files:", err); - setCodeContent(`# Generated experiment code\n# Files are being generated in: ${experimentDir}\n\n# Please check the directory for the actual code files.`); + console.log(`Error loading generated files from ${safeDir}:`, err); + setExperimentFiles({}); + setExperimentFileList([]); + setCodeContent( + `# Generated experiment code +# Files are being generated in: ${safeDir} + +# Please check the directory for the actual code files.` + ); + setExperimentRuns([]); + return safeDir; } }; @@ -2281,11 +2546,11 @@ const TreePlotVisualization = () => { console.log("Manual file loading completed"); // Set a fake successful result to satisfy the UI - setCodeResult({ + setCodeResult(normalizeCodeResult({ status: true, success: true, experiment_dir: "experiments" - }); + })); } catch (err) { console.log("Manual file loading failed:", err); @@ -2304,6 +2569,7 @@ const TreePlotVisualization = () => { setOperationStatus('Retrying code generation...'); setCodeResult(null); setExperimentFiles({}); + setExperimentFileList([]); setShowLogs(true); // Show logs when starting code generation try { @@ -2325,25 +2591,28 @@ const TreePlotVisualization = () => { const codeData = await codeResponse.json(); console.log("Retry code generation completed:", codeData); - setCodeResult(codeData); - if (!codeData.success) { throw new Error(codeData.error || 'Code generation failed'); } + const normalizedResult = normalizeCodeResult(codeData); + setCodeResult(normalizedResult); + // Load generated files setOperationStatus('Loading generated files...'); - await loadGeneratedFiles(codeData.experiment_dir); + if (normalizedResult.experiment_dir) { + await loadGeneratedFiles(normalizedResult.experiment_dir); + } setOperationStatus('Code generation retry completed successfully!'); } catch (error) { console.error("Retry code generation failed:", error); setOperationStatus('Retry code generation failed: ' + error.message); - setCodeResult({ + setCodeResult(normalizeCodeResult({ success: false, error: error.message, error_details: error.message - }); + })); } finally { setIsGeneratingCode(false); } @@ -2369,15 +2638,13 @@ const TreePlotVisualization = () => { setShowLogs(true); // Show logs when starting paper generation try { - // Get S2 API key from localStorage or prompt user - let s2ApiKey = localStorage.getItem('s2_api_key'); - if (!s2ApiKey) { - s2ApiKey = prompt('Please enter your Semantic Scholar API Key:'); - if (!s2ApiKey) { - throw new Error('Semantic Scholar API key is required for paper generation'); - } - localStorage.setItem('s2_api_key', s2ApiKey); - } + const effectiveS2Key = + sanitizeS2ApiKey(s2ApiKey) || sanitizeS2ApiKey(getStoredS2ApiKey()); + persistS2ApiKey(effectiveS2Key); + setS2ApiKey(effectiveS2Key); + console.log( + `Semantic Scholar API key ${effectiveS2Key ? 'detected' : 'not provided'} for paper generation` + ); const paperResponse = await fetch('/api/write', { method: 'POST', @@ -2388,7 +2655,7 @@ const TreePlotVisualization = () => { body: JSON.stringify({ idea: node.originalData, experiment_dir: experimentDir, - s2_api_key: s2ApiKey + s2_api_key: effectiveS2Key || null }) }); @@ -2448,11 +2715,9 @@ const TreePlotVisualization = () => { // Check if the idea is experimental to determine S2 API key requirement const isExperimental = selectedNode.originalData.is_experimental === true; - // Validate Semantic Scholar API key only for non-experimental ideas - if (!isExperimental && !s2ApiKey.trim()) { - setProceedError('Semantic Scholar API key is required for non-experimental ideas'); - return; - } + const sanitizedS2Key = sanitizeS2ApiKey(s2ApiKey); + persistS2ApiKey(sanitizedS2Key); + setS2ApiKey(sanitizedS2Key); setShowProceedConfirm(false); setProceedError(null); @@ -2566,7 +2831,6 @@ const TreePlotVisualization = () => { codeData = await codeResponse.json(); console.log("Code generation completed:", codeData); - setCodeResult(codeData); if (!codeData.success) { throw new Error(codeData.error || 'Code generation failed'); @@ -2608,12 +2872,17 @@ const TreePlotVisualization = () => { } // Store results - setCodeResult(codeData); - const finalExperimentDir = codeData.experiment_dir; + const normalizedResult = normalizeCodeResult(codeData); + setCodeResult(normalizedResult); + const finalExperimentDir = normalizedResult?.experiment_dir; // Load generated files setOperationStatus('Loading generated files...'); - await loadGeneratedFiles(finalExperimentDir); + if (finalExperimentDir) { + await loadGeneratedFiles(finalExperimentDir); + } else { + console.warn("Code generation succeeded but experiment directory is missing."); + } // Mark that code has been generated (this will show Code View tab) setHasGeneratedCode(true); @@ -2643,7 +2912,7 @@ const TreePlotVisualization = () => { body: JSON.stringify({ idea: selectedNode.originalData, experiment_dir: null, // No experiment directory for non-experimental papers - s2_api_key: s2ApiKey.trim(), + s2_api_key: sanitizedS2Key || null, }), }); @@ -2776,7 +3045,14 @@ const TreePlotVisualization = () => { // Enhanced code editing functions const switchCodeTab = (tabName) => { - // Save current content before switching + if (!tabName) { + return; + } + + if (experimentFiles[tabName] === undefined) { + return; + } + if (activeCodeTab && experimentFiles[activeCodeTab] !== undefined) { setExperimentFiles(prev => ({ ...prev, @@ -2784,9 +3060,9 @@ const TreePlotVisualization = () => { })); } - // Switch to new tab + const fileItem = experimentFileList.find((item) => item.path === tabName); setActiveCodeTab(tabName); - setCodeFileName(tabName); + setCodeFileName(fileItem?.tabLabel || tabName); setCodeContent(experimentFiles[tabName] || ''); }; @@ -2894,14 +3170,14 @@ const TreePlotVisualization = () => { {/* Download All Button */} - - -
- {consoleOutput || 'No output yet...'} -
- {proceedError && ( @@ -3198,7 +3502,7 @@ const TreePlotVisualization = () => { )} - {!codeResult && !isGeneratingCode && !proceedError && Object.keys(experimentFiles).length === 0 && ( + {!codeResult && !isGeneratingCode && !proceedError && !hasExperimentFiles && (
{ color: '#374151', fontWeight: 500 }}> - Semantic Scholar API Key {!selectedNode?.originalData?.is_experimental ? '*' : '(Optional for now)'} + Semantic Scholar API Key (Optional) setS2ApiKey(e.target.value)} - placeholder={selectedNode?.originalData?.is_experimental ? - "Enter your Semantic Scholar API key (can be provided later for paper generation)" : - "Enter your Semantic Scholar API key"} + placeholder="Optional: improves citation quality" style={{ width: '100%', padding: '8px 12px', @@ -4128,10 +4430,7 @@ const TreePlotVisualization = () => { }} />
- {selectedNode?.originalData?.is_experimental ? - 'For experimental ideas: Required only when generating paper. You can provide it later.' : - 'Required for paper generation.' - } Get your API key from{' '} + Providing a key lets us fetch references from Semantic Scholar. Leave blank to skip for now. Get your API key from{' '} { diff --git a/frontend/src/setupProxy.js b/frontend/src/setupProxy.js new file mode 100644 index 0000000..082265b --- /dev/null +++ b/frontend/src/setupProxy.js @@ -0,0 +1,12 @@ +const { createProxyMiddleware } = require('http-proxy-middleware'); + +module.exports = function setupProxy(app) { + app.use( + ['/api', '/socket.io'], + createProxyMiddleware({ + target: 'http://localhost:5000', + changeOrigin: true, + ws: true, + }) + ); +}; diff --git a/poetry.lock b/poetry.lock index faf3eb5..84ef393 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.2.0 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. 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[tool.poetry.group.dev.dependencies] pre-commit = "*" diff --git a/tiny_scientist/mcp/docker_runner_server.py b/tiny_scientist/mcp/docker_runner_server.py index 8bd7664..e9ccc26 100644 --- a/tiny_scientist/mcp/docker_runner_server.py +++ b/tiny_scientist/mcp/docker_runner_server.py @@ -21,6 +21,27 @@ CONFIG_PATH = PACKAGE_ROOT / "config.toml" config = toml.load(CONFIG_PATH) if CONFIG_PATH.exists() else {"core": {}} +DEFAULT_BASE_PACKAGES = [ + "numpy", + "pandas", + "scikit-learn", + "matplotlib", + "seaborn", + "tqdm", + "requests", + "pillow", +] + + +def _resolve_configured_packages() -> List[str]: + docker_cfg = config.get("docker") if isinstance(config, dict) else None + configured = None + if isinstance(docker_cfg, dict): + configured = docker_cfg.get("base_packages") + if isinstance(configured, list) and all(isinstance(pkg, str) for pkg in configured): + return configured + return DEFAULT_BASE_PACKAGES.copy() + class DockerExperimentRunner: def __init__( @@ -32,6 +53,8 @@ def __init__( self.docker_base = docker_base self.docker_client = None self.use_docker = False + self.base_packages = _resolve_configured_packages() + self._base_package_set: Set[str] = set(self.base_packages) # Initialize Docker client try: @@ -49,24 +72,7 @@ def detect_required_packages( ) -> List[str]: """Detect required packages from import statements in a Python file.""" if base_packages is None: - base_packages = set( - [ - "numpy", - "pandas", - "scikit-learn", - "matplotlib", - "seaborn", - "torch", - "tensorflow", - "transformers", - "datasets", - "evaluate", - "wandb", - "tqdm", - "requests", - "pillow", - ] - ) + base_packages = set(DEFAULT_BASE_PACKAGES) # Common package name mappings (import_name -> pip_package_name) package_mapping = { @@ -298,10 +304,11 @@ def get_or_build_base_image(self) -> Optional[str]: print(f"[Docker] Using existing image: {self.docker_image}") except ImageNotFound: print(f"[Docker] Building image: {self.docker_image}") - dockerfile = f""" -FROM {self.docker_base} -RUN pip install --no-cache-dir numpy pandas scikit-learn matplotlib seaborn torch tensorflow transformers datasets evaluate wandb tqdm requests pillow -""" + dockerfile_lines = [f"FROM {self.docker_base}"] + if self.base_packages: + joined = " ".join(sorted(self.base_packages)) + dockerfile_lines.append(f"RUN pip install --no-cache-dir {joined}") + dockerfile = "\n".join(dockerfile_lines) + "\n" with tempfile.TemporaryDirectory() as tmpdir: with open(os.path.join(tmpdir, "Dockerfile"), "w") as f: f.write(dockerfile) @@ -316,7 +323,9 @@ def get_or_build_experiment_image(self, experiment_py_path: str) -> Optional[str if not self.use_docker: return None base_image = self.get_or_build_base_image() - extra_pkgs = self.detect_required_packages(experiment_py_path) + extra_pkgs = self.detect_required_packages( + experiment_py_path, base_packages=self._base_package_set + ) if extra_pkgs: image_name = f"tiny-scientist-exp-{hash(tuple(extra_pkgs))}" if self.docker_client is not None: From 6ecf7e22b428355cc984a70a301cadf8749bf727 Mon Sep 17 00:00:00 2001 From: 4R5T Date: Sat, 1 Nov 2025 03:21:13 -0500 Subject: [PATCH 04/14] feat(ui): add cache demo on UI --- .gitignore | 7 + backend/app.py | 174 ++++- backend/demo_cache.py | 270 ++++++++ backend/run_app.sh | 16 + backend/run_demo.sh | 20 + .../experiments/idea-1/experiment.py | 132 ++++ .../experiments/idea-1/experiment_results.txt | 20 + .../generated/experiments/idea-1/notes.txt | 17 + .../generated/experiments/idea-1/run_1.py | 135 ++++ .../experiments/idea-1/run_1/final_info.json | 1 + .../generated/experiments/idea-1/run_2.py | 138 ++++ .../experiments/idea-1/run_2/final_info.json | 1 + .../experiments/idea-1/run_2/notes.txt | 4 + .../generated/experiments/idea-1/run_3.py | 132 ++++ .../experiments/idea-1/run_3/final_info.json | 1 + .../experiments/idea-1/run_3/notes.txt | 4 + .../experiments/idea-2/experiment.py | 126 ++++ .../experiments/idea-2/experiment_results.txt | 20 + .../generated/experiments/idea-2/notes.txt | 17 + .../generated/experiments/idea-2/run_1.py | 130 ++++ .../experiments/idea-2/run_1/final_info.json | 1 + .../generated/experiments/idea-2/run_2.py | 122 ++++ .../experiments/idea-2/run_2/final_info.json | 1 + .../experiments/idea-2/run_2/notes.txt | 4 + .../generated/experiments/idea-2/run_3.py | 126 ++++ .../experiments/idea-2/run_3/final_info.json | 1 + .../experiments/idea-2/run_3/notes.txt | 4 + .../experiments/idea-3/experiment.py | 143 ++++ .../experiments/idea-3/experiment_results.txt | 17 + .../generated/experiments/idea-3/notes.txt | 17 + .../generated/experiments/idea-3/run_1.py | 139 ++++ .../experiments/idea-3/run_1/final_info.json | 1 + .../generated/experiments/idea-3/run_2.py | 143 ++++ .../experiments/idea-3/run_2/final_info.json | 1 + .../experiments/idea-3/run_2/notes.txt | 4 + .../generated/experiments/idea-3/run_3.py | 143 ++++ .../experiments/idea-3/run_3/final_info.json | 1 + .../experiments/idea-3/run_3/notes.txt | 4 + ...ong-range_coherence_in_code_generation.pdf | Bin 0 -> 550657 bytes ...ce_coherent_long-range_code_generation.pdf | Bin 0 -> 479486 bytes ...ed_coherent_long-range_code_generation.pdf | Bin 0 -> 571638 bytes frontend/demo_cache/ideas/idea-1-1/idea.json | 47 ++ frontend/demo_cache/ideas/idea-1-2/idea.json | 47 ++ frontend/demo_cache/ideas/idea-1-3/idea.json | 68 ++ frontend/demo_cache/ideas/idea-1/idea.json | 68 ++ frontend/demo_cache/ideas/idea-2/idea.json | 48 ++ frontend/demo_cache/ideas/idea-3/idea.json | 47 ++ .../demo_cache/reviews/idea-1/review.json | 38 ++ .../demo_cache/reviews/idea-2/review.json | 39 ++ .../demo_cache/reviews/idea-3/review.json | 39 ++ frontend/demo_cache/session.json | 623 ++++++++++++++++++ frontend/src/components/TopNav.jsx | 86 ++- .../src/components/TreePlotVisualization.jsx | 61 +- frontend/src/setupProxy.js | 28 +- tiny_scientist/mcp/docker_runner_server.py | 33 +- 55 files changed, 3454 insertions(+), 55 deletions(-) create mode 100644 backend/demo_cache.py create mode 100755 backend/run_app.sh create mode 100755 backend/run_demo.sh create mode 100644 frontend/demo_cache/generated/experiments/idea-1/experiment.py create mode 100644 frontend/demo_cache/generated/experiments/idea-1/experiment_results.txt create mode 100644 frontend/demo_cache/generated/experiments/idea-1/notes.txt create mode 100644 frontend/demo_cache/generated/experiments/idea-1/run_1.py create mode 100644 frontend/demo_cache/generated/experiments/idea-1/run_1/final_info.json create mode 100644 frontend/demo_cache/generated/experiments/idea-1/run_2.py create mode 100644 frontend/demo_cache/generated/experiments/idea-1/run_2/final_info.json create mode 100644 frontend/demo_cache/generated/experiments/idea-1/run_2/notes.txt create mode 100644 frontend/demo_cache/generated/experiments/idea-1/run_3.py create mode 100644 frontend/demo_cache/generated/experiments/idea-1/run_3/final_info.json create mode 100644 frontend/demo_cache/generated/experiments/idea-1/run_3/notes.txt create mode 100644 frontend/demo_cache/generated/experiments/idea-2/experiment.py create mode 100644 frontend/demo_cache/generated/experiments/idea-2/experiment_results.txt create mode 100644 frontend/demo_cache/generated/experiments/idea-2/notes.txt create mode 100644 frontend/demo_cache/generated/experiments/idea-2/run_1.py create mode 100644 frontend/demo_cache/generated/experiments/idea-2/run_1/final_info.json create mode 100644 frontend/demo_cache/generated/experiments/idea-2/run_2.py create mode 100644 frontend/demo_cache/generated/experiments/idea-2/run_2/final_info.json create mode 100644 frontend/demo_cache/generated/experiments/idea-2/run_2/notes.txt create mode 100644 frontend/demo_cache/generated/experiments/idea-2/run_3.py create mode 100644 frontend/demo_cache/generated/experiments/idea-2/run_3/final_info.json create mode 100644 frontend/demo_cache/generated/experiments/idea-2/run_3/notes.txt create mode 100644 frontend/demo_cache/generated/experiments/idea-3/experiment.py create mode 100644 frontend/demo_cache/generated/experiments/idea-3/experiment_results.txt create mode 100644 frontend/demo_cache/generated/experiments/idea-3/notes.txt create mode 100644 frontend/demo_cache/generated/experiments/idea-3/run_1.py create mode 100644 frontend/demo_cache/generated/experiments/idea-3/run_1/final_info.json create mode 100644 frontend/demo_cache/generated/experiments/idea-3/run_2.py create mode 100644 frontend/demo_cache/generated/experiments/idea-3/run_2/final_info.json create mode 100644 frontend/demo_cache/generated/experiments/idea-3/run_2/notes.txt create mode 100644 frontend/demo_cache/generated/experiments/idea-3/run_3.py create mode 100644 frontend/demo_cache/generated/experiments/idea-3/run_3/final_info.json create mode 100644 frontend/demo_cache/generated/experiments/idea-3/run_3/notes.txt create mode 100644 frontend/demo_cache/generated/papers/idea-1/investigating_adaptive_prompt_decomposition_for_improved_long-range_coherence_in_code_generation.pdf create mode 100644 frontend/demo_cache/generated/papers/idea-2/investigating_adaptive_prompt_decomposition_to_enhance_coherent_long-range_code_generation.pdf create mode 100644 frontend/demo_cache/generated/papers/idea-3/exploring_adaptive_prompt_decomposition_for_enhanced_coherent_long-range_code_generation.pdf create mode 100644 frontend/demo_cache/ideas/idea-1-1/idea.json create mode 100644 frontend/demo_cache/ideas/idea-1-2/idea.json create mode 100644 frontend/demo_cache/ideas/idea-1-3/idea.json create mode 100644 frontend/demo_cache/ideas/idea-1/idea.json create mode 100644 frontend/demo_cache/ideas/idea-2/idea.json create mode 100644 frontend/demo_cache/ideas/idea-3/idea.json create mode 100644 frontend/demo_cache/reviews/idea-1/review.json create mode 100644 frontend/demo_cache/reviews/idea-2/review.json create mode 100644 frontend/demo_cache/reviews/idea-3/review.json create mode 100644 frontend/demo_cache/session.json diff --git a/.gitignore b/.gitignore index 8e3515c..8e011e2 100644 --- a/.gitignore +++ b/.gitignore @@ -160,6 +160,13 @@ demo/ frontend/node_modules/ run_experiments/ generated/ +!frontend/demo_cache/generated/experiments/** +!frontend/demo_cache/generated/ +!frontend/demo_cache/generated/** +!frontend/demo_cache/ideas/ +!frontend/demo_cache/ideas/** +!frontend/demo_cache/reviews/ +!frontend/demo_cache/reviews/** runs/ testst/ diff --git a/backend/app.py b/backend/app.py index 45fc392..6bdcd7a 100644 --- a/backend/app.py +++ b/backend/app.py @@ -86,6 +86,7 @@ def websocket_print(*args: Any, **kwargs: Any) -> None: pass +from backend.demo_cache import DemoCacheError, DemoCacheService # noqa: E402 from tiny_scientist.budget_checker import BudgetChecker # noqa: E402 from tiny_scientist.coder import Coder # noqa: E402 from tiny_scientist.reviewer import Reviewer # noqa: E402 @@ -116,6 +117,38 @@ def patch_module_print() -> None: # Call the patching function patch_module_print() + +def _demo_log(message: str, level: str = "info") -> None: + _push_log(message, level) + + +DEMO_CACHE_MODE = os.environ.get("DEMO_CACHE_MODE", "").strip().lower() in { + "1", + "true", + "yes", + "on", +} +DEMO_CACHE_DIR = os.path.abspath( + os.environ.get("DEMO_CACHE_DIR") + or os.path.join(project_root, "frontend", "demo_cache") +) + +try: + demo_cache = DemoCacheService( + DEMO_CACHE_DIR, + enabled=DEMO_CACHE_MODE, + log_fn=_demo_log, + ) + if demo_cache.enabled: + print(f"🗄️ Demo cache enabled using data at {DEMO_CACHE_DIR}") +except DemoCacheError as exc: + print(f"⚠️ Demo cache disabled: {exc}") + demo_cache = DemoCacheService( + DEMO_CACHE_DIR, + enabled=False, + log_fn=_demo_log, + ) + app = Flask(__name__) app.secret_key = "your-secret-key-here" CORS( @@ -162,6 +195,12 @@ def configure() -> Union[Response, tuple[Response, int]]: data = request.json if data is None: return jsonify({"error": "No JSON data provided"}), 400 + if demo_cache.enabled: + try: + response_payload = demo_cache.apply_config(session) + except DemoCacheError as exc: + return jsonify({"error": str(exc)}), 409 + return jsonify(response_payload) model = data.get("model") api_key = data.get("api_key") budget = data.get("budget") @@ -255,11 +294,19 @@ def generate_initial() -> Union[Response, tuple[Response, int]]: data = request.json if data is None: return jsonify({"error": "No JSON data provided"}), 400 - if thinker is None: - return jsonify({"error": "Thinker not configured"}), 400 intent = data.get("intent") num_ideas = data.get("num_ideas", 3) + if demo_cache.enabled: + try: + response_payload = demo_cache.get_initial_ideas(intent) + except DemoCacheError as exc: + return jsonify({"error": str(exc)}), 409 + return jsonify(response_payload) + + if thinker is None: + return jsonify({"error": "Thinker not configured"}), 400 + # Generate ideas ideas = thinker.run(intent=intent, num_ideas=num_ideas) @@ -285,16 +332,23 @@ def generate_initial() -> Union[Response, tuple[Response, int]]: @app.route("/api/set-system-prompt", methods=["POST"]) def set_system_prompt() -> Union[Response, tuple[Response, int]]: """Set the system prompt for the Thinker""" - global thinker - - if not thinker: - return jsonify({"error": "Thinker not configured"}), 400 - data = request.json if data is None: return jsonify({"error": "No JSON data provided"}), 400 system_prompt = data.get("system_prompt") + if demo_cache.enabled: + try: + demo_cache.update_system_prompt(system_prompt) + except DemoCacheError as exc: + return jsonify({"error": str(exc)}), 409 + return jsonify({"status": "success", "message": "System prompt updated"}) + + global thinker + + if not thinker: + return jsonify({"error": "Thinker not configured"}), 400 + # If empty string or None, reset to default if not system_prompt: thinker.set_system_prompt(None) # This will reset to default @@ -307,11 +361,6 @@ def set_system_prompt() -> Union[Response, tuple[Response, int]]: @app.route("/api/set-criteria", methods=["POST"]) def set_criteria() -> Union[Response, tuple[Response, int]]: """Set evaluation criteria for a specific dimension""" - global thinker - - if not thinker: - return jsonify({"error": "Thinker not configured"}), 400 - data = request.json if data is None: return jsonify({"error": "No JSON data provided"}), 400 @@ -321,6 +370,23 @@ def set_criteria() -> Union[Response, tuple[Response, int]]: if dimension not in ["novelty", "feasibility", "impact"]: return jsonify({"error": "Invalid dimension"}), 400 + if demo_cache.enabled: + try: + demo_cache.update_criteria(dimension, criteria) + except DemoCacheError as exc: + return jsonify({"error": str(exc)}), 409 + return jsonify( + { + "status": "success", + "message": f"{dimension.capitalize()} criteria updated", + } + ) + + global thinker + + if not thinker: + return jsonify({"error": "Thinker not configured"}), 400 + # If empty string or None, reset to default if not criteria: thinker.set_criteria(dimension, None) # This will reset to default @@ -335,6 +401,12 @@ def set_criteria() -> Union[Response, tuple[Response, int]]: @app.route("/api/get-prompts", methods=["GET"]) def get_prompts() -> Union[Response, tuple[Response, int]]: """Get current prompts and criteria""" + if demo_cache.enabled: + try: + return jsonify(demo_cache.get_prompts()) + except DemoCacheError as exc: + return jsonify({"error": str(exc)}), 409 + global thinker if thinker is None: @@ -364,6 +436,12 @@ def generate_children() -> Union[Response, tuple[Response, int]]: data = request.json if data is None: return jsonify({"error": "No JSON data provided"}), 400 + if demo_cache.enabled: + try: + response_payload = demo_cache.get_child_ideas() + except DemoCacheError as exc: + return jsonify({"error": str(exc)}), 409 + return jsonify(response_payload) if thinker is None: return jsonify({"error": "Thinker not configured"}), 400 parent_content = data.get("parent_content") @@ -398,6 +476,12 @@ def modify_idea() -> Union[Response, tuple[Response, int]]: data = request.json if data is None: return jsonify({"error": "No JSON data provided"}), 400 + if demo_cache.enabled: + try: + response_payload = demo_cache.get_modified_idea() + except DemoCacheError as exc: + return jsonify({"error": str(exc)}), 409 + return jsonify(response_payload) if thinker is None: return jsonify({"error": "Thinker not configured"}), 400 original_idea = data.get("original_idea") @@ -435,6 +519,12 @@ def merge_ideas() -> Union[Response, tuple[Response, int]]: data = request.json if data is None: return jsonify({"error": "No JSON data provided"}), 400 + if demo_cache.enabled: + try: + response_payload = demo_cache.get_merged_idea() + except DemoCacheError as exc: + return jsonify({"error": str(exc)}), 409 + return jsonify(response_payload) if thinker is None: return jsonify({"error": "Thinker not configured"}), 400 idea_a = data.get("idea_a") @@ -467,10 +557,17 @@ def evaluate_ideas() -> Union[Response, tuple[Response, int]]: data = request.json if data is None: return jsonify({"error": "No JSON data provided"}), 400 + ideas = data.get("ideas") or [] + intent = data.get("intent") + + if demo_cache.enabled: + try: + response_payload = demo_cache.evaluate(ideas) + except DemoCacheError as exc: + return jsonify({"error": str(exc)}), 409 + return jsonify(response_payload) if thinker is None: return jsonify({"error": "Thinker not configured"}), 400 - ideas = data.get("ideas") - intent = data.get("intent") # Use original data directly (no conversion needed) thinker_ideas = ideas @@ -532,15 +629,22 @@ def generate_code() -> Union[Response, tuple[Response, int]]: flush_log_buffer() # Emit any buffered logs global coder - if coder is None: - return jsonify({"error": "Coder not configured"}), 400 - data = request.json if data is None: return jsonify({"error": "No JSON data provided"}), 400 idea_data = data.get("idea") baseline_results = data.get("baseline_results", {}) + if demo_cache.enabled: + try: + response_payload = demo_cache.get_code_result() + except DemoCacheError as exc: + return jsonify({"error": str(exc)}), 409 + return jsonify(response_payload) + + if coder is None: + return jsonify({"error": "Coder not configured"}), 400 + print("💻 Starting synchronous code generation...") if not idea_data: @@ -629,6 +733,13 @@ def generate_paper() -> Union[Response, tuple[Response, int]]: idea_data = data.get("idea") experiment_dir = data.get("experiment_dir", None) + if demo_cache.enabled: + try: + response_payload = demo_cache.get_paper_result() + except DemoCacheError as exc: + return jsonify({"error": str(exc)}), 409 + return jsonify(response_payload) + s2_api_key = data.get("s2_api_key", None) if isinstance(s2_api_key, str): s2_api_key = s2_api_key.strip() or None @@ -740,15 +851,14 @@ def generate_paper() -> Union[Response, tuple[Response, int]]: def serve_experiment_file(file_path: str) -> Union[Response, tuple[Response, int]]: """Serve generated experiment files""" try: - # The base directory for all generated content - generated_base = os.path.join(project_root, "generated") - - # Construct the full path securely - full_path = os.path.abspath(os.path.join(generated_base, file_path)) - - # Security check: ensure the file is within the allowed directory - if not full_path.startswith(os.path.abspath(generated_base)): - return jsonify({"error": "Access denied"}), 403 + if demo_cache.enabled: + generated_base = demo_cache.generated_base + full_path = demo_cache.resolve_generated_path(file_path) + else: + generated_base = os.path.join(project_root, "generated") + full_path = os.path.abspath(os.path.join(generated_base, file_path)) + if not full_path.startswith(os.path.abspath(generated_base)): + return jsonify({"error": "Access denied"}), 403 if not os.path.exists(full_path): return jsonify({"error": "File not found"}), 404 @@ -762,6 +872,8 @@ def serve_experiment_file(file_path: str) -> Union[Response, tuple[Response, int # For other files, serve directly return send_file(full_path) + except DemoCacheError as exc: + return jsonify({"error": str(exc)}), 404 except Exception as e: print(f"Error serving file {file_path}: {e}") return jsonify({"error": str(e)}), 500 @@ -780,6 +892,13 @@ def review_paper() -> Union[Response, tuple[Response, int]]: pdf_path = data.get("pdf_path") s2_api_key = data.get("s2_api_key") + if demo_cache.enabled: + try: + response_payload = demo_cache.get_review_result() + except DemoCacheError as exc: + return jsonify({"error": str(exc)}), 409 + return jsonify(response_payload) + if not pdf_path: return jsonify({"error": "No PDF path provided"}), 400 @@ -862,11 +981,12 @@ def review_paper() -> Union[Response, tuple[Response, int]]: if __name__ == "__main__": # Configure Flask for long-running requests app.config["SEND_FILE_MAX_AGE_DEFAULT"] = 0 + port = int(os.environ.get("PORT", "5000")) socketio.run( app, debug=True, use_reloader=False, - port=5000, + port=port, host="0.0.0.0", allow_unsafe_werkzeug=True, ) diff --git a/backend/demo_cache.py b/backend/demo_cache.py new file mode 100644 index 0000000..859bec9 --- /dev/null +++ b/backend/demo_cache.py @@ -0,0 +1,270 @@ +from __future__ import annotations + +import copy +import json +from collections import defaultdict +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional + + +class DemoCacheError(RuntimeError): + """Raised when demo cache data is missing or inconsistent.""" + + +class DemoCacheService: + """Utility to replay a pre-recorded Tiny Scientist session for demos.""" + + def __init__( + self, + base_dir: str | Path, + *, + enabled: bool, + log_fn: Optional[Callable[[str, str], None]] = None, + ) -> None: + self.base_dir = Path(base_dir) + self.enabled = bool(enabled) + self._log_fn = log_fn or (lambda message, level="info": None) + + self.generated_root = "generated" + self.generated_base = self.base_dir / self.generated_root + + self._queues: Dict[str, List[Any]] = {} + self._counters: Dict[str, int] = defaultdict(int) + self._evaluation_by_name: Dict[str, Dict[str, Any]] = {} + self._evaluation_default: Optional[Dict[str, Any]] = None + self._prompts_state: Dict[str, Any] = { + "system_prompt": "", + "criteria": {}, + "defaults": {}, + } + self._configure_state: Dict[str, Any] = {} + self._logs: Dict[str, List[Any]] = {} + self.intent: Optional[str] = None + + if self.enabled: + self._load() + + def _load(self) -> None: + session_path = self.base_dir / "session.json" + if not session_path.exists(): + raise DemoCacheError( + f"Demo cache session file not found at {session_path!s}" + ) + + with session_path.open("r", encoding="utf-8") as fh: + payload = json.load(fh) + + self.intent = payload.get("intent") + self.generated_root = payload.get("generated_root") or "generated" + self.generated_base = self.base_dir / self.generated_root + + self._configure_state = payload.get("configure") or {} + + prompts = payload.get("prompts") or {} + defaults = prompts.get("defaults") or {} + if not defaults and prompts.get("criteria"): + defaults = copy.deepcopy(prompts["criteria"]) + + self._prompts_state = { + "system_prompt": prompts.get("system_prompt", ""), + "criteria": copy.deepcopy(prompts.get("criteria") or {}), + "defaults": copy.deepcopy(defaults), + } + + evaluation_payload = payload.get("evaluation") or {} + self._evaluation_by_name = copy.deepcopy( + evaluation_payload.get("by_name") or {} + ) + default_entry = evaluation_payload.get("default") + self._evaluation_default = ( + copy.deepcopy(default_entry) if default_entry else None + ) + + self._logs = {} + raw_logs = payload.get("logs") or {} + for key, messages in raw_logs.items(): + if isinstance(messages, list): + self._logs[key] = list(messages) + elif isinstance(messages, dict): + self._logs[key] = [messages] + else: + self._logs[key] = [messages] + + queue_keys = [ + "generate_initial", + "generate_children", + "modify", + "merge", + "code", + "write", + "review", + ] + for key in queue_keys: + raw_queue = payload.get(key) + if raw_queue is None: + self._queues[key] = [] + elif isinstance(raw_queue, dict): + self._queues[key] = [copy.deepcopy(raw_queue)] + else: + self._queues[key] = [copy.deepcopy(item) for item in raw_queue] + + def _emit_logs(self, channel: str) -> None: + if not self.enabled: + return + + messages = self._logs.get(channel) or [] + for entry in messages: + if isinstance(entry, dict): + message = entry.get("message") + level = entry.get("level", "info") + else: + message = str(entry) + level = "info" + + if message: + self._log_fn(message, level) + + def _next(self, key: str) -> Any: + queue = self._queues.get(key) or [] + if not queue: + raise DemoCacheError(f"No cached payloads configured for '{key}'") + + index = self._counters[key] + if index < len(queue): + payload = queue[index] + self._counters[key] = index + 1 + else: + payload = queue[-1] + + self._emit_logs(key) + return copy.deepcopy(payload) + + def apply_config(self, flask_session: Any) -> Dict[str, Any]: + if not self.enabled: + raise DemoCacheError("Demo cache mode is disabled") + + session_values = copy.deepcopy(self._configure_state.get("session") or {}) + if session_values: + for key, value in session_values.items(): + flask_session[key] = value + else: + flask_session["configured"] = True + + response_payload = copy.deepcopy( + self._configure_state.get("response") + or { + "status": "configured", + "model": session_values.get("model", "demo-model"), + "budget": session_values.get("budget"), + "budget_preference": session_values.get("budget_preference"), + } + ) + + self._emit_logs("configure") + return response_payload + + def get_prompts(self) -> Dict[str, Any]: + if not self.enabled: + raise DemoCacheError("Demo cache mode is disabled") + + return { + "system_prompt": self._prompts_state.get("system_prompt", ""), + "criteria": copy.deepcopy(self._prompts_state.get("criteria") or {}), + "defaults": copy.deepcopy(self._prompts_state.get("defaults") or {}), + } + + def update_system_prompt(self, system_prompt: Optional[str]) -> None: + if not self.enabled: + raise DemoCacheError("Demo cache mode is disabled") + + default_prompt = self._prompts_state.get("defaults", {}).get( + "system_prompt", "" + ) + self._prompts_state["system_prompt"] = system_prompt or default_prompt + self._emit_logs("set_system_prompt") + + def update_criteria(self, dimension: str, criteria: Optional[str]) -> None: + if not self.enabled: + raise DemoCacheError("Demo cache mode is disabled") + + defaults = self._prompts_state.get("defaults") or {} + self._prompts_state.setdefault("criteria", {}) + if criteria: + self._prompts_state["criteria"][dimension] = criteria + else: + fallback = defaults.get(dimension, "") + if fallback: + self._prompts_state["criteria"][dimension] = fallback + else: + self._prompts_state["criteria"].pop(dimension, None) + self._emit_logs("set_criteria") + + def get_initial_ideas(self, intent: Optional[str] = None) -> Dict[str, Any]: + _ = intent # Intent is kept for potential validation/debugging. + return self._next("generate_initial") + + def get_child_ideas(self) -> Dict[str, Any]: + return self._next("generate_children") + + def get_modified_idea(self) -> Dict[str, Any]: + return self._next("modify") + + def get_merged_idea(self) -> Dict[str, Any]: + return self._next("merge") + + def get_code_result(self) -> Dict[str, Any]: + return self._next("code") + + def get_paper_result(self) -> Dict[str, Any]: + return self._next("write") + + def get_review_result(self) -> Dict[str, Any]: + return self._next("review") + + def evaluate(self, ideas: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + if not self.enabled: + raise DemoCacheError("Demo cache mode is disabled") + + results: List[Dict[str, Any]] = [] + for idx, idea in enumerate(ideas): + idea_id = idea.get("id") or f"idea_{idx}" + name_candidates = [ + idea.get("Name"), + idea.get("Title"), + idea.get("title"), + idea.get("name"), + ] + + score_entry: Optional[Dict[str, Any]] = None + for name in name_candidates: + if isinstance(name, str) and name in self._evaluation_by_name: + score_entry = self._evaluation_by_name[name] + break + + if score_entry is None: + score_entry = self._evaluation_default + + if score_entry is None: + raise DemoCacheError( + f"No cached evaluation scores for idea '{name_candidates[0] or idea_id}'" + ) + + payload = copy.deepcopy(score_entry) + payload["id"] = idea_id + results.append(payload) + + self._emit_logs("evaluate") + return results + + def resolve_generated_path(self, relative_path: str) -> Path: + if not self.enabled: + raise DemoCacheError("Demo cache mode is disabled") + + sanitized = relative_path.lstrip("/\\") + candidate = (self.generated_base / sanitized).resolve() + base = self.generated_base.resolve() + if not str(candidate).startswith(str(base)): + raise DemoCacheError( + f"Attempt to access file outside demo cache: {relative_path}" + ) + return candidate diff --git a/backend/run_app.sh b/backend/run_app.sh new file mode 100755 index 0000000..08b2e91 --- /dev/null +++ b/backend/run_app.sh @@ -0,0 +1,16 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Resolve repository root relative to this script. +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" + +cd "$ROOT_DIR" + +# Ensure demo flags are not leaking into the normal instance. +unset DEMO_CACHE_MODE || true +unset DEMO_CACHE_DIR || true + +# Default port for live instance +export PORT="${PORT:-5000}" + +exec poetry run python backend/app.py "$@" diff --git a/backend/run_demo.sh b/backend/run_demo.sh new file mode 100755 index 0000000..1e99306 --- /dev/null +++ b/backend/run_demo.sh @@ -0,0 +1,20 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Resolve repository root relative to this script. +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +CACHE_DIR="${DEMO_CACHE_DIR:-$ROOT_DIR/frontend/demo_cache}" + +if [[ ! -f "$CACHE_DIR/session.json" ]]; then + echo "[demo] Missing cache snapshot at $CACHE_DIR/session.json" >&2 + echo "[demo] Run: poetry run python scripts/generate_demo_cache.py ..." >&2 + exit 1 +fi + +cd "$ROOT_DIR" + +export DEMO_CACHE_MODE=1 +export DEMO_CACHE_DIR="$CACHE_DIR" +export PORT="${PORT:-5001}" + +exec poetry run python backend/app.py "$@" diff --git a/frontend/demo_cache/generated/experiments/idea-1/experiment.py b/frontend/demo_cache/generated/experiments/idea-1/experiment.py new file mode 100644 index 0000000..1949a01 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-1/experiment.py @@ -0,0 +1,132 @@ +import argparse +import json +import os + +import torch +from datasets import load_dataset +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics import accuracy_score, precision_recall_fscore_support +from torch import nn, optim +from torch.utils.data import DataLoader, TensorDataset + + +# Load and preprocess the data +def load_data(): + # Load the dataset + dataset = load_dataset("ag_news") + + # Subsample the dataset + train_data = dataset["train"].select(range(5000)) + val_data = dataset["train"].select(range(5000, 7000)) + test_data = dataset["test"].select(range(2000)) + + # Vectorize the text data + vectorizer = TfidfVectorizer(max_features=768, stop_words="english") + + # Fit on training data and transform + X_train = vectorizer.fit_transform(train_data["text"]).toarray() + X_val = vectorizer.transform(val_data["text"]).toarray() + X_test = vectorizer.transform(test_data["text"]).toarray() + + # Convert labels to tensors + y_train = torch.tensor(train_data["label"], dtype=torch.long) + y_val = torch.tensor(val_data["label"], dtype=torch.long) + y_test = torch.tensor(test_data["label"], dtype=torch.long) + + # Create DataLoader for each set + train_loader = DataLoader( + TensorDataset(torch.tensor(X_train, dtype=torch.float32), y_train), + batch_size=64, + shuffle=True, + ) + val_loader = DataLoader( + TensorDataset(torch.tensor(X_val, dtype=torch.float32), y_val), + batch_size=64, + shuffle=False, + ) + test_loader = DataLoader( + TensorDataset(torch.tensor(X_test, dtype=torch.float32), y_test), + batch_size=64, + shuffle=False, + ) + + return train_loader, val_loader, test_loader + + +class ShallowMLP(nn.Module): + def __init__(self, input_dim=768, output_dim=4): + super(ShallowMLP, self).__init__() + self.fc1 = nn.Linear(input_dim, 128) + self.fc2 = nn.Linear(128, 64) + self.fc3 = nn.Linear(64, output_dim) + self.relu = nn.ReLU() + self.softmax = nn.Softmax(dim=1) + + def forward(self, x): + x = self.relu(self.fc1(x)) + x = self.relu(self.fc2(x)) + x = self.softmax(self.fc3(x)) + return x + + +def train(model, train_loader, optimizer, criterion, device): + model.train() + for epoch in range(5): + for inputs, labels in train_loader: + inputs, labels = inputs.to(device), labels.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + print(f"Training completed over {epoch+1} epochs.") + + +def evaluate(model, data_loader, device): + model.eval() + all_preds = [] + all_labels = [] + with torch.no_grad(): + for inputs, labels in data_loader: + inputs, labels = inputs.to(device), labels.to(device) + outputs = model(inputs) + preds = torch.argmax(outputs, dim=1).cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.cpu().numpy()) + + accuracy = accuracy_score(all_labels, all_preds) + precision, recall, f1, _ = precision_recall_fscore_support( + all_labels, all_preds, average="weighted" + ) + return {"Accuracy": accuracy, "Precision": precision, "Recall": recall, "F1": f1} + + +def main(out_dir): + os.makedirs(out_dir, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + train_loader, val_loader, test_loader = load_data() + + model = ShallowMLP(input_dim=768, output_dim=4) + model = model.to(device) + + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=0.0005) + + train(model, train_loader, optimizer, criterion, device) + metrics = evaluate(model, test_loader, device) + + with open(os.path.join(out_dir, "final_info.json"), "w") as f: + json.dump(metrics, f) + + print(f"Train dataset size: {len(train_loader.dataset)}") + print(f"Validation dataset size: {len(val_loader.dataset)}") + print(f"Test dataset size: {len(test_loader.dataset)}") + print("Run 2 - Lowered learning rate to 0.0005") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--out_dir", type=str, required=True) + args = parser.parse_args() + main(args.out_dir) diff --git a/frontend/demo_cache/generated/experiments/idea-1/experiment_results.txt b/frontend/demo_cache/generated/experiments/idea-1/experiment_results.txt new file mode 100644 index 0000000..a0826db --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-1/experiment_results.txt @@ -0,0 +1,20 @@ +{ + "run_1": { + "Accuracy": 0.811, + "Precision": 0.8145642555829715, + "Recall": 0.811, + "F1": 0.8108722139466467 + }, + "run_2": { + "Accuracy": 0.827, + "Precision": 0.8276568041561658, + "Recall": 0.827, + "F1": 0.826017973910428 + }, + "run_3": { + "Accuracy": 0.821, + "Precision": 0.8251032168130884, + "Recall": 0.821, + "F1": 0.8208549347485607 + } +} diff --git a/frontend/demo_cache/generated/experiments/idea-1/notes.txt b/frontend/demo_cache/generated/experiments/idea-1/notes.txt new file mode 100644 index 0000000..47c6d3b --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-1/notes.txt @@ -0,0 +1,17 @@ +Figure 1: filename_figure1.png +This plot illustrates the relationship between variable X and variable Y, showcasing a positive linear correlation. As variable X increases, variable Y also tends to increase, indicating a direct relationship. The data points are scattered closely along the line of best fit, suggesting a strong correlation. This plot is useful for understanding how changes in X may predict changes in Y. + +Figure 2: filename_figure2.png +The bar chart depicted in this figure represents the distribution of category Z across different groups. Each bar corresponds to a different group, and the height of the bar indicates the frequency or count of category Z within that group. This visualization helps in comparing the prevalence of category Z among the various groups, highlighting any significant differences or trends. + +Figure 3: filename_figure3.png +This figure presents a histogram of variable A, providing insights into its distribution. The x-axis represents the range of variable A, while the y-axis indicates the frequency of occurrences within each bin. The shape of the histogram reveals whether the data is normally distributed, skewed, or has multiple modes. This information is crucial for understanding the overall pattern and any potential outliers in the dataset. + +Figure 4: filename_figure4.png +The scatter plot shown in this figure examines the interaction between variables B and C. Each point on the plot represents an observation, with its position determined by the values of B and C. A trend line is included to highlight any potential correlation between the two variables. This plot aids in identifying patterns, clusters, or anomalies and is essential for exploratory data analysis. + +Figure 5: filename_figure5.png +This line graph illustrates the time series data of variable D over a defined period. The x-axis represents time intervals, while the y-axis displays the values of variable D. The line connecting the data points provides a visual representation of trends, cycles, or seasonality present in the data. Observing this plot can help predict future values and understand past behavior of variable D. + +Figure 6: filename_figure6.png +The pie chart in this figure represents the composition of variable E, divided into different segments that illustrate the proportional contribution of each component to the whole. Each segment is labeled with either the component name or percentage it represents. This visualization is effective in conveying the relative sizes of parts within a dataset, offering a clear view of the distribution and significance of each component. diff --git a/frontend/demo_cache/generated/experiments/idea-1/run_1.py b/frontend/demo_cache/generated/experiments/idea-1/run_1.py new file mode 100644 index 0000000..6f42485 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-1/run_1.py @@ -0,0 +1,135 @@ +import argparse +import json +import os + +import torch +from datasets import load_dataset +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics import accuracy_score, precision_recall_fscore_support +from torch import nn, optim +from torch.utils.data import DataLoader, TensorDataset + + +# Load and preprocess the data +def load_data(): + # Load the dataset + dataset = load_dataset("ag_news") + + # Subsample the dataset + train_data = dataset["train"].select(range(5000)) + val_data = dataset["train"].select(range(5000, 7000)) + test_data = dataset["test"].select(range(2000)) + + # Vectorize the text data + vectorizer = TfidfVectorizer(max_features=768, stop_words="english") + + # Fit on training data and transform + X_train = vectorizer.fit_transform(train_data["text"]).toarray() + X_val = vectorizer.transform(val_data["text"]).toarray() + X_test = vectorizer.transform(test_data["text"]).toarray() + + # Convert labels to tensors + y_train = torch.tensor(train_data["label"], dtype=torch.long) + y_val = torch.tensor(val_data["label"], dtype=torch.long) + y_test = torch.tensor(test_data["label"], dtype=torch.long) + + # Create DataLoader for each set + train_loader = DataLoader( + TensorDataset(torch.tensor(X_train, dtype=torch.float32), y_train), + batch_size=64, + shuffle=True, + ) + val_loader = DataLoader( + TensorDataset(torch.tensor(X_val, dtype=torch.float32), y_val), + batch_size=64, + shuffle=False, + ) + test_loader = DataLoader( + TensorDataset(torch.tensor(X_test, dtype=torch.float32), y_test), + batch_size=64, + shuffle=False, + ) + + return train_loader, val_loader, test_loader + + +class ShallowMLP(nn.Module): + def __init__( + self, input_dim=768, output_dim=4 + ): # Correct output_dim to 4 for ag_news + super(ShallowMLP, self).__init__() + self.fc1 = nn.Linear(input_dim, 128) + self.fc2 = nn.Linear(128, 64) + self.fc3 = nn.Linear(64, output_dim) + self.relu = nn.ReLU() + self.softmax = nn.Softmax(dim=1) + + def forward(self, x): + x = self.relu(self.fc1(x)) + x = self.relu(self.fc2(x)) + x = self.softmax(self.fc3(x)) + return x + + +def train(model, train_loader, optimizer, criterion, device): + model.train() + for epoch in range(5): + for inputs, labels in train_loader: + inputs, labels = inputs.to(device), labels.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + print(f"Training completed over {epoch+1} epochs.") + + +def evaluate(model, data_loader, device): + model.eval() + all_preds = [] + all_labels = [] + with torch.no_grad(): + for inputs, labels in data_loader: + inputs, labels = inputs.to(device), labels.to(device) + outputs = model(inputs) + preds = torch.argmax(outputs, dim=1).cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.cpu().numpy()) + + accuracy = accuracy_score(all_labels, all_preds) + precision, recall, f1, _ = precision_recall_fscore_support( + all_labels, all_preds, average="weighted" + ) + return {"Accuracy": accuracy, "Precision": precision, "Recall": recall, "F1": f1} + + +def main(out_dir): + os.makedirs(out_dir, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + train_loader, val_loader, test_loader = load_data() + + model = ShallowMLP( + input_dim=768, output_dim=4 + ) # Correct output_dim to 4 for ag_news + model = model.to(device) + + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters()) + + train(model, train_loader, optimizer, criterion, device) + metrics = evaluate(model, test_loader, device) + + with open(os.path.join(out_dir, "final_info.json"), "w") as f: + json.dump(metrics, f) + + print(f"Train dataset size: {len(train_loader.dataset)}") + print(f"Validation dataset size: {len(val_loader.dataset)}") + print(f"Test dataset size: {len(test_loader.dataset)}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--out_dir", type=str, required=True) + args = parser.parse_args() + main(args.out_dir) diff --git a/frontend/demo_cache/generated/experiments/idea-1/run_1/final_info.json b/frontend/demo_cache/generated/experiments/idea-1/run_1/final_info.json new file mode 100644 index 0000000..d628bbb --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-1/run_1/final_info.json @@ -0,0 +1 @@ +{"Accuracy": 0.811, "Precision": 0.8145642555829715, "Recall": 0.811, "F1": 0.8108722139466467} diff --git a/frontend/demo_cache/generated/experiments/idea-1/run_2.py b/frontend/demo_cache/generated/experiments/idea-1/run_2.py new file mode 100644 index 0000000..4646d3f --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-1/run_2.py @@ -0,0 +1,138 @@ +import argparse +import json +import os + +import torch +from datasets import load_dataset +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics import accuracy_score, precision_recall_fscore_support +from torch import nn, optim +from torch.utils.data import DataLoader, TensorDataset + + +# Load and preprocess the data +def load_data(): + # Load the dataset + dataset = load_dataset("ag_news") + + # Subsample the dataset + train_data = dataset["train"].select(range(5000)) + val_data = dataset["train"].select(range(5000, 7000)) + test_data = dataset["test"].select(range(2000)) + + # Vectorize the text data + vectorizer = TfidfVectorizer(max_features=768, stop_words="english") + + # Fit on training data and transform + X_train = vectorizer.fit_transform(train_data["text"]).toarray() + X_val = vectorizer.transform(val_data["text"]).toarray() + X_test = vectorizer.transform(test_data["text"]).toarray() + + # Convert labels to tensors + y_train = torch.tensor(train_data["label"], dtype=torch.long) + y_val = torch.tensor(val_data["label"], dtype=torch.long) + y_test = torch.tensor(test_data["label"], dtype=torch.long) + + # Create DataLoader for each set + train_loader = DataLoader( + TensorDataset(torch.tensor(X_train, dtype=torch.float32), y_train), + batch_size=64, + shuffle=True, + ) + val_loader = DataLoader( + TensorDataset(torch.tensor(X_val, dtype=torch.float32), y_val), + batch_size=64, + shuffle=False, + ) + test_loader = DataLoader( + TensorDataset(torch.tensor(X_test, dtype=torch.float32), y_test), + batch_size=64, + shuffle=False, + ) + + return train_loader, val_loader, test_loader + + +class ShallowMLP(nn.Module): + def __init__( + self, input_dim=768, output_dim=4 + ): # Correct output_dim to 4 for ag_news + super(ShallowMLP, self).__init__() + self.fc1 = nn.Linear(input_dim, 128) + self.fc2 = nn.Linear(128, 64) + self.fc3 = nn.Linear(64, output_dim) + self.relu = nn.ReLU() + self.softmax = nn.Softmax(dim=1) + + def forward(self, x): + x = self.relu(self.fc1(x)) + x = self.relu(self.fc2(x)) + x = self.softmax(self.fc3(x)) + return x + + +def train(model, train_loader, optimizer, criterion, device): + model.train() + for epoch in range(5): + for inputs, labels in train_loader: + inputs, labels = inputs.to(device), labels.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + print(f"Training completed over {epoch+1} epochs.") + + +def evaluate(model, data_loader, device): + model.eval() + all_preds = [] + all_labels = [] + with torch.no_grad(): + for inputs, labels in data_loader: + inputs, labels = inputs.to(device), labels.to(device) + outputs = model(inputs) + preds = torch.argmax(outputs, dim=1).cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.cpu().numpy()) + + accuracy = accuracy_score(all_labels, all_preds) + precision, recall, f1, _ = precision_recall_fscore_support( + all_labels, all_preds, average="weighted" + ) + return {"Accuracy": accuracy, "Precision": precision, "Recall": recall, "F1": f1} + + +def main(out_dir): + os.makedirs(out_dir, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + train_loader, val_loader, test_loader = load_data() + + model = ShallowMLP( + input_dim=768, output_dim=4 + ) # Correct output_dim to 4 for ag_news + model = model.to(device) + + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam( + model.parameters(), lr=0.0005 + ) # Lower learning rate for next experiment + + train(model, train_loader, optimizer, criterion, device) + metrics = evaluate(model, test_loader, device) + + with open(os.path.join(out_dir, "final_info.json"), "w") as f: + json.dump(metrics, f) + + print(f"Train dataset size: {len(train_loader.dataset)}") + print(f"Validation dataset size: {len(val_loader.dataset)}") + print(f"Test dataset size: {len(test_loader.dataset)}") + print("Run 2 - Lowered learning rate to 0.0005") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--out_dir", type=str, required=True) + args = parser.parse_args() + main(args.out_dir) diff --git a/frontend/demo_cache/generated/experiments/idea-1/run_2/final_info.json b/frontend/demo_cache/generated/experiments/idea-1/run_2/final_info.json new file mode 100644 index 0000000..31d47a5 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-1/run_2/final_info.json @@ -0,0 +1 @@ +{"Accuracy": 0.827, "Precision": 0.8276568041561658, "Recall": 0.827, "F1": 0.826017973910428} diff --git a/frontend/demo_cache/generated/experiments/idea-1/run_2/notes.txt b/frontend/demo_cache/generated/experiments/idea-1/run_2/notes.txt new file mode 100644 index 0000000..90f3881 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-1/run_2/notes.txt @@ -0,0 +1,4 @@ +Experiment Description: +In this experiment, we used a SimpleGRUClassifier with a GRU layer and a linear layer to classify IMDB reviews. The model was trained using subsampled training data from the IMDB dataset, with a validation set for hyperparameter tuning and a separate test set for evaluating the model's generalization. +Run Number: 1 +Results: {'Accuracy': 1.0} diff --git a/frontend/demo_cache/generated/experiments/idea-1/run_3.py b/frontend/demo_cache/generated/experiments/idea-1/run_3.py new file mode 100644 index 0000000..1949a01 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-1/run_3.py @@ -0,0 +1,132 @@ +import argparse +import json +import os + +import torch +from datasets import load_dataset +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics import accuracy_score, precision_recall_fscore_support +from torch import nn, optim +from torch.utils.data import DataLoader, TensorDataset + + +# Load and preprocess the data +def load_data(): + # Load the dataset + dataset = load_dataset("ag_news") + + # Subsample the dataset + train_data = dataset["train"].select(range(5000)) + val_data = dataset["train"].select(range(5000, 7000)) + test_data = dataset["test"].select(range(2000)) + + # Vectorize the text data + vectorizer = TfidfVectorizer(max_features=768, stop_words="english") + + # Fit on training data and transform + X_train = vectorizer.fit_transform(train_data["text"]).toarray() + X_val = vectorizer.transform(val_data["text"]).toarray() + X_test = vectorizer.transform(test_data["text"]).toarray() + + # Convert labels to tensors + y_train = torch.tensor(train_data["label"], dtype=torch.long) + y_val = torch.tensor(val_data["label"], dtype=torch.long) + y_test = torch.tensor(test_data["label"], dtype=torch.long) + + # Create DataLoader for each set + train_loader = DataLoader( + TensorDataset(torch.tensor(X_train, dtype=torch.float32), y_train), + batch_size=64, + shuffle=True, + ) + val_loader = DataLoader( + TensorDataset(torch.tensor(X_val, dtype=torch.float32), y_val), + batch_size=64, + shuffle=False, + ) + test_loader = DataLoader( + TensorDataset(torch.tensor(X_test, dtype=torch.float32), y_test), + batch_size=64, + shuffle=False, + ) + + return train_loader, val_loader, test_loader + + +class ShallowMLP(nn.Module): + def __init__(self, input_dim=768, output_dim=4): + super(ShallowMLP, self).__init__() + self.fc1 = nn.Linear(input_dim, 128) + self.fc2 = nn.Linear(128, 64) + self.fc3 = nn.Linear(64, output_dim) + self.relu = nn.ReLU() + self.softmax = nn.Softmax(dim=1) + + def forward(self, x): + x = self.relu(self.fc1(x)) + x = self.relu(self.fc2(x)) + x = self.softmax(self.fc3(x)) + return x + + +def train(model, train_loader, optimizer, criterion, device): + model.train() + for epoch in range(5): + for inputs, labels in train_loader: + inputs, labels = inputs.to(device), labels.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + print(f"Training completed over {epoch+1} epochs.") + + +def evaluate(model, data_loader, device): + model.eval() + all_preds = [] + all_labels = [] + with torch.no_grad(): + for inputs, labels in data_loader: + inputs, labels = inputs.to(device), labels.to(device) + outputs = model(inputs) + preds = torch.argmax(outputs, dim=1).cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.cpu().numpy()) + + accuracy = accuracy_score(all_labels, all_preds) + precision, recall, f1, _ = precision_recall_fscore_support( + all_labels, all_preds, average="weighted" + ) + return {"Accuracy": accuracy, "Precision": precision, "Recall": recall, "F1": f1} + + +def main(out_dir): + os.makedirs(out_dir, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + train_loader, val_loader, test_loader = load_data() + + model = ShallowMLP(input_dim=768, output_dim=4) + model = model.to(device) + + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=0.0005) + + train(model, train_loader, optimizer, criterion, device) + metrics = evaluate(model, test_loader, device) + + with open(os.path.join(out_dir, "final_info.json"), "w") as f: + json.dump(metrics, f) + + print(f"Train dataset size: {len(train_loader.dataset)}") + print(f"Validation dataset size: {len(val_loader.dataset)}") + print(f"Test dataset size: {len(test_loader.dataset)}") + print("Run 2 - Lowered learning rate to 0.0005") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--out_dir", type=str, required=True) + args = parser.parse_args() + main(args.out_dir) diff --git a/frontend/demo_cache/generated/experiments/idea-1/run_3/final_info.json b/frontend/demo_cache/generated/experiments/idea-1/run_3/final_info.json new file mode 100644 index 0000000..4a004d7 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-1/run_3/final_info.json @@ -0,0 +1 @@ +{"Accuracy": 0.821, "Precision": 0.8251032168130884, "Recall": 0.821, "F1": 0.8208549347485607} diff --git a/frontend/demo_cache/generated/experiments/idea-1/run_3/notes.txt b/frontend/demo_cache/generated/experiments/idea-1/run_3/notes.txt new file mode 100644 index 0000000..ba51aef --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-1/run_3/notes.txt @@ -0,0 +1,4 @@ +Experiment Description: +In this experiment, we used a SimpleGRUClassifier with a GRU layer and a linear layer to classify IMDB reviews. The model was trained using subsampled training data from the IMDB dataset, with a validation set for hyperparameter tuning and a separate test set for evaluating the model's generalization. +Run Number: 2 +Results: {'Accuracy': 1.0} diff --git a/frontend/demo_cache/generated/experiments/idea-2/experiment.py b/frontend/demo_cache/generated/experiments/idea-2/experiment.py new file mode 100644 index 0000000..b280f1b --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-2/experiment.py @@ -0,0 +1,126 @@ +import argparse +import json +import os + +import torch +from datasets import load_dataset +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics import accuracy_score, precision_recall_fscore_support +from torch import nn, optim +from torch.utils.data import DataLoader, TensorDataset + + +def load_data(): + dataset = load_dataset("ag_news") + train_data = dataset["train"].select(range(5000)) + val_data = dataset["train"].select(range(5000, 7000)) + test_data = dataset["test"].select(range(2000)) + + vectorizer = TfidfVectorizer(max_features=512, stop_words="english") + + X_train = vectorizer.fit_transform(train_data["text"]).toarray() + X_val = vectorizer.transform(val_data["text"]).toarray() + X_test = vectorizer.transform(test_data["text"]).toarray() + + y_train = torch.tensor(train_data["label"], dtype=torch.long) + y_val = torch.tensor(val_data["label"], dtype=torch.long) + y_test = torch.tensor(test_data["label"], dtype=torch.long) + + train_loader = DataLoader( + TensorDataset(torch.tensor(X_train, dtype=torch.float32), y_train), + batch_size=64, + shuffle=True, + ) + val_loader = DataLoader( + TensorDataset(torch.tensor(X_val, dtype=torch.float32), y_val), + batch_size=64, + shuffle=False, + ) + test_loader = DataLoader( + TensorDataset(torch.tensor(X_test, dtype=torch.float32), y_test), + batch_size=64, + shuffle=False, + ) + + return train_loader, val_loader, test_loader + + +class SingleLayerGRU(nn.Module): + def __init__( + self, input_dim=512, hidden_units=256, output_dim=4 + ): # Changed hidden_units from 128 to 256 + super(SingleLayerGRU, self).__init__() + self.gru = nn.GRU(input_dim, hidden_units, batch_first=True) + self.fc = nn.Linear(hidden_units, output_dim) + self.softmax = nn.Softmax(dim=1) + + def forward(self, x): + x, _ = self.gru(x.view(x.size(0), 1, -1)) + x = self.fc(x[:, -1, :]) + x = self.softmax(x) + return x + + +def train(model, train_loader, optimizer, criterion, device): + model.train() + for epoch in range(5): + for inputs, labels in train_loader: + inputs, labels = inputs.to(device), labels.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + print(f"Training completed over {epoch+1} epochs.") + + +def evaluate(model, data_loader, device): + model.eval() + all_preds = [] + all_labels = [] + with torch.no_grad(): + for inputs, labels in data_loader: + inputs, labels = inputs.to(device), labels.to(device) + outputs = model(inputs) + preds = torch.argmax(outputs, dim=1).cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.cpu().numpy()) + + accuracy = accuracy_score(all_labels, all_preds) + precision, recall, f1, _ = precision_recall_fscore_support( + all_labels, all_preds, average="weighted" + ) + return {"Accuracy": accuracy, "Precision": precision, "Recall": recall, "F1": f1} + + +def main(out_dir): + os.makedirs(out_dir, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + train_loader, val_loader, test_loader = load_data() + + model = SingleLayerGRU( + input_dim=512, hidden_units=256, output_dim=4 + ) # Changed hidden_units from 128 to 256 + model = model.to(device) + + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=0.001) + + train(model, train_loader, optimizer, criterion, device) + metrics = evaluate(model, test_loader, device) + + with open(os.path.join(out_dir, "final_info.json"), "w") as f: + json.dump(metrics, f) + + print(f"Train dataset size: {len(train_loader.dataset)}") + print(f"Validation dataset size: {len(val_loader.dataset)}") + print(f"Test dataset size: {len(test_loader.dataset)}") + print("Experiment completed successfully.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--out_dir", type=str, required=True) + args = parser.parse_args() + main(args.out_dir) diff --git a/frontend/demo_cache/generated/experiments/idea-2/experiment_results.txt b/frontend/demo_cache/generated/experiments/idea-2/experiment_results.txt new file mode 100644 index 0000000..1053b3e --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-2/experiment_results.txt @@ -0,0 +1,20 @@ +{ + "run_1": { + "Accuracy": 0.793, + "Precision": 0.795239024398564, + "Recall": 0.793, + "F1": 0.7925706551495995 + }, + "run_2": { + "Accuracy": 0.805, + "Precision": 0.8056109579615937, + "Recall": 0.805, + "F1": 0.8047095700204514 + }, + "run_3": { + "Accuracy": 0.7985, + "Precision": 0.8008647556477098, + "Recall": 0.7985, + "F1": 0.7981580518342577 + } +} diff --git a/frontend/demo_cache/generated/experiments/idea-2/notes.txt b/frontend/demo_cache/generated/experiments/idea-2/notes.txt new file mode 100644 index 0000000..47c6d3b --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-2/notes.txt @@ -0,0 +1,17 @@ +Figure 1: filename_figure1.png +This plot illustrates the relationship between variable X and variable Y, showcasing a positive linear correlation. As variable X increases, variable Y also tends to increase, indicating a direct relationship. The data points are scattered closely along the line of best fit, suggesting a strong correlation. This plot is useful for understanding how changes in X may predict changes in Y. + +Figure 2: filename_figure2.png +The bar chart depicted in this figure represents the distribution of category Z across different groups. Each bar corresponds to a different group, and the height of the bar indicates the frequency or count of category Z within that group. This visualization helps in comparing the prevalence of category Z among the various groups, highlighting any significant differences or trends. + +Figure 3: filename_figure3.png +This figure presents a histogram of variable A, providing insights into its distribution. The x-axis represents the range of variable A, while the y-axis indicates the frequency of occurrences within each bin. The shape of the histogram reveals whether the data is normally distributed, skewed, or has multiple modes. This information is crucial for understanding the overall pattern and any potential outliers in the dataset. + +Figure 4: filename_figure4.png +The scatter plot shown in this figure examines the interaction between variables B and C. Each point on the plot represents an observation, with its position determined by the values of B and C. A trend line is included to highlight any potential correlation between the two variables. This plot aids in identifying patterns, clusters, or anomalies and is essential for exploratory data analysis. + +Figure 5: filename_figure5.png +This line graph illustrates the time series data of variable D over a defined period. The x-axis represents time intervals, while the y-axis displays the values of variable D. The line connecting the data points provides a visual representation of trends, cycles, or seasonality present in the data. Observing this plot can help predict future values and understand past behavior of variable D. + +Figure 6: filename_figure6.png +The pie chart in this figure represents the composition of variable E, divided into different segments that illustrate the proportional contribution of each component to the whole. Each segment is labeled with either the component name or percentage it represents. This visualization is effective in conveying the relative sizes of parts within a dataset, offering a clear view of the distribution and significance of each component. diff --git a/frontend/demo_cache/generated/experiments/idea-2/run_1.py b/frontend/demo_cache/generated/experiments/idea-2/run_1.py new file mode 100644 index 0000000..670be65 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-2/run_1.py @@ -0,0 +1,130 @@ +import argparse +import json +import os + +import torch +from datasets import load_dataset +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics import accuracy_score, precision_recall_fscore_support +from torch import nn, optim +from torch.utils.data import DataLoader, TensorDataset + + +# Load and preprocess the data +def load_data(): + # Load the dataset + dataset = load_dataset("ag_news") + + # Subsample the dataset + train_data = dataset["train"].select(range(5000)) + val_data = dataset["train"].select(range(5000, 7000)) + test_data = dataset["test"].select(range(2000)) + + # Vectorize the text data using TF-IDF + vectorizer = TfidfVectorizer(max_features=512, stop_words="english") + + # Fit on training data and transform + X_train = vectorizer.fit_transform(train_data["text"]).toarray() + X_val = vectorizer.transform(val_data["text"]).toarray() + X_test = vectorizer.transform(test_data["text"]).toarray() + + # Convert labels to tensors + y_train = torch.tensor(train_data["label"], dtype=torch.long) + y_val = torch.tensor(val_data["label"], dtype=torch.long) + y_test = torch.tensor(test_data["label"], dtype=torch.long) + + # Create DataLoader for each set + train_loader = DataLoader( + TensorDataset(torch.tensor(X_train, dtype=torch.float32), y_train), + batch_size=64, + shuffle=True, + ) + val_loader = DataLoader( + TensorDataset(torch.tensor(X_val, dtype=torch.float32), y_val), + batch_size=64, + shuffle=False, + ) + test_loader = DataLoader( + TensorDataset(torch.tensor(X_test, dtype=torch.float32), y_test), + batch_size=64, + shuffle=False, + ) + + return train_loader, val_loader, test_loader + + +class SingleLayerGRU(nn.Module): + def __init__(self, input_dim=512, hidden_units=64, output_dim=4): + super(SingleLayerGRU, self).__init__() + self.gru = nn.GRU(input_dim, hidden_units, batch_first=True) + self.fc = nn.Linear(hidden_units, output_dim) + self.softmax = nn.Softmax(dim=1) + + def forward(self, x): + x, _ = self.gru(x.view(x.size(0), 1, -1)) # Adding sequence dimension + x = self.fc(x[:, -1, :]) # Take the output of the last time step + x = self.softmax(x) + return x + + +def train(model, train_loader, optimizer, criterion, device): + model.train() + for epoch in range(3): # 3 epochs are sufficient for small datasets + for inputs, labels in train_loader: + inputs, labels = inputs.to(device), labels.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + print(f"Training completed over {epoch+1} epochs.") + + +def evaluate(model, data_loader, device): + model.eval() + all_preds = [] + all_labels = [] + with torch.no_grad(): + for inputs, labels in data_loader: + inputs, labels = inputs.to(device), labels.to(device) + outputs = model(inputs) + preds = torch.argmax(outputs, dim=1).cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.cpu().numpy()) + + accuracy = accuracy_score(all_labels, all_preds) + precision, recall, f1, _ = precision_recall_fscore_support( + all_labels, all_preds, average="weighted" + ) + return {"Accuracy": accuracy, "Precision": precision, "Recall": recall, "F1": f1} + + +def main(out_dir): + os.makedirs(out_dir, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + train_loader, val_loader, test_loader = load_data() + + model = SingleLayerGRU(input_dim=512, hidden_units=64, output_dim=4) + model = model.to(device) + + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=0.001) + + train(model, train_loader, optimizer, criterion, device) + metrics = evaluate(model, test_loader, device) + + with open(os.path.join(out_dir, "final_info.json"), "w") as f: + json.dump(metrics, f) + + print(f"Train dataset size: {len(train_loader.dataset)}") + print(f"Validation dataset size: {len(val_loader.dataset)}") + print(f"Test dataset size: {len(test_loader.dataset)}") + print("Experiment completed successfully.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--out_dir", type=str, required=True) + args = parser.parse_args() + main(args.out_dir) diff --git a/frontend/demo_cache/generated/experiments/idea-2/run_1/final_info.json b/frontend/demo_cache/generated/experiments/idea-2/run_1/final_info.json new file mode 100644 index 0000000..4d5235d --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-2/run_1/final_info.json @@ -0,0 +1 @@ +{"Accuracy": 0.793, "Precision": 0.795239024398564, "Recall": 0.793, "F1": 0.7925706551495995} diff --git a/frontend/demo_cache/generated/experiments/idea-2/run_2.py b/frontend/demo_cache/generated/experiments/idea-2/run_2.py new file mode 100644 index 0000000..d942d7c --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-2/run_2.py @@ -0,0 +1,122 @@ +import argparse +import json +import os + +import torch +from datasets import load_dataset +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics import accuracy_score, precision_recall_fscore_support +from torch import nn, optim +from torch.utils.data import DataLoader, TensorDataset + + +def load_data(): + dataset = load_dataset("ag_news") + train_data = dataset["train"].select(range(5000)) + val_data = dataset["train"].select(range(5000, 7000)) + test_data = dataset["test"].select(range(2000)) + + vectorizer = TfidfVectorizer(max_features=512, stop_words="english") + + X_train = vectorizer.fit_transform(train_data["text"]).toarray() + X_val = vectorizer.transform(val_data["text"]).toarray() + X_test = vectorizer.transform(test_data["text"]).toarray() + + y_train = torch.tensor(train_data["label"], dtype=torch.long) + y_val = torch.tensor(val_data["label"], dtype=torch.long) + y_test = torch.tensor(test_data["label"], dtype=torch.long) + + train_loader = DataLoader( + TensorDataset(torch.tensor(X_train, dtype=torch.float32), y_train), + batch_size=64, + shuffle=True, + ) + val_loader = DataLoader( + TensorDataset(torch.tensor(X_val, dtype=torch.float32), y_val), + batch_size=64, + shuffle=False, + ) + test_loader = DataLoader( + TensorDataset(torch.tensor(X_test, dtype=torch.float32), y_test), + batch_size=64, + shuffle=False, + ) + + return train_loader, val_loader, test_loader + + +class SingleLayerGRU(nn.Module): + def __init__(self, input_dim=512, hidden_units=128, output_dim=4): + super(SingleLayerGRU, self).__init__() + self.gru = nn.GRU(input_dim, hidden_units, batch_first=True) + self.fc = nn.Linear(hidden_units, output_dim) + self.softmax = nn.Softmax(dim=1) + + def forward(self, x): + x, _ = self.gru(x.view(x.size(0), 1, -1)) + x = self.fc(x[:, -1, :]) + x = self.softmax(x) + return x + + +def train(model, train_loader, optimizer, criterion, device): + model.train() + for epoch in range(5): + for inputs, labels in train_loader: + inputs, labels = inputs.to(device), labels.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + print(f"Training completed over {epoch+1} epochs.") + + +def evaluate(model, data_loader, device): + model.eval() + all_preds = [] + all_labels = [] + with torch.no_grad(): + for inputs, labels in data_loader: + inputs, labels = inputs.to(device), labels.to(device) + outputs = model(inputs) + preds = torch.argmax(outputs, dim=1).cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.cpu().numpy()) + + accuracy = accuracy_score(all_labels, all_preds) + precision, recall, f1, _ = precision_recall_fscore_support( + all_labels, all_preds, average="weighted" + ) + return {"Accuracy": accuracy, "Precision": precision, "Recall": recall, "F1": f1} + + +def main(out_dir): + os.makedirs(out_dir, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + train_loader, val_loader, test_loader = load_data() + + model = SingleLayerGRU(input_dim=512, hidden_units=128, output_dim=4) + model = model.to(device) + + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=0.001) + + train(model, train_loader, optimizer, criterion, device) + metrics = evaluate(model, test_loader, device) + + with open(os.path.join(out_dir, "final_info.json"), "w") as f: + json.dump(metrics, f) + + print(f"Train dataset size: {len(train_loader.dataset)}") + print(f"Validation dataset size: {len(val_loader.dataset)}") + print(f"Test dataset size: {len(test_loader.dataset)}") + print("Experiment completed successfully.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--out_dir", type=str, required=True) + args = parser.parse_args() + main(args.out_dir) diff --git a/frontend/demo_cache/generated/experiments/idea-2/run_2/final_info.json b/frontend/demo_cache/generated/experiments/idea-2/run_2/final_info.json new file mode 100644 index 0000000..f64ec3a --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-2/run_2/final_info.json @@ -0,0 +1 @@ +{"Accuracy": 0.805, "Precision": 0.8056109579615937, "Recall": 0.805, "F1": 0.8047095700204514} diff --git a/frontend/demo_cache/generated/experiments/idea-2/run_2/notes.txt b/frontend/demo_cache/generated/experiments/idea-2/run_2/notes.txt new file mode 100644 index 0000000..90f3881 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-2/run_2/notes.txt @@ -0,0 +1,4 @@ +Experiment Description: +In this experiment, we used a SimpleGRUClassifier with a GRU layer and a linear layer to classify IMDB reviews. The model was trained using subsampled training data from the IMDB dataset, with a validation set for hyperparameter tuning and a separate test set for evaluating the model's generalization. +Run Number: 1 +Results: {'Accuracy': 1.0} diff --git a/frontend/demo_cache/generated/experiments/idea-2/run_3.py b/frontend/demo_cache/generated/experiments/idea-2/run_3.py new file mode 100644 index 0000000..b280f1b --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-2/run_3.py @@ -0,0 +1,126 @@ +import argparse +import json +import os + +import torch +from datasets import load_dataset +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics import accuracy_score, precision_recall_fscore_support +from torch import nn, optim +from torch.utils.data import DataLoader, TensorDataset + + +def load_data(): + dataset = load_dataset("ag_news") + train_data = dataset["train"].select(range(5000)) + val_data = dataset["train"].select(range(5000, 7000)) + test_data = dataset["test"].select(range(2000)) + + vectorizer = TfidfVectorizer(max_features=512, stop_words="english") + + X_train = vectorizer.fit_transform(train_data["text"]).toarray() + X_val = vectorizer.transform(val_data["text"]).toarray() + X_test = vectorizer.transform(test_data["text"]).toarray() + + y_train = torch.tensor(train_data["label"], dtype=torch.long) + y_val = torch.tensor(val_data["label"], dtype=torch.long) + y_test = torch.tensor(test_data["label"], dtype=torch.long) + + train_loader = DataLoader( + TensorDataset(torch.tensor(X_train, dtype=torch.float32), y_train), + batch_size=64, + shuffle=True, + ) + val_loader = DataLoader( + TensorDataset(torch.tensor(X_val, dtype=torch.float32), y_val), + batch_size=64, + shuffle=False, + ) + test_loader = DataLoader( + TensorDataset(torch.tensor(X_test, dtype=torch.float32), y_test), + batch_size=64, + shuffle=False, + ) + + return train_loader, val_loader, test_loader + + +class SingleLayerGRU(nn.Module): + def __init__( + self, input_dim=512, hidden_units=256, output_dim=4 + ): # Changed hidden_units from 128 to 256 + super(SingleLayerGRU, self).__init__() + self.gru = nn.GRU(input_dim, hidden_units, batch_first=True) + self.fc = nn.Linear(hidden_units, output_dim) + self.softmax = nn.Softmax(dim=1) + + def forward(self, x): + x, _ = self.gru(x.view(x.size(0), 1, -1)) + x = self.fc(x[:, -1, :]) + x = self.softmax(x) + return x + + +def train(model, train_loader, optimizer, criterion, device): + model.train() + for epoch in range(5): + for inputs, labels in train_loader: + inputs, labels = inputs.to(device), labels.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + print(f"Training completed over {epoch+1} epochs.") + + +def evaluate(model, data_loader, device): + model.eval() + all_preds = [] + all_labels = [] + with torch.no_grad(): + for inputs, labels in data_loader: + inputs, labels = inputs.to(device), labels.to(device) + outputs = model(inputs) + preds = torch.argmax(outputs, dim=1).cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.cpu().numpy()) + + accuracy = accuracy_score(all_labels, all_preds) + precision, recall, f1, _ = precision_recall_fscore_support( + all_labels, all_preds, average="weighted" + ) + return {"Accuracy": accuracy, "Precision": precision, "Recall": recall, "F1": f1} + + +def main(out_dir): + os.makedirs(out_dir, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + train_loader, val_loader, test_loader = load_data() + + model = SingleLayerGRU( + input_dim=512, hidden_units=256, output_dim=4 + ) # Changed hidden_units from 128 to 256 + model = model.to(device) + + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=0.001) + + train(model, train_loader, optimizer, criterion, device) + metrics = evaluate(model, test_loader, device) + + with open(os.path.join(out_dir, "final_info.json"), "w") as f: + json.dump(metrics, f) + + print(f"Train dataset size: {len(train_loader.dataset)}") + print(f"Validation dataset size: {len(val_loader.dataset)}") + print(f"Test dataset size: {len(test_loader.dataset)}") + print("Experiment completed successfully.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--out_dir", type=str, required=True) + args = parser.parse_args() + main(args.out_dir) diff --git a/frontend/demo_cache/generated/experiments/idea-2/run_3/final_info.json b/frontend/demo_cache/generated/experiments/idea-2/run_3/final_info.json new file mode 100644 index 0000000..af16b5b --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-2/run_3/final_info.json @@ -0,0 +1 @@ +{"Accuracy": 0.7985, "Precision": 0.8008647556477098, "Recall": 0.7985, "F1": 0.7981580518342577} diff --git a/frontend/demo_cache/generated/experiments/idea-2/run_3/notes.txt b/frontend/demo_cache/generated/experiments/idea-2/run_3/notes.txt new file mode 100644 index 0000000..ba51aef --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-2/run_3/notes.txt @@ -0,0 +1,4 @@ +Experiment Description: +In this experiment, we used a SimpleGRUClassifier with a GRU layer and a linear layer to classify IMDB reviews. The model was trained using subsampled training data from the IMDB dataset, with a validation set for hyperparameter tuning and a separate test set for evaluating the model's generalization. +Run Number: 2 +Results: {'Accuracy': 1.0} diff --git a/frontend/demo_cache/generated/experiments/idea-3/experiment.py b/frontend/demo_cache/generated/experiments/idea-3/experiment.py new file mode 100644 index 0000000..48cb308 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-3/experiment.py @@ -0,0 +1,143 @@ +import argparse +import json +import os + +import torch +from datasets import load_dataset +from nltk.translate.bleu_score import corpus_bleu +from rouge_score import rouge_scorer +from sklearn.feature_extraction.text import CountVectorizer +from torch import nn, optim +from torch.utils.data import DataLoader, TensorDataset + + +def load_data(): + # Load the ag_news dataset + dataset = load_dataset("ag_news") + # Subsample the dataset + train_data = dataset["train"].select(range(3500)) + val_data = dataset["train"].select(range(3500, 4500)) + test_data = dataset["test"].select(range(500)) + + # Use Count Vectorizer for converting text data to vectors, ensuring a fixed input size of 500 + vectorizer = CountVectorizer(max_features=500, stop_words="english") + + X_train = vectorizer.fit_transform(train_data["text"]).toarray() + X_val = vectorizer.transform(val_data["text"]).toarray() + X_test = vectorizer.transform(test_data["text"]).toarray() + + y_train = torch.tensor(train_data["label"], dtype=torch.long) + y_val = torch.tensor(val_data["label"], dtype=torch.long) + y_test = torch.tensor(test_data["label"], dtype=torch.long) + + # Create DataLoaders for the datasets + train_loader = DataLoader( + TensorDataset(torch.tensor(X_train, dtype=torch.float32), y_train), + batch_size=64, + shuffle=True, + ) + val_loader = DataLoader( + TensorDataset(torch.tensor(X_val, dtype=torch.float32), y_val), + batch_size=64, + shuffle=False, + ) + test_loader = DataLoader( + TensorDataset(torch.tensor(X_test, dtype=torch.float32), y_test), + batch_size=64, + shuffle=False, + ) + + return train_loader, val_loader, test_loader + + +class SingleLayerGRU(nn.Module): + def __init__( + self, input_dim=500, hidden_units=128, output_dim=4 + ): # Increased hidden units + super(SingleLayerGRU, self).__init__() + self.gru = nn.GRU(input_dim, hidden_units, batch_first=True) + self.fc = nn.Linear(hidden_units, output_dim) + self.softmax = nn.Softmax(dim=1) + + def forward(self, x): + x = x.unsqueeze(1) # Add sequence dimension + x, _ = self.gru(x) + x = self.fc(x[:, -1, :]) + x = self.softmax(x) + return x + + +def train(model, train_loader, optimizer, criterion, device): + model.train() + for epoch in range(10): # Increase epochs + for inputs, labels in train_loader: + inputs, labels = inputs.to(device), labels.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + print(f"Training completed over {epoch+1} epochs.") + + +def evaluate(model, data_loader, device): + model.eval() + all_preds = [] + all_labels = [] + with torch.no_grad(): + for inputs, labels in data_loader: + inputs, labels = inputs.to(device), labels.to(device) + outputs = model(inputs) + preds = torch.argmax(outputs, dim=1).cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.cpu().numpy()) + + # Compute BLEU and ROUGE metrics + bleu_score = corpus_bleu( + [[str(l)] for l in all_labels], [str(p) for p in all_preds] + ) + rouge_scorer_obj = rouge_scorer.RougeScorer(["rouge1", "rougeL"], use_stemmer=True) + rouge_scores = [ + rouge_scorer_obj.score(str(l), str(p)) for l, p in zip(all_labels, all_preds) + ] + avg_rouge1 = sum([score["rouge1"].fmeasure for score in rouge_scores]) / len( + rouge_scores + ) + avg_rougeL = sum([score["rougeL"].fmeasure for score in rouge_scores]) / len( + rouge_scores + ) + + return {"BLEU": bleu_score, "ROUGE-1": avg_rouge1, "ROUGE-L": avg_rougeL} + + +def main(out_dir): + os.makedirs(out_dir, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + train_loader, val_loader, test_loader = load_data() + + model = SingleLayerGRU( + input_dim=500, hidden_units=128, output_dim=4 + ) # Updated model + model = model.to(device) + + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=0.001) + + train(model, train_loader, optimizer, criterion, device) + metrics = evaluate(model, test_loader, device) + + with open(os.path.join(out_dir, "final_info.json"), "w") as f: + json.dump(metrics, f) + + print(f"Train dataset size: {len(train_loader.dataset)}") + print(f"Validation dataset size: {len(val_loader.dataset)}") + print(f"Test dataset size: {len(test_loader.dataset)}") + print("Experiment completed successfully.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--out_dir", type=str, required=True) + args = parser.parse_args() + main(args.out_dir) diff --git a/frontend/demo_cache/generated/experiments/idea-3/experiment_results.txt b/frontend/demo_cache/generated/experiments/idea-3/experiment_results.txt new file mode 100644 index 0000000..e323502 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-3/experiment_results.txt @@ -0,0 +1,17 @@ +{ + "run_1": { + "BLEU": 1.7229823170315744e-231, + "ROUGE-1": 0.8, + "ROUGE-L": 0.8 + }, + "run_2": { + "BLEU": 1.7420470999998382e-231, + "ROUGE-1": 0.836, + "ROUGE-L": 0.836 + }, + "run_3": { + "BLEU": 1.7315333603727777e-231, + "ROUGE-1": 0.816, + "ROUGE-L": 0.816 + } +} diff --git a/frontend/demo_cache/generated/experiments/idea-3/notes.txt b/frontend/demo_cache/generated/experiments/idea-3/notes.txt new file mode 100644 index 0000000..47c6d3b --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-3/notes.txt @@ -0,0 +1,17 @@ +Figure 1: filename_figure1.png +This plot illustrates the relationship between variable X and variable Y, showcasing a positive linear correlation. As variable X increases, variable Y also tends to increase, indicating a direct relationship. The data points are scattered closely along the line of best fit, suggesting a strong correlation. This plot is useful for understanding how changes in X may predict changes in Y. + +Figure 2: filename_figure2.png +The bar chart depicted in this figure represents the distribution of category Z across different groups. Each bar corresponds to a different group, and the height of the bar indicates the frequency or count of category Z within that group. This visualization helps in comparing the prevalence of category Z among the various groups, highlighting any significant differences or trends. + +Figure 3: filename_figure3.png +This figure presents a histogram of variable A, providing insights into its distribution. The x-axis represents the range of variable A, while the y-axis indicates the frequency of occurrences within each bin. The shape of the histogram reveals whether the data is normally distributed, skewed, or has multiple modes. This information is crucial for understanding the overall pattern and any potential outliers in the dataset. + +Figure 4: filename_figure4.png +The scatter plot shown in this figure examines the interaction between variables B and C. Each point on the plot represents an observation, with its position determined by the values of B and C. A trend line is included to highlight any potential correlation between the two variables. This plot aids in identifying patterns, clusters, or anomalies and is essential for exploratory data analysis. + +Figure 5: filename_figure5.png +This line graph illustrates the time series data of variable D over a defined period. The x-axis represents time intervals, while the y-axis displays the values of variable D. The line connecting the data points provides a visual representation of trends, cycles, or seasonality present in the data. Observing this plot can help predict future values and understand past behavior of variable D. + +Figure 6: filename_figure6.png +The pie chart in this figure represents the composition of variable E, divided into different segments that illustrate the proportional contribution of each component to the whole. Each segment is labeled with either the component name or percentage it represents. This visualization is effective in conveying the relative sizes of parts within a dataset, offering a clear view of the distribution and significance of each component. diff --git a/frontend/demo_cache/generated/experiments/idea-3/run_1.py b/frontend/demo_cache/generated/experiments/idea-3/run_1.py new file mode 100644 index 0000000..3ffc535 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-3/run_1.py @@ -0,0 +1,139 @@ +import argparse +import json +import os + +import torch +from datasets import load_dataset +from nltk.translate.bleu_score import corpus_bleu +from rouge_score import rouge_scorer +from sklearn.feature_extraction.text import CountVectorizer +from torch import nn, optim +from torch.utils.data import DataLoader, TensorDataset + + +def load_data(): + # Load the ag_news dataset + dataset = load_dataset("ag_news") + # Subsample the dataset + train_data = dataset["train"].select(range(3500)) + val_data = dataset["train"].select(range(3500, 4500)) + test_data = dataset["test"].select(range(500)) + + # Use Count Vectorizer for converting text data to vectors, ensuring a fixed input size of 500 + vectorizer = CountVectorizer(max_features=500, stop_words="english") + + X_train = vectorizer.fit_transform(train_data["text"]).toarray() + X_val = vectorizer.transform(val_data["text"]).toarray() + X_test = vectorizer.transform(test_data["text"]).toarray() + + y_train = torch.tensor(train_data["label"], dtype=torch.long) + y_val = torch.tensor(val_data["label"], dtype=torch.long) + y_test = torch.tensor(test_data["label"], dtype=torch.long) + + # Create DataLoaders for the datasets + train_loader = DataLoader( + TensorDataset(torch.tensor(X_train, dtype=torch.float32), y_train), + batch_size=64, + shuffle=True, + ) + val_loader = DataLoader( + TensorDataset(torch.tensor(X_val, dtype=torch.float32), y_val), + batch_size=64, + shuffle=False, + ) + test_loader = DataLoader( + TensorDataset(torch.tensor(X_test, dtype=torch.float32), y_test), + batch_size=64, + shuffle=False, + ) + + return train_loader, val_loader, test_loader + + +class SingleLayerGRU(nn.Module): + def __init__(self, input_dim=500, hidden_units=64, output_dim=4): + super(SingleLayerGRU, self).__init__() + self.gru = nn.GRU(input_dim, hidden_units, batch_first=True) + self.fc = nn.Linear(hidden_units, output_dim) + self.softmax = nn.Softmax(dim=1) + + def forward(self, x): + x = x.unsqueeze(1) # Add sequence dimension + x, _ = self.gru(x) + x = self.fc(x[:, -1, :]) + x = self.softmax(x) + return x + + +def train(model, train_loader, optimizer, criterion, device): + model.train() + for epoch in range(3): + for inputs, labels in train_loader: + inputs, labels = inputs.to(device), labels.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + print(f"Training completed over {epoch+1} epochs.") + + +def evaluate(model, data_loader, device): + model.eval() + all_preds = [] + all_labels = [] + with torch.no_grad(): + for inputs, labels in data_loader: + inputs, labels = inputs.to(device), labels.to(device) + outputs = model(inputs) + preds = torch.argmax(outputs, dim=1).cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.cpu().numpy()) + + # Compute BLEU and ROUGE metrics + bleu_score = corpus_bleu( + [[str(l)] for l in all_labels], [str(p) for p in all_preds] + ) + rouge_scorer_obj = rouge_scorer.RougeScorer(["rouge1", "rougeL"], use_stemmer=True) + rouge_scores = [ + rouge_scorer_obj.score(str(l), str(p)) for l, p in zip(all_labels, all_preds) + ] + avg_rouge1 = sum([score["rouge1"].fmeasure for score in rouge_scores]) / len( + rouge_scores + ) + avg_rougeL = sum([score["rougeL"].fmeasure for score in rouge_scores]) / len( + rouge_scores + ) + + return {"BLEU": bleu_score, "ROUGE-1": avg_rouge1, "ROUGE-L": avg_rougeL} + + +def main(out_dir): + os.makedirs(out_dir, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + train_loader, val_loader, test_loader = load_data() + + model = SingleLayerGRU(input_dim=500, hidden_units=64, output_dim=4) + model = model.to(device) + + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=0.001) + + train(model, train_loader, optimizer, criterion, device) + metrics = evaluate(model, test_loader, device) + + with open(os.path.join(out_dir, "final_info.json"), "w") as f: + json.dump(metrics, f) + + print(f"Train dataset size: {len(train_loader.dataset)}") + print(f"Validation dataset size: {len(val_loader.dataset)}") + print(f"Test dataset size: {len(test_loader.dataset)}") + print("Experiment completed successfully.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--out_dir", type=str, required=True) + args = parser.parse_args() + main(args.out_dir) diff --git a/frontend/demo_cache/generated/experiments/idea-3/run_1/final_info.json b/frontend/demo_cache/generated/experiments/idea-3/run_1/final_info.json new file mode 100644 index 0000000..c6bd67c --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-3/run_1/final_info.json @@ -0,0 +1 @@ +{"BLEU": 1.7229823170315744e-231, "ROUGE-1": 0.8, "ROUGE-L": 0.8} diff --git a/frontend/demo_cache/generated/experiments/idea-3/run_2.py b/frontend/demo_cache/generated/experiments/idea-3/run_2.py new file mode 100644 index 0000000..42e4625 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-3/run_2.py @@ -0,0 +1,143 @@ +import argparse +import json +import os + +import torch +from datasets import load_dataset +from nltk.translate.bleu_score import corpus_bleu +from rouge_score import rouge_scorer +from sklearn.feature_extraction.text import CountVectorizer +from torch import nn, optim +from torch.utils.data import DataLoader, TensorDataset + + +def load_data(): + # Load the ag_news dataset + dataset = load_dataset("ag_news") + # Subsample the dataset + train_data = dataset["train"].select(range(3500)) + val_data = dataset["train"].select(range(3500, 4500)) + test_data = dataset["test"].select(range(500)) + + # Use Count Vectorizer for converting text data to vectors, ensuring a fixed input size of 500 + vectorizer = CountVectorizer(max_features=500, stop_words="english") + + X_train = vectorizer.fit_transform(train_data["text"]).toarray() + X_val = vectorizer.transform(val_data["text"]).toarray() + X_test = vectorizer.transform(test_data["text"]).toarray() + + y_train = torch.tensor(train_data["label"], dtype=torch.long) + y_val = torch.tensor(val_data["label"], dtype=torch.long) + y_test = torch.tensor(test_data["label"], dtype=torch.long) + + # Create DataLoaders for the datasets + train_loader = DataLoader( + TensorDataset(torch.tensor(X_train, dtype=torch.float32), y_train), + batch_size=64, + shuffle=True, + ) + val_loader = DataLoader( + TensorDataset(torch.tensor(X_val, dtype=torch.float32), y_val), + batch_size=64, + shuffle=False, + ) + test_loader = DataLoader( + TensorDataset(torch.tensor(X_test, dtype=torch.float32), y_test), + batch_size=64, + shuffle=False, + ) + + return train_loader, val_loader, test_loader + + +class SingleLayerGRU(nn.Module): + def __init__( + self, input_dim=500, hidden_units=128, output_dim=4 + ): # Increased hidden units + super(SingleLayerGRU, self).__init__() + self.gru = nn.GRU(input_dim, hidden_units, batch_first=True) + self.fc = nn.Linear(hidden_units, output_dim) + self.softmax = nn.Softmax(dim=1) + + def forward(self, x): + x = x.unsqueeze(1) # Add sequence dimension + x, _ = self.gru(x) + x = self.fc(x[:, -1, :]) + x = self.softmax(x) + return x + + +def train(model, train_loader, optimizer, criterion, device): + model.train() + for epoch in range(5): # Changed epochs to 5 + for inputs, labels in train_loader: + inputs, labels = inputs.to(device), labels.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + print(f"Training completed over {epoch+1} epochs.") + + +def evaluate(model, data_loader, device): + model.eval() + all_preds = [] + all_labels = [] + with torch.no_grad(): + for inputs, labels in data_loader: + inputs, labels = inputs.to(device), labels.to(device) + outputs = model(inputs) + preds = torch.argmax(outputs, dim=1).cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.cpu().numpy()) + + # Compute BLEU and ROUGE metrics + bleu_score = corpus_bleu( + [[str(l)] for l in all_labels], [str(p) for p in all_preds] + ) + rouge_scorer_obj = rouge_scorer.RougeScorer(["rouge1", "rougeL"], use_stemmer=True) + rouge_scores = [ + rouge_scorer_obj.score(str(l), str(p)) for l, p in zip(all_labels, all_preds) + ] + avg_rouge1 = sum([score["rouge1"].fmeasure for score in rouge_scores]) / len( + rouge_scores + ) + avg_rougeL = sum([score["rougeL"].fmeasure for score in rouge_scores]) / len( + rouge_scores + ) + + return {"BLEU": bleu_score, "ROUGE-1": avg_rouge1, "ROUGE-L": avg_rougeL} + + +def main(out_dir): + os.makedirs(out_dir, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + train_loader, val_loader, test_loader = load_data() + + model = SingleLayerGRU( + input_dim=500, hidden_units=128, output_dim=4 + ) # Updated model + model = model.to(device) + + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=0.001) + + train(model, train_loader, optimizer, criterion, device) + metrics = evaluate(model, test_loader, device) + + with open(os.path.join(out_dir, "final_info.json"), "w") as f: + json.dump(metrics, f) + + print(f"Train dataset size: {len(train_loader.dataset)}") + print(f"Validation dataset size: {len(val_loader.dataset)}") + print(f"Test dataset size: {len(test_loader.dataset)}") + print("Experiment completed successfully.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--out_dir", type=str, required=True) + args = parser.parse_args() + main(args.out_dir) diff --git a/frontend/demo_cache/generated/experiments/idea-3/run_2/final_info.json b/frontend/demo_cache/generated/experiments/idea-3/run_2/final_info.json new file mode 100644 index 0000000..107d9a1 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-3/run_2/final_info.json @@ -0,0 +1 @@ +{"BLEU": 1.7420470999998382e-231, "ROUGE-1": 0.836, "ROUGE-L": 0.836} diff --git a/frontend/demo_cache/generated/experiments/idea-3/run_2/notes.txt b/frontend/demo_cache/generated/experiments/idea-3/run_2/notes.txt new file mode 100644 index 0000000..90f3881 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-3/run_2/notes.txt @@ -0,0 +1,4 @@ +Experiment Description: +In this experiment, we used a SimpleGRUClassifier with a GRU layer and a linear layer to classify IMDB reviews. The model was trained using subsampled training data from the IMDB dataset, with a validation set for hyperparameter tuning and a separate test set for evaluating the model's generalization. +Run Number: 1 +Results: {'Accuracy': 1.0} diff --git a/frontend/demo_cache/generated/experiments/idea-3/run_3.py b/frontend/demo_cache/generated/experiments/idea-3/run_3.py new file mode 100644 index 0000000..48cb308 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-3/run_3.py @@ -0,0 +1,143 @@ +import argparse +import json +import os + +import torch +from datasets import load_dataset +from nltk.translate.bleu_score import corpus_bleu +from rouge_score import rouge_scorer +from sklearn.feature_extraction.text import CountVectorizer +from torch import nn, optim +from torch.utils.data import DataLoader, TensorDataset + + +def load_data(): + # Load the ag_news dataset + dataset = load_dataset("ag_news") + # Subsample the dataset + train_data = dataset["train"].select(range(3500)) + val_data = dataset["train"].select(range(3500, 4500)) + test_data = dataset["test"].select(range(500)) + + # Use Count Vectorizer for converting text data to vectors, ensuring a fixed input size of 500 + vectorizer = CountVectorizer(max_features=500, stop_words="english") + + X_train = vectorizer.fit_transform(train_data["text"]).toarray() + X_val = vectorizer.transform(val_data["text"]).toarray() + X_test = vectorizer.transform(test_data["text"]).toarray() + + y_train = torch.tensor(train_data["label"], dtype=torch.long) + y_val = torch.tensor(val_data["label"], dtype=torch.long) + y_test = torch.tensor(test_data["label"], dtype=torch.long) + + # Create DataLoaders for the datasets + train_loader = DataLoader( + TensorDataset(torch.tensor(X_train, dtype=torch.float32), y_train), + batch_size=64, + shuffle=True, + ) + val_loader = DataLoader( + TensorDataset(torch.tensor(X_val, dtype=torch.float32), y_val), + batch_size=64, + shuffle=False, + ) + test_loader = DataLoader( + TensorDataset(torch.tensor(X_test, dtype=torch.float32), y_test), + batch_size=64, + shuffle=False, + ) + + return train_loader, val_loader, test_loader + + +class SingleLayerGRU(nn.Module): + def __init__( + self, input_dim=500, hidden_units=128, output_dim=4 + ): # Increased hidden units + super(SingleLayerGRU, self).__init__() + self.gru = nn.GRU(input_dim, hidden_units, batch_first=True) + self.fc = nn.Linear(hidden_units, output_dim) + self.softmax = nn.Softmax(dim=1) + + def forward(self, x): + x = x.unsqueeze(1) # Add sequence dimension + x, _ = self.gru(x) + x = self.fc(x[:, -1, :]) + x = self.softmax(x) + return x + + +def train(model, train_loader, optimizer, criterion, device): + model.train() + for epoch in range(10): # Increase epochs + for inputs, labels in train_loader: + inputs, labels = inputs.to(device), labels.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + print(f"Training completed over {epoch+1} epochs.") + + +def evaluate(model, data_loader, device): + model.eval() + all_preds = [] + all_labels = [] + with torch.no_grad(): + for inputs, labels in data_loader: + inputs, labels = inputs.to(device), labels.to(device) + outputs = model(inputs) + preds = torch.argmax(outputs, dim=1).cpu().numpy() + all_preds.extend(preds) + all_labels.extend(labels.cpu().numpy()) + + # Compute BLEU and ROUGE metrics + bleu_score = corpus_bleu( + [[str(l)] for l in all_labels], [str(p) for p in all_preds] + ) + rouge_scorer_obj = rouge_scorer.RougeScorer(["rouge1", "rougeL"], use_stemmer=True) + rouge_scores = [ + rouge_scorer_obj.score(str(l), str(p)) for l, p in zip(all_labels, all_preds) + ] + avg_rouge1 = sum([score["rouge1"].fmeasure for score in rouge_scores]) / len( + rouge_scores + ) + avg_rougeL = sum([score["rougeL"].fmeasure for score in rouge_scores]) / len( + rouge_scores + ) + + return {"BLEU": bleu_score, "ROUGE-1": avg_rouge1, "ROUGE-L": avg_rougeL} + + +def main(out_dir): + os.makedirs(out_dir, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + train_loader, val_loader, test_loader = load_data() + + model = SingleLayerGRU( + input_dim=500, hidden_units=128, output_dim=4 + ) # Updated model + model = model.to(device) + + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=0.001) + + train(model, train_loader, optimizer, criterion, device) + metrics = evaluate(model, test_loader, device) + + with open(os.path.join(out_dir, "final_info.json"), "w") as f: + json.dump(metrics, f) + + print(f"Train dataset size: {len(train_loader.dataset)}") + print(f"Validation dataset size: {len(val_loader.dataset)}") + print(f"Test dataset size: {len(test_loader.dataset)}") + print("Experiment completed successfully.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--out_dir", type=str, required=True) + args = parser.parse_args() + main(args.out_dir) diff --git a/frontend/demo_cache/generated/experiments/idea-3/run_3/final_info.json b/frontend/demo_cache/generated/experiments/idea-3/run_3/final_info.json new file mode 100644 index 0000000..7c79ec3 --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-3/run_3/final_info.json @@ -0,0 +1 @@ +{"BLEU": 1.7315333603727777e-231, "ROUGE-1": 0.816, "ROUGE-L": 0.816} diff --git a/frontend/demo_cache/generated/experiments/idea-3/run_3/notes.txt b/frontend/demo_cache/generated/experiments/idea-3/run_3/notes.txt new file mode 100644 index 0000000..ba51aef --- /dev/null +++ b/frontend/demo_cache/generated/experiments/idea-3/run_3/notes.txt @@ -0,0 +1,4 @@ +Experiment Description: +In this experiment, we used a SimpleGRUClassifier with a GRU layer and a linear layer to classify IMDB reviews. The model was trained using subsampled training data from the IMDB dataset, with a validation set for hyperparameter tuning and a separate test set for evaluating the model's generalization. +Run Number: 2 +Results: {'Accuracy': 1.0} diff --git a/frontend/demo_cache/generated/papers/idea-1/investigating_adaptive_prompt_decomposition_for_improved_long-range_coherence_in_code_generation.pdf b/frontend/demo_cache/generated/papers/idea-1/investigating_adaptive_prompt_decomposition_for_improved_long-range_coherence_in_code_generation.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ea255ff1e148bd438346b8548970bbe9a2b7507d GIT binary patch literal 550657 zcmb?k2|SeD_m>tzD3umzS}@A;%s!&Bl%?!@*%}PS&LD(}kR&ZCT1bi%Nwh1KXrm|+ zQrRn&HAO+c!#>FIL;0uI4> z*}Dp;s0b(;`g%F~J1~85c<&%@M<-cz! z;Rr+l#q}&lHqHu125%bUXiDf0fkZ=pi3HeyF^)(hp}!<59sMO!;NLgKQOMYP6guac zPQmWev34j**mpKuB*8nQlw%}mF#H(qUe16KNE;6QlUyG_%Eo>G--9f8`Y}EI*f{iW 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- {/* GitHub Logo */} - e.target.style.color = '#fff'} - onMouseLeave={(e) => e.target.style.color = '#A0AEC0'} - > - {githubIcon} - +
+ {/* Demo Mode Button */} + + + {/* GitHub Logo */} + (e.target.style.color = '#fff')} + onMouseLeave={(e) => (e.target.style.color = '#A0AEC0')} + > + {githubIcon} + +
); }; diff --git a/frontend/src/components/TreePlotVisualization.jsx b/frontend/src/components/TreePlotVisualization.jsx index 4bf56ed..5134f1b 100644 --- a/frontend/src/components/TreePlotVisualization.jsx +++ b/frontend/src/components/TreePlotVisualization.jsx @@ -553,6 +553,44 @@ const TreePlotVisualization = () => { const [isReviewing, setIsReviewing] = useState(false); const [rightPanelTab, setRightPanelTab] = useState('comments'); // 'comments' or 'review' + const isDemoRoute = + typeof window !== 'undefined' && + (window.location.pathname === '/demo' || + window.location.pathname.startsWith('/demo/')); + const FALLBACK_DEMO_INTENT = + 'Adaptive Prompt Decomposition for Coherent Long-Range Code Generation'; + const demoDefaultIntent = + (typeof process !== 'undefined' && process.env.REACT_APP_DEMO_INTENT) || + FALLBACK_DEMO_INTENT; + + useEffect(() => { + if (isDemoRoute) { + setCurrentView('exploration'); + setIsConfigured(true); + } + }, [isDemoRoute]); + + useEffect(() => { + if (!isDemoRoute) { + return; + } + let desiredIntent = demoDefaultIntent; + if (typeof window !== 'undefined') { + try { + const stored = window.sessionStorage.getItem('demo_intent_prefill'); + if (stored) { + desiredIntent = stored; + window.sessionStorage.removeItem('demo_intent_prefill'); + } + } catch (_) { + // ignore storage errors + } + } + if (desiredIntent && analysisIntent !== desiredIntent) { + setAnalysisIntent(desiredIntent); + } + }, [isDemoRoute, analysisIntent, demoDefaultIntent]); + const experimentFileCount = experimentFileList.length; const hasExperimentFiles = experimentFileCount > 0; @@ -2972,10 +3010,24 @@ const TreePlotVisualization = () => { setPdfComments(pdfComments.filter(c => c.id !== commentId)); }; + const buildRelativeApiUrl = (path) => { + if (!path) return path; + if (/^https?:\/\//i.test(path)) { + return path; + } + if (path.startsWith('/')) { + return path; + } + return `/${path}`; + }; + const downloadPDF = async (pdfPath) => { try { + const requestUrl = buildRelativeApiUrl(pdfPath); // Fetch the PDF as a blob to force download - const response = await fetch(`http://localhost:5000${pdfPath}`); + const response = await fetch(requestUrl, { + credentials: 'include', + }); if (!response.ok) { throw new Error(`Failed to download PDF: ${response.status}`); @@ -3015,13 +3067,14 @@ const TreePlotVisualization = () => { try { console.log('Starting paper review for:', pdfPath); - const response = await fetch('http://localhost:5000/api/review', { + const response = await fetch('/api/review', { method: 'POST', headers: { 'Content-Type': 'application/json', }, + credentials: 'include', body: JSON.stringify({ - pdf_path: pdfPath + pdf_path: buildRelativeApiUrl(pdfPath) }), }); @@ -3636,7 +3689,7 @@ const TreePlotVisualization = () => {