diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..1cc393c Binary files /dev/null and b/.DS_Store differ diff --git a/.gitignore b/.gitignore index 4ef4b7b..51adb0f 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,2 @@ -*.rdb \ No newline at end of file +*.rdb +.envbackend/.env diff --git a/README.md b/README.md index 4240c9f..da05334 100644 --- a/README.md +++ b/README.md @@ -1,20 +1,19 @@ -# TEP-LLM +# FaultExplainer -This project consists of a backend and a frontend. +This project consists of a backend and a frontend. It requires a valid `OPENAI_API_KEY` to be placed in `backend/.env` ## Backend -The backend requires `redis-server` to be already installed. To run the backend, follow these steps: +To run the backend, follow these steps: -1. Start the `redis-server`. +1. Create a virtual environment and install necessary packages `pip install -r backend/requirements.txt`. [Save yourself some time while installing requirements.txt. Install graphviz instead of pygraphviz {https://stackoverflow.com/questions/40266604/pip-install-pygraphviz-fails-failed-building-wheel-for-pygraphviz}. Steps are here depending on the OS you are running] 2. Change directory to the backend folder: `cd backend`. -3. Run the backend application: `python app.py`. +3. Run the backend application: `fastapi dev app.py`. ## Frontend To start the frontend, follow these steps: -1. Change directory to the frontend folder: `cd frontend`. -2. Install the required dependencies using `yarn`: `yarn`. +1. Start up a new terminal and change directory to the frontend folder: `cd frontend`. +2. Install the required dependencies using yarn: `yarn`. 3. Start the frontend development server: `yarn dev`. - diff --git a/backend/.env b/backend/.env new file mode 100644 index 0000000..401c969 --- /dev/null +++ b/backend/.env @@ -0,0 +1 @@ +OPENAI_API_KEY='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' \ No newline at end of file diff --git a/backend/.gitignore b/backend/.gitignore index ba0430d..efa407c 100644 --- a/backend/.gitignore +++ b/backend/.gitignore @@ -1 +1,162 @@ -__pycache__/ \ No newline at end of file +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ \ No newline at end of file diff --git a/backend/app.py b/backend/app.py index b777361..3e37beb 100644 --- a/backend/app.py +++ b/backend/app.py @@ -1,259 +1,655 @@ -from flask import Flask, jsonify, request -from flask_socketio import SocketIO -from flask_cors import CORS -import redis -import threading -from csvSimulator import DataFrameSimulator -from langchain.prompts import PromptTemplate -from langchain.chat_models import ChatOpenAI -from langchain.schema import StrOutputParser -from langchain.schema.messages import HumanMessage, SystemMessage - +# # imports +# from openai import OpenAI +# from fastapi import FastAPI, HTTPException +# from fastapi.responses import StreamingResponse +# from fastapi.middleware.cors import CORSMiddleware +# from pydantic import BaseModel +# from dotenv import load_dotenv +# import json +# from asyncio import sleep +# import base64 +# import matplotlib + +# matplotlib.use("Agg") +# import matplotlib.pyplot as plt +# import io +# import networkx as nx +# from networkx.drawing.nx_agraph import graphviz_layout + + +# cols = [ +# "A Feed", +# "D Feed", +# "E Feed", +# "A and C Feed", +# "Recycle Flow", +# "Reactor Feed Rate", +# "Reactor Pressure", +# "Reactor Level", +# "Reactor Temperature", +# "Purge Rate", +# "Product Sep Temp", +# "Product Sep Level", +# "Product Sep Pressure", +# "Product Sep Underflow", +# "Stripper Level", +# "Stripper Pressure", +# "Stripper Underflow", +# "Stripper Temp", +# "Stripper Steam Flow", +# "Compressor Work", +# "Reactor Coolant Temp", +# "Separator Coolant Temp", +# "D feed load", +# "E feed load", +# "A feed load", +# "A and C feed load", +# "Compressor recycle valve", +# "Purge valve", +# "Separator liquid load", +# "Stripper liquid load", +# "Stripper steam valve", +# "Reactor coolant load", +# "Condenser coolant load", +# ] +# mapping = {f"x{idx+1}": c for idx, c in enumerate(cols)} + +# G = nx.read_adjlist("./cg.adjlist", create_using=nx.DiGraph) +# # G = nx.reverse(G) +# G = nx.relabel_nodes(G, mapping) + + +# def get_subgraph(G, nodes): +# subgraph_nodes = set(nodes) +# for node in nodes: +# subgraph_nodes.update(G.predecessors(node)) +# subgraph_nodes.update(G.successors(node)) +# return G.subgraph(subgraph_nodes) + + +# load_dotenv() + + +# INTRO_MESSAGE = """The process produces two products from four reactants. Also present are an inert and a byproduct making a total of eight components: +# A, B, C, D, E, F, G, and H. The reactions are: + +# A(g) + C(g) + D(g) - G(liq): Product 1, + +# A(g) + C(g) + E(g) - H(liq): Product 2, + +# A(g) + E(g) - F(liq): Byproduct, + +# 3D(g) - 2F(liq): Byproduct. + +# All the reactions are irreversible and exothermic. The reaction rates are a function of temperature through an Arrhenius expression. +# The reaction to produce G has a higher activation energy resulting in more sensitivity to temperature. +# Also, the reactions are approximately first-order with respect to the reactant concentrations. + +# The process has five major unit operations: the reactor, the product condenser, a vapor-liquid separator, a recycle compressor and a product stripper. +# Figure showing a diagram of the process is attached. + +# The gaseous reactants are fed to the reactor where they react to form liquid products. The gas phase reactions are catalyzed by a nonvolatile catalyst dissolved +# in the liquid phase. The reactor has an internal cooling bundle for removing the heat of reaction. The products leave the reactor as vapors along with the unreacted feeds. +# The catalyst remains in the reactor. The reactor product stream passes through a cooler for condensing the products and from there to a vapor-liquid separator. +# Noncondensed components recycle back through a centrifugal compressor to the reactor feed. +# Condensed components move to a product stripping column to remove remaining reactants by stripping with feed stream number 4. +# Products G and H exit the stripper base and are separated in a downstream refining section which is not included in this problem. +# The inert and byproduct are primarily purged from the system as a vapor from the vapor-liquid separator.""" + +# SYSTEM_MESSAGE = ( +# "You are a helpful AI chatbot trained to assist with " +# "monitoring the Tennessee Eastman process. The Tennessee Eastman " +# f"Process can be described as follows:\n{INTRO_MESSAGE}" +# "\n\nYour purpose is to help the user identify and understand potential " +# "explanations for any faults that occur during the process. You should " +# "explain your reasoning using the graphs provided." +# ) + +# EXPLAIN_PROMPT = ( +# "You are provided with the general schematics of the Tennessee" +# "Eastman process, causal graphs of different features and graphs showing " +# "the values of the top contributing features for a recent fault. For every " +# "contributing feature reason about the observation graphs (not all " +# "contributing features might have sudden change around the fault) and " +# "create hypotheses for these observations based on the causal graph. " +# "Finally combine these hypotheses in order to generate an explanation as to" +# "why this fault occurred and how it is propagating." +# ) + +# client = OpenAI() + +# # Initialize FastAPI app +# app = FastAPI() + +# origins = ["http://localhost", "http://localhost:5173", "*"] + +# app.add_middleware( +# CORSMiddleware, +# allow_origins=origins, +# allow_credentials=True, +# allow_methods=["*"], +# allow_headers=["*"], +# ) + + +# # Define request and response models +# class MessageRequest(BaseModel): +# data: list[dict[str, str]] +# id: str + + +# class ExplainationRequest(BaseModel): +# data: dict[str, list[float]] +# id: str + + +# class Image(BaseModel): +# image: str +# name: str + + +# class MessageResponse(BaseModel): +# content: str +# images: list[Image] = [] +# index: int +# id: str + + +# def ChatModelCompletion( +# messages: list[dict[str, str]], msg_id: str, images: list[str] = None +# ): +# # Send the message to OpenAI's GPT-4 +# # print(messages) +# response = client.chat.completions.create( +# model="gpt-4o", +# messages=messages, +# stream=True, +# temperature=0, +# ) +# print("sending response") +# index = 0 +# for chunk in response: +# # print(chunk) +# # Extract the response text +# if chunk.choices[0].delta.content: +# response_text = chunk.choices[0].delta.content +# if index == 0 and images: +# response_str = json.dumps( +# { +# "index": index, +# "content": response_text, +# "id": msg_id, +# "images": images, +# } +# ) +# else: +# response_str = json.dumps( +# { +# "index": index, +# "content": response_text, +# "id": msg_id, +# "images": [], +# } +# ) +# index += 1 +# yield "data: " + response_str + "\n\n" +# print(f"Sent {index} chunks") + + +# def plot_causal_subgraph(request: ExplainationRequest): +# nodes_of_interest = request.data.keys() +# subgraph = get_subgraph(G, nodes_of_interest) +# # Visualize the subgraph +# pos = graphviz_layout(subgraph, prog="dot") +# nx.draw( +# subgraph, +# pos, +# with_labels=True, +# node_color="lightblue", +# node_size=500, +# font_size=10, +# arrows=True, +# ) +# nx.draw_networkx_nodes( +# subgraph, pos, nodelist=nodes_of_interest, node_color="red", node_size=600 +# ) +# plt.title("Causal graph of important features (higlighted in red)") +# plt.axis("off") +# img_bytes = io.BytesIO() +# plt.savefig(img_bytes, format="png") +# img_bytes.seek(0) +# img_base64 = base64.b64encode(img_bytes.read()).decode() +# # # DEBUG +# # with open(f"./img/causal_graph.png", "wb") as f: +# # f.write(base64.b64decode(bytes(img_base64, "utf-8"))) +# plt.close() +# return {"image": img_base64, "name": "Causal graph"} + + +# def plot_graphs_to_base64(request: ExplainationRequest): +# graphs = [] +# for feature_name in request.data: +# try: +# # Plot the feature's historical data +# fig, ax = plt.subplots() +# ax.step( +# range(len(request.data[feature_name])), +# request.data[feature_name], +# label=feature_name, +# where="mid", +# ) +# # ax.plot(request.data[feature_name], label=feature_name) + +# ax.axvline( +# x=len(request.data[feature_name]) - 20, +# color="r", +# linestyle="--", +# label="Fault Start", +# ) + +# ax.set_xlabel("Time") +# ax.set_ylabel(feature_name) +# ax.set_title(f"{feature_name} over Time around Fault") + +# # Ensure layout is neat +# plt.tight_layout() + +# # Convert plot to a format that can be sent over WebSocket +# img_bytes = io.BytesIO() +# plt.savefig(img_bytes, format="png") +# img_bytes.seek(0) +# img_base64 = base64.b64encode(img_bytes.read()).decode() +# # # DEBUG +# # with open(f"./img/{feature_name}.png", "wb") as f: +# # f.write(base64.b64decode(bytes(img_base64, "utf-8"))) +# # Send the image to the frontend +# graphs.append({"name": feature_name, "image": img_base64}) +# plt.close(fig) +# except Exception as e: +# print(e) +# return graphs + + +# @app.post("/explain", response_model=None) +# async def explain(request: ExplainationRequest): +# try: +# with open("./tep_flowsheet.png", "rb") as image_file: +# schematic_img_base64 = base64.b64encode(image_file.read()).decode("utf-8") +# graphs = plot_graphs_to_base64(request) +# causal_graph = plot_causal_subgraph(request) +# schema_image = { +# "type": "image_url", +# "image_url": {"url": f"data:image/png;base64,{schematic_img_base64}"}, +# } +# emessages = [ +# {"role": "system", "content": SYSTEM_MESSAGE}, +# { +# "role": "user", +# "content": [ +# {"type": "text", "text": EXPLAIN_PROMPT}, +# schema_image, +# ] +# + [ +# { +# "type": "image_url", +# "image_url": {"url": f"data:image/png;base64,{graph['image']}"}, +# } +# for graph in graphs +# ] +# + [ +# { +# "type": "image_url", +# "image_url": { +# "url": f"data:image/png;base64,{causal_graph['image']}" +# }, +# } +# ], +# }, +# ] +# return StreamingResponse( +# ChatModelCompletion( +# messages=emessages, +# msg_id=request.id, +# images=graphs + [causal_graph], +# ), +# media_type="text/event-stream", +# ) +# except Exception as e: +# raise HTTPException(status_code=500, detail=str(e)) + + +# @app.post("/send_message", response_model=MessageResponse) +# async def send_message(request: MessageRequest): +# try: +# return StreamingResponse( +# ChatModelCompletion(messages=request.data, msg_id=f"reply-{request.id}"), +# media_type="text/event-stream", +# ) +# except Exception as e: +# raise HTTPException(status_code=500, detail=str(e)) + + +# imports +from openai import OpenAI +from fastapi import FastAPI, HTTPException +from fastapi.responses import StreamingResponse +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel +from dotenv import load_dotenv import json -import datetime -import io +from asyncio import sleep import base64 import matplotlib -matplotlib.use('Agg') + +matplotlib.use("Agg") import matplotlib.pyplot as plt -import matplotlib.dates as mdates +import io +import networkx as nx +from networkx.drawing.nx_agraph import graphviz_layout + + +cols = [ + "A Feed", + "D Feed", + "E Feed", + "A and C Feed", + "Recycle Flow", + "Reactor Feed Rate", + "Reactor Pressure", + "Reactor Level", + "Reactor Temperature", + "Purge Rate", + "Product Sep Temp", + "Product Sep Level", + "Product Sep Pressure", + "Product Sep Underflow", + "Stripper Level", + "Stripper Pressure", + "Stripper Underflow", + "Stripper Temp", + "Stripper Steam Flow", + "Compressor Work", + "Reactor Coolant Temp", + "Separator Coolant Temp", + "D feed load", + "E feed load", + "A feed load", + "A and C feed load", + "Compressor recycle valve", + "Purge valve", + "Separator liquid load", + "Stripper liquid load", + "Stripper steam valve", + "Reactor coolant load", + "Condenser coolant load", +] +mapping = {f"x{idx+1}": c for idx, c in enumerate(cols)} + +G = nx.read_adjlist("./cg.adjlist", create_using=nx.DiGraph) +# G = nx.reverse(G) +G = nx.relabel_nodes(G, mapping) + + +def get_full_graph(G): + return G # Return the entire graph + +def get_subgraph(G, nodes): + subgraph_nodes = set(nodes) + for node in nodes: + subgraph_nodes.update(G.predecessors(node)) + subgraph_nodes.update(G.successors(node)) + return G.subgraph(subgraph_nodes) + +load_dotenv() + + +INTRO_MESSAGE = """The process produces two products from four reactants. Also present are an inert and a byproduct making a total of eight components: +A, B, C, D, E, F, G, and H. The reactions are: + +A(g) + C(g) + D(g) - G(liq): Product 1, + +A(g) + C(g) + E(g) - H(liq): Product 2, + +A(g) + E(g) - F(liq): Byproduct, + +3D(g) - 2F(liq): Byproduct. + +All the reactions are irreversible and exothermic. The reaction rates are a function of temperature through an Arrhenius expression. +The reaction to produce G has a higher activation energy resulting in more sensitivity to temperature. +Also, the reactions are approximately first-order with respect to the reactant concentrations. + +The process has five major unit operations: the reactor, the product condenser, a vapor-liquid separator, a recycle compressor and a product stripper. +Figure showing a diagram of the process is attached. + +The gaseous reactants are fed to the reactor where they react to form liquid products. The gas phase reactions are catalyzed by a nonvolatile catalyst dissolved +in the liquid phase. The reactor has an internal cooling bundle for removing the heat of reaction. The products leave the reactor as vapors along with the unreacted feeds. +The catalyst remains in the reactor. The reactor product stream passes through a cooler for condensing the products and from there to a vapor-liquid separator. +Noncondensed components recycle back through a centrifugal compressor to the reactor feed. +Condensed components move to a product stripping column to remove remaining reactants by stripping with feed stream number 4. +Products G and H exit the stripper base and are separated in a downstream refining section which is not included in this problem. +The inert and byproduct are primarily purged from the system as a vapor from the vapor-liquid separator.""" + +SYSTEM_MESSAGE = ( + "You are a helpful AI chatbot trained to assist with " + "monitoring the Tennessee Eastman process. The Tennessee Eastman " + f"Process can be described as follows:\n{INTRO_MESSAGE}" + "\n\nYour purpose is to help the user identify and understand potential " + "explanations for any faults that occur during the process. You should " + "explain your reasoning using the graphs provided." +) +EXPLAIN_PROMPT = ( + "You are provided with the general schematics of the Tennessee" + "Eastman process, causal graphs of different features and graphs showing " + "the values of the top contributing features for a recent fault. For every " + "contributing feature reason about the observation graphs (not all " + "contributing features might have sudden change around the fault) and " + "create hypotheses for these observations based on the causal graph. " + "Finally combine these hypotheses in order to generate an explanation as to" + "why this fault occurred and how it is propagating." +) -# Fault Datacetion model integration -from model import FaultDetectionModel -import pandas as pd +client = OpenAI() -train_data = pd.read_csv("./data/fault0.csv") -fault_detector = FaultDetectionModel(alpha=0.001) -fault_detector.fit(train_data.iloc[:, 1:]) +# Initialize FastAPI app +app = FastAPI() -fault_history = [] +origins = ["http://localhost", "http://localhost:5173", "*"] -def top_feature_graphs(top_features, timestamp): - target_idx = fault_detector.data_buffer[fault_detector.data_buffer['timestamp'] == timestamp].index[0] - start_index = max(0, target_idx - 50) # 30 points before the fault - graphs = [] - for feature in top_features: - # Plot the feature's historical data - fig, ax = plt.subplots() - ax.plot(fault_detector.data_buffer['timestamp'][start_index:target_idx], fault_detector.data_buffer[feature][start_index:target_idx], label=feature) +app.add_middleware( + CORSMiddleware, + allow_origins=origins, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) - ax.axvline(x=timestamp-datetime.timedelta(minutes=fault_detector.post_fault_threshold*3), color='r', linestyle='--', label='Fault Start') +# Define request and response models +class MessageRequest(BaseModel): + data: list[dict[str, str]] + id: str - # Format the x-axis to display dates correctly - ax.xaxis.set_major_locator(mdates.AutoDateLocator()) - ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S')) - # Rotate date labels for better readability - plt.xticks(rotation=45) +class ExplainationRequest(BaseModel): + data: dict[str, list[float]] + id: str - # Set labels and title - ax.set_xlabel('Time') - ax.set_ylabel(feature) - ax.set_title(f'{feature} over Time around Fault') - # Ensure layout is neat - plt.tight_layout() +class Image(BaseModel): + image: str + name: str - # Convert plot to a format that can be sent over WebSocket - img_bytes = io.BytesIO() - plt.savefig(img_bytes, format='png') - img_bytes.seek(0) - img_base64 = base64.b64encode(img_bytes.read()).decode() - # Send the image to the frontend - graphs.append({'feature': feature, 'image': img_base64}) +class MessageResponse(BaseModel): + content: str + images: list[Image] = [] + index: int + id: str - # with open(f"debug_{feature}.txt", "w") as file: - # file.write(img_base64) - plt.close(fig) +def ChatModelCompletion( + messages: list[dict[str, str]], msg_id: str, images: list[str] = None +): + # Send the message to OpenAI's GPT-4 + # print(messages) + response = client.chat.completions.create( + model="gpt-4o", + messages=messages, + stream=True, + temperature=0, + ) + print("sending response") + index = 0 + for chunk in response: + # print(chunk) + # Extract the response text + if chunk.choices[0].delta.content: + response_text = chunk.choices[0].delta.content + if index == 0 and images: + response_str = json.dumps( + { + "index": index, + "content": response_text, + "id": msg_id, + "images": images, + } + ) + else: + response_str = json.dumps( + { + "index": index, + "content": response_text, + "id": msg_id, + "images": [], + } + ) + index += 1 + yield "data: " + response_str + "\n\n" + print(f"Sent {index} chunks") + + +def plot_causal_subgraph(request: ExplainationRequest): + nodes_of_interest = request.data.keys() + full_graph = get_full_graph(G) + sub_graph = get_subgraph(G, nodes_of_interest) + # Visualize the subgraph + pos = graphviz_layout(sub_graph, prog="dot") + nx.draw( + sub_graph, + pos, + with_labels=True, + node_color="lightblue", + node_size=500, + font_size=10, + arrows=True, + ) + nx.draw_networkx_nodes( + sub_graph, pos, nodelist=nodes_of_interest, node_color="red", node_size=600 + ) + plt.title("Causal graph of important features (higlighted in red)") + plt.axis("off") + img_bytes = io.BytesIO() + plt.savefig(img_bytes, format="png") + img_bytes.seek(0) + img_base64 = base64.b64encode(img_bytes.read()).decode() + # # DEBUG + # with open(f"./img/causal_graph.png", "wb") as f: + # f.write(base64.b64decode(bytes(img_base64, "utf-8"))) + plt.close() + return {"image": img_base64, "name": "Causal graph"} + + +def plot_graphs_to_base64(request: ExplainationRequest): + graphs = [] + for feature_name in request.data: + try: + # Plot the feature's historical data + fig, ax = plt.subplots() + ax.step( + range(len(request.data[feature_name])), + request.data[feature_name], + label=feature_name, + where="mid", + ) + # ax.plot(request.data[feature_name], label=feature_name) + + ax.axvline( + x=len(request.data[feature_name]) - 20, + color="r", + linestyle="--", + label="Fault Start", + ) + + ax.set_xlabel("Time") + ax.set_ylabel(feature_name) + ax.set_title(f"{feature_name} over Time around Fault") + + # Ensure layout is neat + plt.tight_layout() + + # Convert plot to a format that can be sent over WebSocket + img_bytes = io.BytesIO() + plt.savefig(img_bytes, format="png") + img_bytes.seek(0) + img_base64 = base64.b64encode(img_bytes.read()).decode() + # # DEBUG + # with open(f"./img/{feature_name}.png", "wb") as f: + # f.write(base64.b64decode(bytes(img_base64, "utf-8"))) + # Send the image to the frontend + graphs.append({"name": feature_name, "image": img_base64}) + plt.close(fig) + except Exception as e: + print(e) return graphs -def handle_fault_detection(processed_data_point): - data_point = processed_data_point['data'] - fault_id = processed_data_point['fault_id'] - # Identify the top contributing features - top_features_contrib = fault_detector.t2_contrib(data_point.iloc[0]['timestamp']) - paired_list = sorted(zip(top_features_contrib, fault_detector.feature_names), reverse=True)[:6] - top_features = [s for _, s in paired_list] - - graphs = top_feature_graphs(top_features, data_point.iloc[0]['timestamp']) - - with open("./tep_flowsheet.png", "rb") as image_file: - schematic_img_base64 = base64.b64encode(image_file.read()).decode('utf-8') - # Add the schematic image - image_data = [ - {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{schematic_img_base64}"}} - ] - image_data.extend([ - {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{graph['image']}"}} - for graph in graphs - ]) - ai_introduction = SystemMessage( - content=[ +@app.post("/explain", response_model=None) +async def explain(request: ExplainationRequest): + try: + with open("./tep_flowsheet.png", "rb") as image_file: + schematic_img_base64 = base64.b64encode(image_file.read()).decode("utf-8") + graphs = plot_graphs_to_base64(request) + causal_graph = plot_causal_subgraph(request) + schema_image = { + "type": "image_url", + "image_url": {"url": f"data:image/png;base64,{schematic_img_base64}"}, + } + emessages = [ + {"role": "system", "content": SYSTEM_MESSAGE}, { - "type": "text", - "text": "I am a helpful AI chatbot trained to assist with monitoring the Tennessee Eastman process. My purpose is to help you identify and understand potential explanations for any faults that occur during the process." - } + "role": "user", + "content": [ + {"type": "text", "text": EXPLAIN_PROMPT}, + schema_image, + ] + + [ + { + "type": "image_url", + "image_url": {"url": f"data:image/png;base64,{graph['image']}"}, + } + for graph in graphs + ] + }, ] - ) - user_prompt = HumanMessage( - content=[ - { - "type": "text", - "text": "You are provided with the general schematics of the Tennessee Eastman reactor and graphs showing the values of the top contributing features for a recent fault. Based on this information, please generate an explanation as to why this fault occurred and how it is propagating." - } - ] + image_data # type: ignore - ) - response = chain.invoke([ai_introduction, user_prompt]) - explanation = response.content if response else "No response received." - # Convert the fault information into a readable message - fault_message = f"Fault detected at {data_point.iloc[0]['timestamp']-datetime.timedelta(minutes=fault_detector.post_fault_threshold*3)}\n {explanation}" - print("Fault explanation sent") - # Send this message to the frontend via WebSocket - socketio.emit('chat_reply', {'images': graphs, 'message': fault_message}) - - # import pdb; pdb.set_trace() - target_idx = fault_detector.data_buffer[fault_detector.data_buffer['timestamp'] == data_point.iloc[0]['timestamp']].index[0] - start_idx = max(0, target_idx-50) - data_df = fault_detector.data_buffer.loc[start_idx:target_idx, top_features+["timestamp"]] - df_melted = data_df.melt(id_vars='timestamp', var_name='key', value_name='value') - result = df_melted.groupby('key').apply(lambda x: x[['timestamp', 'value']].to_dict('records')).to_json() - fault_info = { - 'start_time': int((data_point.iloc[0]['timestamp']-datetime.timedelta(minutes=fault_detector.post_fault_threshold*3)).timestamp()*1000), - 'explanation': explanation, - 'top_features': result - } - fault_history.append(fault_info) - -fault_detector.register_fault_callback(handle_fault_detection) - - -# LLM Integration - -import os - -k = os.environ["OPENAI_API_KEY"] -chain = ChatOpenAI(model='gpt-4-vision-preview', api_key=k, max_tokens=2048) -prompt = PromptTemplate.from_template( -""" -{text} -""" -) -runnable = prompt | chain | StrOutputParser() - -app = Flask(__name__) -app.config['SECRET_KEY'] = 'secret!' -CORS(app) # Enable CORS for all routes -socketio = SocketIO(app, cors_allowed_origins="*") - -import configparser -config = configparser.ConfigParser() -config.read('config.ini') - -redis_host = config['Redis']['host'] -redis_port = int(config['Redis']['port']) # Port should be an integer -redis_db = int(config['Redis']['db']) # DB should be an integer - - -redis_client = redis.Redis(host=redis_host, port=redis_port, db=redis_db) - -simulator = DataFrameSimulator() -simulator.start() - -def data_listener(): - TIME_DELTA = datetime.timedelta(minutes=3) - PREV_TIME = datetime.datetime.now() - TIME_DELTA - while True: - # Block until data is available - _, data = redis_client.blpop('simulator_data') # type: ignore - data_point = json.loads(data.decode('utf-8')) - df_data_point = pd.DataFrame([data_point]).iloc[:, 1:] - df_data_point['timestamp'] = (PREV_TIME + TIME_DELTA) - PREV_TIME = (PREV_TIME + TIME_DELTA) - - socketio.emit('data_update', {'data': df_data_point.iloc[0].to_json()}) - # print(df_data_point.iloc[0].to_json()) - # print(df_data_point.iloc[0]['timestamp'].timestamp() ) - t2_stat, anomaly = fault_detector.process_data_point(df_data_point) - socketio.emit('t2_update', {'t2_stat': t2_stat, 'anomaly': anomaly.item(), 'timestamp': int(df_data_point.iloc[0]['timestamp'].timestamp()*1000) } ) - # handle through callback - # if anomaly: - # handle_fault_detection(df_data_point) - - - -# Start the data listener in a separate thread -listener_thread = threading.Thread(target=data_listener, daemon=True) -listener_thread.start() - -@app.route('/') -def hello_world(): - return "

Hello, World!

" - -@app.route('/set_rate', methods=['POST']) -def set_rate(): - data = request.get_json() - new_rate = float(data.get('rate', 1)) # Default to 1 if not specified - print(new_rate) - simulator.change_rate(new_rate) - return jsonify({'message': f'Rate updated to {new_rate}'}) - -@app.route('/change_state', methods=['POST']) -def change_state(): - data = request.get_json() - new_state = int(data.get('state', 0)) # Default to 0 if not specified - simulator.induce_fault(new_state) - return jsonify({'message': f'State updated to {new_state}'}) - -@app.route('/pause', methods=['POST']) -def pause_sim(): - data = request.get_json() - simulator.pause() - print(simulator.paused) - return jsonify({'message': f'Simulator status updated to Paused'}) - -@app.route('/resume', methods=['POST']) -def resume_sim(): - data = request.get_json() - simulator.resume() - print(simulator.paused) - return jsonify({'message': f'Simulator status updated to Running'}) - - -@app.route('/get_state', methods=['GET']) -def get_state(): - return jsonify({'state': simulator.fault_id}) - -@app.route('/get_rate', methods=['GET']) -def get_rate(): - return jsonify({'rate': float(simulator.rate)}) - -@app.route('/get_pause_status', methods=['GET']) -def get_pause_status(): - return jsonify({'status': simulator.paused}) - -@app.route('/fault_history') -def get_fault_history(): - return jsonify(fault_history) - -# @socketio.on('change_state') -# def handle_change_state(message): -# print(f"Received state change request: {message}") -# fault_id = int(message.split()[-1]) if message.split()[-1].isdigit() else 0 -# simulator.induce_fault(fault_id) - -@socketio.on('chat_message') -def handle_chat_message(message): - print(f"Received chat message: {message}") - # Process the message and generate a reply - # reply = f"SERVER Reply to: {message}" # Example reply - reply = runnable.invoke({"text": message}) - socketio.emit('chat_reply', reply) # Send reply back to the frontend - - -if __name__ == '__main__': - socketio.run(app, host="0.0.0.0", port=5001) # type: ignore + return StreamingResponse( + ChatModelCompletion( + messages=emessages, + msg_id=request.id, + images=graphs, + ), + media_type="text/event-stream", + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@app.post("/send_message", response_model=MessageResponse) +async def send_message(request: MessageRequest): + try: + return StreamingResponse( + ChatModelCompletion(messages=request.data, msg_id=f"reply-{request.id}"), + media_type="text/event-stream", + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) diff --git a/backend/cg.adjlist b/backend/cg.adjlist new file mode 100644 index 0000000..4ce8644 --- /dev/null +++ b/backend/cg.adjlist @@ -0,0 +1,33 @@ +x1 x25 +x2 x23 +x3 x24 +x4 x26 +x5 x27 +x6 x9 +x7 x22 +x8 x9 +x9 x7 +x10 x28 +x11 x13 x14 x27 +x12 x11 +x13 x28 +x14 x29 +x15 x16 x17 x18 +x16 +x17 x30 +x18 +x19 x31 +x20 +x21 x32 +x22 x11 x33 +x23 x6 +x24 x6 +x25 x6 +x26 x15 +x27 x20 +x28 +x29 x15 +x30 x19 +x31 +x32 x9 +x33 \ No newline at end of file diff --git a/backend/cg.png b/backend/cg.png new file mode 100644 index 0000000..e0112fc Binary files /dev/null and b/backend/cg.png differ diff --git a/backend/config.ini b/backend/config.ini deleted file mode 100644 index 994c820..0000000 --- a/backend/config.ini +++ /dev/null @@ -1,4 +0,0 @@ -[Redis] -host = localhost -port = 6379 -db = 0 diff --git a/backend/csvSimulator.py b/backend/csvSimulator.py deleted file mode 100644 index 9cba70c..0000000 --- a/backend/csvSimulator.py +++ /dev/null @@ -1,66 +0,0 @@ -import pandas as pd -import threading -import redis -from simulator import BaseSimulator - -class DataFrameSimulator(BaseSimulator): - initial_file = './data/fault0.csv' - def __init__(self, redis_host='localhost', redis_port=6379, redis_db=0): - super().__init__() - self.data_frame = pd.read_csv(self.initial_file) - self.iterator = self.data_frame.iterrows() - self.redis_client = redis.Redis(host=redis_host, port=redis_port, db=redis_db) - self.fault_id = 0 - - def step(self): - try: - # Get the next row from the iterator - _, row = next(self.iterator) - # print(f"Thread ID: {threading.get_ident()}, Data: {row['time']}") - except StopIteration: - # Restart the iterator if we reach the end of the DataFrame - self.iterator = self.data_frame.iterrows() - _, row = next(self.iterator) - # print(f"Thread ID: {threading.get_ident()}, Data: {row['time']}") - - # Convert the row to a string or a suitable format - data_str = row.to_json() - # Publish the data to Redis - self.redis_client.rpush('simulator_data', data_str) - - def induce_fault(self, fault_id): - self.fault_id = fault_id - # Change the DataFrame based on the fault_id - print(f"Changing dataframe to fault{fault_id}.csv") - new_file = f"./data/fault{fault_id}.csv" - self.data_frame = pd.read_csv(new_file) - self.iterator = self.data_frame.iterrows() - - -if __name__ == "__main__": - import time - - # Create an instance of the Simulator - simulator = DataFrameSimulator() - print(f"Thread ID: {threading.get_ident()}> Starting Simulator") - - # Start the simulator - simulator.start() - time.sleep(5) # Let it run for a while - - print(f"Thread ID: {threading.get_ident()}> Introducing Fault") - # Induce a fault by setting the counter to 3 - simulator.induce_fault(3) - - time.sleep(5) # Let it run for a while after inducing the fault - - # Pause, resume, and stop to demonstrate control - print(f"Thread ID: {threading.get_ident()}> Pausing Simulator") - simulator.pause() - time.sleep(2) # Paused for 2 seconds - print(f"Thread ID: {threading.get_ident()}> Resuming Simulator") - simulator.resume() - - time.sleep(5) # Let it run for a while more - print(f"Thread ID: {threading.get_ident()}> Stopping Simulator") - simulator.stop() diff --git a/backend/data/data_processing.ipynb b/backend/data/data_processing.ipynb deleted file mode 100644 index 7ed320f..0000000 --- a/backend/data/data_processing.ipynb +++ /dev/null @@ -1,477 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import pyreadr" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "data = pyreadr.read_r(\"./TEP_Faulty_Training.RData\")" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "df = data['faulty_training']" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " faultNumber simulationRun sample xmeas_1 xmeas_2 xmeas_3 \\\n", - "0 1 1.0 1 0.25038 3674.0 4529.0 \n", - "1 1 1.0 2 0.25109 3659.4 4556.6 \n", - "2 1 1.0 3 0.25038 3660.3 4477.8 \n", - "3 1 1.0 4 0.24977 3661.3 4512.1 \n", - "4 1 1.0 5 0.29405 3679.0 4497.0 \n", - "... ... ... ... ... ... ... \n", - "4999995 20 500.0 496 0.23419 3655.3 4461.7 \n", - "4999996 20 500.0 497 0.26704 3647.4 4540.2 \n", - "4999997 20 500.0 498 0.26543 3630.3 4571.6 \n", - "4999998 20 500.0 499 0.27671 3655.7 4498.9 \n", - "4999999 20 500.0 500 0.27421 3640.4 4474.4 \n", - "\n", - " xmeas_4 xmeas_5 xmeas_6 xmeas_7 ... xmv_2 xmv_3 xmv_4 \\\n", - "0 9.2320 26.889 42.402 2704.3 ... 53.744 24.657 62.544 \n", - "1 9.4264 26.721 42.576 2705.0 ... 53.414 24.588 59.259 \n", - "2 9.4426 26.875 42.070 2706.2 ... 54.357 24.666 61.275 \n", - "3 9.4776 26.758 42.063 2707.2 ... 53.946 24.725 59.856 \n", - "4 9.3381 26.889 42.650 2705.1 ... 53.658 28.797 60.717 \n", - "... ... ... ... ... ... ... ... ... \n", - "4999995 9.3448 27.008 42.481 2703.0 ... 53.670 23.350 61.061 \n", - "4999996 9.3546 27.034 42.671 2704.7 ... 54.650 26.362 60.020 \n", - "4999997 9.4089 27.129 42.470 2705.1 ... 54.274 26.521 59.824 \n", - "4999998 9.3781 27.353 42.281 2705.8 ... 53.506 26.781 62.818 \n", - "4999999 9.3866 27.145 41.985 2706.0 ... 53.800 27.027 59.757 \n", - "\n", - " xmv_5 xmv_6 xmv_7 xmv_8 xmv_9 xmv_10 xmv_11 \n", - "0 22.137 39.935 42.323 47.757 47.510 41.258 18.447 \n", - "1 22.084 40.176 38.554 43.692 47.427 41.359 17.194 \n", - "2 22.380 40.244 38.990 46.699 47.468 41.199 20.530 \n", - "3 22.277 40.257 38.072 47.541 47.658 41.643 18.089 \n", - "4 21.947 39.144 41.955 47.645 47.346 41.507 18.461 \n", - "... ... ... ... ... ... ... ... \n", - "4999995 20.719 40.999 38.653 47.386 47.528 40.212 17.659 \n", - "4999996 20.263 41.579 33.624 47.536 47.647 41.199 18.741 \n", - "4999997 20.189 41.505 40.967 52.437 47.802 41.302 23.199 \n", - "4999998 20.453 40.208 40.957 47.628 48.086 40.510 15.932 \n", - "4999999 20.157 40.326 36.039 48.885 48.170 41.115 15.752 \n", - "\n", - "[5000000 rows x 55 columns]" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "cols = ['time', 'A Feed', 'D Feed', 'E Feed', 'A and C Feed', 'Recycle Flow',\n", - " 'Reactor Feed Rate', 'Reactor Pressure', 'Reactor Level',\n", - " 'Reactor Temperature', 'Purge Rate', 'Product Sep Temp',\n", - " 'Product Sep Level', 'Product Sep Pressure', 'Product Sep Underflow',\n", - " 'Stripper Level', 'Stripper Pressure', 'Stripper Underflow',\n", - " 'Stripper Temp', 'Stripper Steam Flow', 'Compressor Work',\n", - " 'Reactor Coolant Temp', 'Separator Coolant Temp',\n", - " 'Component A to Reactor', 'Component B to Reactor',\n", - " 'Component C to Reactor', 'Component D to Reactor',\n", - " 'Component E to Reactor', 'Component F to Reactor',\n", - " 'Component A in Purge', 'Component B in Purge', 'Component C in Purge',\n", - " 'Component D in Purge', 'Component E in Purge', 'Component F in Purge',\n", - " 'Component G in Purge', 'Component H in Purge',\n", - " 'Component D in Product', 'Component E in Product',\n", - " 'Component F in Product', 'Component G in Product',\n", - " 'Component H in Product', 'D feed load', 'E feed load', 'A feed load', 'A and C feed load',\n", - " 'Compressor recycle valve', 'Purge valve', 'Separator liquid load',\n", - " 'Stripper liquid load', 'Stripper steam valve', 'Reactor coolant load',\n", - " 'Condenser coolant load']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(1, 21):\n", - " fd = df.loc[(df['simulationRun']==1) & (df['faultNumber']==i)].drop(['simulationRun', 'faultNumber'], axis=1)\n", - " fd = fd.rename(columns=dict(zip(fd.columns, cols)))\n", - " fd['time'] = fd['time']*0.05\n", - " fd.to_csv(f\"./data/fault{i}.csv\", index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "tep", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/backend/mapping_adj_list.png b/backend/mapping_adj_list.png new file mode 100644 index 0000000..c205bde Binary files /dev/null and b/backend/mapping_adj_list.png differ diff --git a/backend/model.py b/backend/model.py index 5895118..0f3f796 100644 --- a/backend/model.py +++ b/backend/model.py @@ -1,9 +1,589 @@ +# # import pandas as pd +# # import numpy as np +# # from sklearn.decomposition import PCA +# # from sklearn.preprocessing import StandardScaler +# # import uuid + + +# # class FaultDetectionModel: +# # def __init__(self, n_components=0.9, alpha=0.05): +# # self.scaler = StandardScaler() +# # self.pca = PCA(n_components=n_components) +# # self.data_buffer = pd.DataFrame() +# # self.fault_callbacks = [] +# # self.alpha = alpha +# # self.t2_threshold = None +# # self.current_fault_id = None +# # self.post_fault_data_count = 0 +# # self.post_fault_threshold = ( +# # 20 # Number of data points to collect after initial fault detection +# # ) + +# # def fit(self, training_df): +# # self.feature_names = training_df.columns.to_list() +# # Z = self.scaler.fit_transform(training_df) +# # self.pca.fit(Z) +# # self.a = self.pca.n_components_ +# # self.m = self.pca.n_features_in_ +# # self.n = self.pca.n_samples_ +# # self.P = self.pca.components_.T +# # self.lamda = self.pca.explained_variance_ +# # # set thresholds +# # self.set_t2_threshold() + +# # def set_t2_threshold(self): +# # assert hasattr(self.pca, "n_samples_"), "Model hasn't been trained" +# # from scipy.stats import f + +# # scaling_factor = (self.a * (self.n - 1) * (self.n + 1)) / ( +# # self.n * (self.n - self.a) +# # ) +# # self.t2_threshold = scaling_factor * f.ppf( +# # 1 - self.alpha, self.a, self.n - self.a +# # ) + +# # def register_fault_callback(self, callback): +# # assert callable(callback) +# # self.fault_callbacks.append(callback) + +# # def calculate_t2_stat(self, data_point): +# # # Calculate T^2 statistic +# # assert isinstance(data_point, pd.DataFrame) +# # x = data_point[self.feature_names] +# # z = self.scaler.transform(x) +# # t2_stat = z @ self.P @ np.diag(self.lamda**-1) @ self.P.T @ z.T +# # return t2_stat.item() + +# # def is_anomaly(self, data_point): +# # t2_stat = self.calculate_t2_stat(data_point) +# # anomaly = t2_stat > self.t2_threshold +# # return anomaly, t2_stat + +# # def process_data_point(self, data_point): +# # anomaly, t2_stat = self.is_anomaly(data_point) +# # data_point[["T^2_stat", "Anomaly"]] = [t2_stat, anomaly] +# # self.data_buffer = pd.concat([self.data_buffer, data_point], ignore_index=True) + +# # # if anomaly: +# # # for callback in self.fault_callbacks: +# # # callback(data_point, t2_stat) +# # # return anomaly + +# # if anomaly: +# # if self.current_fault_id is None: +# # self.current_fault_id = uuid.uuid4() # Assign a new fault ID +# # self.post_fault_data_count = 0 +# # else: +# # self.post_fault_data_count += 1 +# # if self.post_fault_data_count == self.post_fault_threshold: +# # # Trigger fault handling after collecting enough data points +# # for callback in self.fault_callbacks: +# # callback( +# # {"data": data_point, "fault_id": self.current_fault_id} +# # ) +# # else: +# # if self.current_fault_id is not None: +# # # Fault has ended +# # self.current_fault_id = None +# # self.post_fault_data_count = 0 + +# # return t2_stat, anomaly + +# # def plot(self): +# # import plotly.express as px + +# # fig = px.line(self.data_buffer, y="T^2_stat", title="T^2 Statistics Over Time") +# # fig.add_hline(y=self.t2_threshold) + +# # in_fault = False +# # for i, t2_stat in enumerate(self.data_buffer["T^2_stat"]): +# # if t2_stat > self.t2_threshold and not in_fault: +# # start_fault = i +# # in_fault = True +# # elif t2_stat <= self.t2_threshold and in_fault: +# # end_fault = i +# # # Add a red shape for the fault period +# # fig.add_shape( +# # type="rect", +# # x0=start_fault, +# # y0=0, +# # x1=end_fault, +# # y1=1, +# # line=dict(width=0), +# # fillcolor="red", +# # opacity=0.2, +# # layer="below", +# # xref="x", +# # yref="paper", +# # ) +# # in_fault = False + +# # # Check if the last data point is still in a fault period +# # if in_fault: +# # fig.add_shape( +# # type="rect", +# # x0=start_fault, +# # y0=0, +# # x1=len(self.data_buffer), +# # y1=1, +# # line=dict(width=0), +# # fillcolor="red", +# # opacity=0.2, +# # layer="below", +# # xref="x", +# # yref="paper", +# # ) + +# # fig.update_layout(xaxis_title="Time", yaxis_title="T^2 Statistic") +# # return fig + +# # def t2_contrib(self, timestamp): +# # x = self.data_buffer[self.data_buffer["timestamp"] == timestamp][ +# # self.feature_names +# # ] +# # z = self.scaler.transform(x)[0] +# # # Precompute t as a vector +# # t = z.T @ self.P + +# # # Vectorized calculation of c(j, i) +# # def calculate_c(j): +# # c_ji = (t g self.lamda) * self.P[j, :] * (z[j]) # - self.scaler.mean_[j]) +# # return np.maximum(c_ji, 0) # Ensures non-negativity + +# # # Calculate C(j) for each j +# # C_list = np.array([calculate_c(j).sum() for j in range(len(z))]) + +# # return C_list + +# # def plot_t2_contributions(self, start_index, end_index=None): +# # import plotly.express as px + +# # if end_index == None: +# # end_index = start_index +# # contributions = [] +# # for index in range(start_index, end_index + 1): +# # # x_j = self.data_buffer.iloc[index].values +# # contrib = self.t2_contrib(index) +# # contributions.append(contrib) + +# # # Create DataFrame for animation +# # timestamps = self.data_buffer["timestamp"][start_index : end_index + 1] +# # # feature_names = [f'Feature {i+1}' for i in range(P.shape[1])] +# # feature_names = self.feature_names + +# # animation_df = pd.DataFrame( +# # contributions, columns=feature_names, index=timestamps +# # ).reset_index() +# # animation_df = animation_df.melt( +# # id_vars="timestamp", var_name="Feature", value_name="T2 Contribution" +# # ) + +# # # Create Animated Bar Plot +# # fig = px.bar( +# # animation_df, +# # x="Feature", +# # y="T2 Contribution", +# # animation_frame="timestamp", +# # range_y=[0, animation_df["T2 Contribution"].max()], +# # ) + +# # fig.update_layout(title_text="T^2 Contributions Over Time") +# # return fig + + +# import pandas as pd +# import numpy as np +# from sklearn.decomposition import PCA +# from sklearn.preprocessing import StandardScaler +# import uuid + + +# class FaultDetectionModel: +# def __init__(self, n_components=0.9, alpha=0.05): +# self.scaler = StandardScaler() +# self.pca = PCA(n_components=n_components) +# self.data_buffer = pd.DataFrame() +# self.fault_callbacks = [] +# self.alpha = alpha +# self.t2_threshold = None +# self.current_fault_id = None +# self.post_fault_data_count = 0 +# self.post_fault_threshold = ( +# 20 # Number of data points to collect after initial fault detection +# ) + +# def fit(self, training_df): +# self.feature_names = training_df.columns.to_list() +# Z = self.scaler.fit_transform(training_df) +# self.pca.fit(Z) +# self.a = self.pca.n_components_ +# self.m = self.pca.n_features_in_ +# self.n = self.pca.n_samples_ +# self.P = self.pca.components_.T +# self.lamda = self.pca.explained_variance_ +# # set thresholds +# self.set_t2_threshold() + +# def set_t2_threshold(self): +# assert hasattr(self.pca, "n_samples_"), "Model hasn't been trained" +# from scipy.stats import f + +# scaling_factor = (self.a * (self.n - 1) * (self.n + 1)) / ( +# self.n * (self.n - self.a) +# ) +# self.t2_threshold = scaling_factor * f.ppf( +# 1 - self.alpha, self.a, self.n - self.a +# ) + +# def register_fault_callback(self, callback): +# assert callable(callback) +# self.fault_callbacks.append(callback) + +# def calculate_t2_stat(self, data_point): +# # Calculate T^2 statistic +# assert isinstance(data_point, pd.DataFrame) +# x = data_point[self.feature_names] +# z = self.scaler.transform(x) +# t2_stat = z @ self.P @ np.diag(self.lamda**-1) @ self.P.T @ z.T +# return t2_stat.item() + +# def is_anomaly(self, data_point): +# t2_stat = self.calculate_t2_stat(data_point) +# anomaly = t2_stat > self.t2_threshold +# return anomaly, t2_stat + +# def process_data_point(self, data_point): +# anomaly, t2_stat = self.is_anomaly(data_point) +# data_point[["T^2_stat", "Anomaly"]] = [t2_stat, anomaly] +# self.data_buffer = pd.concat([self.data_buffer, data_point], ignore_index=True) + +# # if anomaly: +# # for callback in self.fault_callbacks: +# # callback(data_point, t2_stat) +# # return anomaly + +# if anomaly: +# if self.current_fault_id is None: +# self.current_fault_id = uuid.uuid4() # Assign a new fault ID +# self.post_fault_data_count = 0 +# else: +# self.post_fault_data_count += 1 +# if self.post_fault_data_count == self.post_fault_threshold: +# # Trigger fault handling after collecting enough data points +# for callback in self.fault_callbacks: +# callback( +# {"data": data_point, "fault_id": self.current_fault_id} +# ) +# else: +# if self.current_fault_id is not None: +# # Fault has ended +# self.current_fault_id = None +# self.post_fault_data_count = 0 + +# return t2_stat, anomaly + +# def plot(self): +# import plotly.express as px + +# fig = px.line(self.data_buffer, y="T^2_stat", title="T^2 Statistics Over Time") +# fig.add_hline(y=self.t2_threshold) + +# in_fault = False +# for i, t2_stat in enumerate(self.data_buffer["T^2_stat"]): +# if t2_stat > self.t2_threshold and not in_fault: +# start_fault = i +# in_fault = True +# elif t2_stat <= self.t2_threshold and in_fault: +# end_fault = i +# # Add a red shape for the fault period +# fig.add_shape( +# type="rect", +# x0=start_fault, +# y0=0, +# x1=end_fault, +# y1=1, +# line=dict(width=0), +# fillcolor="red", +# opacity=0.2, +# layer="below", +# xref="x", +# yref="paper", +# ) +# in_fault = False + +# # Check if the last data point is still in a fault period +# if in_fault: +# fig.add_shape( +# type="rect", +# x0=start_fault, +# y0=0, +# x1=len(self.data_buffer), +# y1=1, +# line=dict(width=0), +# fillcolor="red", +# opacity=0.2, +# layer="below", +# xref="x", +# yref="paper", +# ) + +# fig.update_layout(xaxis_title="Time", yaxis_title="T^2 Statistic") +# return fig + +# def t2_contrib(self, timestamp): +# x = self.data_buffer[self.data_buffer["timestamp"] == timestamp][ +# self.feature_names +# ] +# z = self.scaler.transform(x)[0] +# # Precompute t as a vector +# t = z.T @ self.P + +# # Vectorized calculation of c(j, i) +# def calculate_c(j): +# c_ji = (t / self.lamda) * self.P[j, :] * (z[j]) # - self.scaler.mean_[j]) +# return np.maximum(c_ji, 0) # Ensures non-negativity + +# # Calculate C(j) for each j +# C_list = np.array([calculate_c(j).sum() for j in range(len(z))]) + +# return C_list + +# def plot_t2_contributions(self, start_index, end_index=None): +# import plotly.express as px + +# if end_index == None: +# end_index = start_index +# contributions = [] +# for index in range(start_index, end_index + 1): +# # x_j = self.data_buffer.iloc[index].values +# contrib = self.t2_contrib(index) +# contributions.append(contrib) + +# # Create DataFrame for animation +# timestamps = self.data_buffer["timestamp"][start_index : end_index + 1] +# # feature_names = [f'Feature {i+1}' for i in range(P.shape[1])] +# feature_names = self.feature_names + +# animation_df = pd.DataFrame( +# contributions, columns=feature_names, index=timestamps +# ).reset_index() +# animation_df = animation_df.melt( +# id_vars="timestamp", var_name="Feature", value_name="T2 Contribution" +# ) + +# # Create Animated Bar Plot +# fig = px.bar( +# animation_df, +# x="Feature", +# y="T2 Contribution", +# animation_frame="timestamp", +# range_y=[0, animation_df["T2 Contribution"].max()], +# ) + +# fig.update_layout(title_text="T^2 Contributions Over Time") +# return fig + + +# import pandas as pd +# import numpy as np +# from sklearn.decomposition import PCA +# from sklearn.preprocessing import StandardScaler +# import uuid + + +# class FaultDetectionModel: +# def __init__(self, n_components=0.9, alpha=0.05): +# self.scaler = StandardScaler() +# self.pca = PCA(n_components=n_components) +# self.data_buffer = pd.DataFrame() +# self.fault_callbacks = [] +# self.alpha = alpha +# self.t2_threshold = None +# self.current_fault_id = None +# self.post_fault_data_count = 0 +# self.post_fault_threshold = ( +# 20 # Number of data points to collect after initial fault detection +# ) + +# def fit(self, training_df): +# self.feature_names = training_df.columns.to_list() +# Z = self.scaler.fit_transform(training_df) +# self.pca.fit(Z) +# self.a = self.pca.n_components_ +# self.m = self.pca.n_features_in_ +# self.n = self.pca.n_samples_ +# self.P = self.pca.components_.T +# self.lamda = self.pca.explained_variance_ +# # set thresholds +# self.set_t2_threshold() + +# def set_t2_threshold(self): +# assert hasattr(self.pca, "n_samples_"), "Model hasn't been trained" +# from scipy.stats import f + +# scaling_factor = (self.a * (self.n - 1) * (self.n + 1)) / ( +# self.n * (self.n - self.a) +# ) +# self.t2_threshold = scaling_factor * f.ppf( +# 1 - self.alpha, self.a, self.n - self.a +# ) + +# def register_fault_callback(self, callback): +# assert callable(callback) +# self.fault_callbacks.append(callback) + +# def calculate_t2_stat(self, data_point): +# # Calculate T^2 statistic +# assert isinstance(data_point, pd.DataFrame) +# x = data_point[self.feature_names] +# z = self.scaler.transform(x) +# t2_stat = z @ self.P @ np.diag(self.lamda**-1) @ self.P.T @ z.T +# return t2_stat.item() + +# def is_anomaly(self, data_point): +# t2_stat = self.calculate_t2_stat(data_point) +# anomaly = t2_stat > self.t2_threshold +# return anomaly, t2_stat + +# def process_data_point(self, data_point): +# anomaly, t2_stat = self.is_anomaly(data_point) +# data_point[["T^2_stat", "Anomaly"]] = [t2_stat, anomaly] +# self.data_buffer = pd.concat([self.data_buffer, data_point], ignore_index=True) + +# # if anomaly: +# # for callback in self.fault_callbacks: +# # callback(data_point, t2_stat) +# # return anomaly + +# if anomaly: +# if self.current_fault_id is None: +# self.current_fault_id = uuid.uuid4() # Assign a new fault ID +# self.post_fault_data_count = 0 +# else: +# self.post_fault_data_count += 1 +# if self.post_fault_data_count == self.post_fault_threshold: +# # Trigger fault handling after collecting enough data points +# for callback in self.fault_callbacks: +# callback( +# {"data": data_point, "fault_id": self.current_fault_id} +# ) +# else: +# if self.current_fault_id is not None: +# # Fault has ended +# self.current_fault_id = None +# self.post_fault_data_count = 0 + +# return t2_stat, anomaly + +# def plot(self): +# import plotly.express as px + +# fig = px.line(self.data_buffer, y="T^2_stat", title="T^2 Statistics Over Time") +# fig.add_hline(y=self.t2_threshold) + +# in_fault = False +# for i, t2_stat in enumerate(self.data_buffer["T^2_stat"]): +# if t2_stat > self.t2_threshold and not in_fault: +# start_fault = i +# in_fault = True +# elif t2_stat <= self.t2_threshold and in_fault: +# end_fault = i +# # Add a red shape for the fault period +# fig.add_shape( +# type="rect", +# x0=start_fault, +# y0=0, +# x1=end_fault, +# y1=1, +# line=dict(width=0), +# fillcolor="red", +# opacity=0.2, +# layer="below", +# xref="x", +# yref="paper", +# ) +# in_fault = False + +# # Check if the last data point is still in a fault period +# if in_fault: +# fig.add_shape( +# type="rect", +# x0=start_fault, +# y0=0, +# x1=len(self.data_buffer), +# y1=1, +# line=dict(width=0), +# fillcolor="red", +# opacity=0.2, +# layer="below", +# xref="x", +# yref="paper", +# ) + +# fig.update_layout(xaxis_title="Time", yaxis_title="T^2 Statistic") +# return fig + +# def t2_contrib(self, timestamp): +# x = self.data_buffer[self.data_buffer["timestamp"] == timestamp][ +# self.feature_names +# ] +# z = self.scaler.transform(x)[0] +# # Precompute t as a vector +# t = z.T @ self.P + +# # Vectorized calculation of c(j, i) +# def calculate_c(j): +# c_ji = (t g self.lamda) * self.P[j, :] * (z[j]) # - self.scaler.mean_[j]) +# return np.maximum(c_ji, 0) # Ensures non-negativity + +# # Calculate C(j) for each j +# C_list = np.array([calculate_c(j).sum() for j in range(len(z))]) + +# return C_list + +# def plot_t2_contributions(self, start_index, end_index=None): +# import plotly.express as px + +# if end_index == None: +# end_index = start_index +# contributions = [] +# for index in range(start_index, end_index + 1): +# # x_j = self.data_buffer.iloc[index].values +# contrib = self.t2_contrib(index) +# contributions.append(contrib) + +# # Create DataFrame for animation +# timestamps = self.data_buffer["timestamp"][start_index : end_index + 1] +# # feature_names = [f'Feature {i+1}' for i in range(P.shape[1])] +# feature_names = self.feature_names + +# animation_df = pd.DataFrame( +# contributions, columns=feature_names, index=timestamps +# ).reset_index() +# animation_df = animation_df.melt( +# id_vars="timestamp", var_name="Feature", value_name="T2 Contribution" +# ) + +# # Create Animated Bar Plot +# fig = px.bar( +# animation_df, +# x="Feature", +# y="T2 Contribution", +# animation_frame="timestamp", +# range_y=[0, animation_df["T2 Contribution"].max()], +# ) + +# fig.update_layout(title_text="T^2 Contributions Over Time") +# return fig + + import pandas as pd import numpy as np from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler import uuid + class FaultDetectionModel: def __init__(self, n_components=0.9, alpha=0.05): self.scaler = StandardScaler() @@ -14,8 +594,9 @@ def __init__(self, n_components=0.9, alpha=0.05): self.t2_threshold = None self.current_fault_id = None self.post_fault_data_count = 0 - self.post_fault_threshold = 20 # Number of data points to collect after initial fault detection - + self.post_fault_threshold = ( + 20 # Number of data points to collect after initial fault detection + ) def fit(self, training_df): self.feature_names = training_df.columns.to_list() @@ -30,10 +611,15 @@ def fit(self, training_df): self.set_t2_threshold() def set_t2_threshold(self): - assert hasattr(self.pca, 'n_samples_'), "Model hasn't been trained" + assert hasattr(self.pca, "n_samples_"), "Model hasn't been trained" from scipy.stats import f - scaling_factor = (self.a*(self.n-1)*(self.n+1))/(self.n*(self.n - self.a)) - self.t2_threshold = scaling_factor*f.ppf(1-self.alpha, self.a, self.n-self.a) + + scaling_factor = (self.a * (self.n - 1) * (self.n + 1)) / ( + self.n * (self.n - self.a) + ) + self.t2_threshold = scaling_factor * f.ppf( + 1 - self.alpha, self.a, self.n - self.a + ) def register_fault_callback(self, callback): assert callable(callback) @@ -54,7 +640,7 @@ def is_anomaly(self, data_point): def process_data_point(self, data_point): anomaly, t2_stat = self.is_anomaly(data_point) - data_point[['T^2_stat', 'Anomaly']] = [t2_stat, anomaly] + data_point[["T^2_stat", "Anomaly"]] = [t2_stat, anomaly] self.data_buffer = pd.concat([self.data_buffer, data_point], ignore_index=True) # if anomaly: @@ -71,7 +657,9 @@ def process_data_point(self, data_point): if self.post_fault_data_count == self.post_fault_threshold: # Trigger fault handling after collecting enough data points for callback in self.fault_callbacks: - callback({"data": data_point, "fault_id": self.current_fault_id}) + callback( + {"data": data_point, "fault_id": self.current_fault_id} + ) else: if self.current_fault_id is not None: # Fault has ended @@ -82,46 +670,63 @@ def process_data_point(self, data_point): def plot(self): import plotly.express as px - fig = px.line(self.data_buffer, y='T^2_stat', title='T^2 Statistics Over Time') + + fig = px.line(self.data_buffer, y="T^2_stat", title="T^2 Statistics Over Time") fig.add_hline(y=self.t2_threshold) in_fault = False - for i, t2_stat in enumerate(self.data_buffer['T^2_stat']): + for i, t2_stat in enumerate(self.data_buffer["T^2_stat"]): if t2_stat > self.t2_threshold and not in_fault: start_fault = i in_fault = True elif t2_stat <= self.t2_threshold and in_fault: end_fault = i # Add a red shape for the fault period - fig.add_shape(type="rect", - x0=start_fault, y0=0, x1=end_fault, y1=1, - line=dict(width=0), - fillcolor="red", opacity=0.2, - layer="below", xref="x", yref="paper") + fig.add_shape( + type="rect", + x0=start_fault, + y0=0, + x1=end_fault, + y1=1, + line=dict(width=0), + fillcolor="red", + opacity=0.2, + layer="below", + xref="x", + yref="paper", + ) in_fault = False # Check if the last data point is still in a fault period if in_fault: - fig.add_shape(type="rect", - x0=start_fault, y0=0, x1=len(self.data_buffer), y1=1, - line=dict(width=0), - fillcolor="red", opacity=0.2, - layer="below", xref="x", yref="paper") - + fig.add_shape( + type="rect", + x0=start_fault, + y0=0, + x1=len(self.data_buffer), + y1=1, + line=dict(width=0), + fillcolor="red", + opacity=0.2, + layer="below", + xref="x", + yref="paper", + ) - - fig.update_layout(xaxis_title='Time', yaxis_title='T^2 Statistic') + fig.update_layout(xaxis_title="Time", yaxis_title="T^2 Statistic") return fig def t2_contrib(self, timestamp): - x = self.data_buffer[self.data_buffer['timestamp']==timestamp][self.feature_names] + x = self.data_buffer[self.data_buffer["timestamp"] == timestamp][ + self.feature_names + ] z = self.scaler.transform(x)[0] # Precompute t as a vector t = z.T @ self.P # Vectorized calculation of c(j, i) def calculate_c(j): - c_ji = (t / self.lamda) * self.P[j, :] * (z[j]) # - self.scaler.mean_[j]) + c_ji = (t / self.lamda) * self.P[j, :] * (z[j]) # - self.scaler.mean_[j]) return np.maximum(c_ji, 0) # Ensures non-negativity # Calculate C(j) for each j @@ -141,20 +746,25 @@ def plot_t2_contributions(self, start_index, end_index=None): contributions.append(contrib) # Create DataFrame for animation - timestamps = self.data_buffer['timestamp'][start_index:end_index + 1] + timestamps = self.data_buffer["timestamp"][start_index : end_index + 1] # feature_names = [f'Feature {i+1}' for i in range(P.shape[1])] feature_names = self.feature_names - animation_df = pd.DataFrame(contributions, columns=feature_names, index=timestamps).reset_index() - animation_df = animation_df.melt(id_vars='timestamp', var_name='Feature', value_name='T2 Contribution') + animation_df = pd.DataFrame( + contributions, columns=feature_names, index=timestamps + ).reset_index() + animation_df = animation_df.melt( + id_vars="timestamp", var_name="Feature", value_name="T2 Contribution" + ) # Create Animated Bar Plot - fig = px.bar(animation_df, - x='Feature', - y='T2 Contribution', - animation_frame='timestamp', - range_y=[0, animation_df['T2 Contribution'].max()]) + fig = px.bar( + animation_df, + x="Feature", + y="T2 Contribution", + animation_frame="timestamp", + range_y=[0, animation_df["T2 Contribution"].max()], + ) - fig.update_layout(title_text='T^2 Contributions Over Time') + fig.update_layout(title_text="T^2 Contributions Over Time") return fig - diff --git a/backend/requirements.txt b/backend/requirements.txt index 3eaa4f7..35945be 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -1,151 +1,141 @@ -aiohttp==3.9.1 -aiosignal==1.3.1 -annotated-types==0.6.0 -anyio==3.7.1 +annotated-types==0.7.0 +anyio==4.4.0 argon2-cffi==23.1.0 argon2-cffi-bindings==21.2.0 arrow==1.3.0 asttokens==2.4.1 async-lru==2.0.4 -attrs==23.1.0 -Babel==2.13.1 -beautifulsoup4==4.12.2 -bidict==0.22.1 +attrs==23.2.0 +Babel==2.15.0 +beautifulsoup4==4.12.3 bleach==6.1.0 -blinker==1.7.0 -certifi==2023.11.17 +certifi==2024.6.2 cffi==1.16.0 charset-normalizer==3.3.2 click==8.1.7 -comm==0.2.0 -contourpy==1.2.0 +comm==0.2.2 +contourpy==1.2.1 cycler==0.12.1 -dataclasses-json==0.6.3 -debugpy==1.8.0 +debugpy==1.8.1 decorator==5.1.1 defusedxml==0.7.1 -distro==1.8.0 +distro==1.9.0 +dnspython==2.6.1 +email_validator==2.1.1 executing==2.0.1 -fastjsonschema==2.19.0 -Flask==3.0.0 -Flask-Cors==4.0.0 -Flask-SocketIO==5.3.6 -fonttools==4.45.1 +fastapi==0.111.0 +fastapi-cli==0.0.4 +fastjsonschema==2.20.0 +fonttools==4.53.0 fqdn==1.5.1 -frozenlist==1.4.0 -greenlet==3.0.1 h11==0.14.0 -httpcore==1.0.2 -httpx==0.25.2 -idna==3.5 -ipykernel==6.26.0 -ipython==8.18.0 -ipywidgets==8.1.1 +httpcore==1.0.5 +httptools==0.6.1 +httpx==0.27.0 +idna==3.7 +ipykernel==6.29.4 +ipython==8.25.0 +ipywidgets==8.1.3 isoduration==20.11.0 -itsdangerous==2.1.2 jedi==0.19.1 -Jinja2==3.1.2 -joblib==1.3.2 -json5==0.9.14 -jsonpatch==1.33 -jsonpointer==2.4 -jsonschema==4.20.0 -jsonschema-specifications==2023.11.1 +Jinja2==3.1.4 +joblib==1.4.2 +json5==0.9.25 +jsonpointer==3.0.0 +jsonschema==4.22.0 +jsonschema-specifications==2023.12.1 jupyter==1.0.0 jupyter-console==6.6.3 -jupyter-events==0.9.0 -jupyter-lsp==2.2.0 -jupyter_client==8.6.0 -jupyter_core==5.5.0 -jupyter_server==2.10.1 -jupyter_server_terminals==0.4.4 -jupyterlab==4.0.9 -jupyterlab-widgets==3.0.9 +jupyter-events==0.10.0 +jupyter-lsp==2.2.5 +jupyter_client==8.6.2 +jupyter_core==5.7.2 +jupyter_server==2.14.1 +jupyter_server_terminals==0.5.3 +jupyterlab==4.2.2 jupyterlab_pygments==0.3.0 -jupyterlab_server==2.25.2 +jupyterlab_server==2.27.2 +jupyterlab_widgets==3.0.11 kiwisolver==1.4.5 -langchain==0.0.342 -langchain-core==0.0.7 -langsmith==0.0.67 -MarkupSafe==2.1.3 -marshmallow==3.20.1 -matplotlib==3.8.2 -matplotlib-inline==0.1.6 +markdown-it-py==3.0.0 +MarkupSafe==2.1.5 +matplotlib==3.9.0 +matplotlib-inline==0.1.7 +mdurl==0.1.2 mistune==3.0.2 -multidict==6.0.4 -mypy-extensions==1.0.0 -nbclient==0.9.0 -nbconvert==7.11.0 -nbformat==5.9.2 -nest-asyncio==1.5.8 -notebook==7.0.6 -notebook_shim==0.2.3 -numpy==1.26.2 -openai==1.3.6 -overrides==7.4.0 -packaging==23.2 -pandas==2.1.3 -pandocfilters==1.5.0 -parso==0.8.3 -pexpect==4.8.0 -Pillow==10.1.0 -platformdirs==4.0.0 -plotly==5.18.0 -prometheus-client==0.19.0 -prompt-toolkit==3.0.41 -psutil==5.9.6 +nbclient==0.10.0 +nbconvert==7.16.4 +nbformat==5.10.4 +nest-asyncio==1.6.0 +networkx==3.3 +notebook==7.2.1 +notebook_shim==0.2.4 +numpy==2.0.0 +openai==1.34.0 +orjson==3.10.5 +overrides==7.7.0 +packaging==24.1 +pandas==2.2.2 +pandocfilters==1.5.1 +parso==0.8.4 +pexpect==4.9.0 +pillow==10.3.0 +platformdirs==4.2.2 +plotly==5.22.0 +prometheus_client==0.20.0 +prompt_toolkit==3.0.47 +psutil==6.0.0 ptyprocess==0.7.0 pure-eval==0.2.2 -pycparser==2.21 -pydantic==2.5.2 -pydantic_core==2.14.5 -Pygments==2.17.2 -pyparsing==3.1.1 -pyreadr==0.5.0 -python-dateutil==2.8.2 -python-engineio==4.8.0 +pycparser==2.22 +pydantic==2.7.4 +pydantic_core==2.18.4 +Pygments==2.18.0 +pyparsing==3.1.2 +python-dateutil==2.9.0.post0 +python-dotenv==1.0.1 python-json-logger==2.0.7 -python-socketio==5.10.0 -pytz==2023.3.post1 +python-multipart==0.0.9 +pytz==2024.1 PyYAML==6.0.1 -pyzmq==25.1.1 -qtconsole==5.5.1 +pyzmq==26.0.3 +qtconsole==5.5.2 QtPy==2.4.1 -redis==5.0.1 -referencing==0.31.0 -requests==2.31.0 +referencing==0.35.1 +requests==2.32.3 rfc3339-validator==0.1.4 rfc3986-validator==0.1.1 -rpds-py==0.13.1 -scikit-learn==1.3.2 -scipy==1.11.4 -Send2Trash==1.8.2 -setuptools==68.0.0 -simple-websocket==1.0.0 +rich==13.7.1 +rpds-py==0.18.1 +scikit-learn==1.5.0 +scipy==1.13.1 +Send2Trash==1.8.3 +shellingham==1.5.4 six==1.16.0 -sniffio==1.3.0 +sniffio==1.3.1 soupsieve==2.5 -SQLAlchemy==2.0.23 stack-data==0.6.3 -tenacity==8.2.3 -terminado==0.18.0 -threadpoolctl==3.2.0 -tinycss2==1.2.1 -tornado==6.3.3 -tqdm==4.66.1 -traitlets==5.13.0 -types-python-dateutil==2.8.19.14 -typing-inspect==0.9.0 -typing_extensions==4.8.0 -tzdata==2023.3 +starlette==0.37.2 +tenacity==8.4.1 +terminado==0.18.1 +threadpoolctl==3.5.0 +tinycss2==1.3.0 +tornado==6.4.1 +tqdm==4.66.4 +traitlets==5.14.3 +typer==0.12.3 +types-python-dateutil==2.9.0.20240316 +typing_extensions==4.12.2 +tzdata==2024.1 +ujson==5.10.0 uri-template==1.3.0 -urllib3==2.1.0 -wcwidth==0.2.12 -webcolors==1.13 +urllib3==2.2.2 +uvicorn==0.30.1 +uvloop==0.19.0 +watchfiles==0.22.0 +wcwidth==0.2.13 +webcolors==24.6.0 webencodings==0.5.1 -websocket-client==1.6.4 -Werkzeug==3.0.1 -wheel==0.41.2 -widgetsnbextension==4.0.9 -wsproto==1.2.0 -yarl==1.9.3 +websocket-client==1.8.0 +websockets==12.0 +widgetsnbextension==4.0.11 diff --git a/backend/simulator.py b/backend/simulator.py deleted file mode 100644 index 1f4995d..0000000 --- a/backend/simulator.py +++ /dev/null @@ -1,83 +0,0 @@ -import threading -import time -from abc import ABC, abstractmethod - -class BaseSimulator(ABC): - def __init__(self): - self.running = False - self.paused = False - self.rate = 1.0 # Default rate of data generation - self._stop_event = threading.Event() - self._thread = None - - @abstractmethod - def step(self): - """ - Abstract method to define the behavior of taking a single step - in the simulation. This method should be implemented by subclasses. - """ - pass - - @abstractmethod - def induce_fault(self): - """ - Induces a fault in the simulation process. - This will affect the output of the step method. - """ - pass - - def run(self): - """ - Main method to run in a separate thread. - Handles the simulation based on the current state. - """ - self.running = True - self._stop_event.clear() - - while not self._stop_event.is_set(): - if not self.paused: - self.step() - time.sleep(1 / self.rate) # Adjust sleep based on the rate - - def start(self): - """ - Starts the simulation in a separate thread. - """ - if self._thread is None or not self._thread.is_alive(): - self._thread = threading.Thread(target=self.run) - self._thread.start() - - def stop(self): - """ - Stops the simulation. - """ - self.running = False - self._stop_event.set() - if self._thread: - self._thread.join() - - def pause(self): - """ - Pauses the simulation. - """ - self.paused = True - - def resume(self): - """ - Resumes the simulation. - """ - self.paused = False - - def reset(self): - """ - Resets the simulator to its initial state. - This includes clearing the fault state. - """ - self.stop() - self.start() - - def change_rate(self, new_rate): - """ - Changes the rate of the simulation. - """ - self.rate = new_rate diff --git a/frontend/.eslintrc.cjs b/frontend/.eslintrc.cjs index d6c9537..227d154 100644 --- a/frontend/.eslintrc.cjs +++ b/frontend/.eslintrc.cjs @@ -2,17 +2,18 @@ module.exports = { root: true, env: { browser: true, es2020: true }, extends: [ - 'eslint:recommended', - 'plugin:@typescript-eslint/recommended', - 'plugin:react-hooks/recommended', + "eslint:recommended", + "plugin:@typescript-eslint/recommended", + "plugin:react-hooks/recommended", + "prettier", ], - ignorePatterns: ['dist', '.eslintrc.cjs'], - parser: '@typescript-eslint/parser', - plugins: ['react-refresh'], + ignorePatterns: ["dist", ".eslintrc.cjs"], + parser: "@typescript-eslint/parser", + plugins: ["react-refresh"], rules: { - 'react-refresh/only-export-components': [ - 'warn', + "react-refresh/only-export-components": [ + "warn", { allowConstantExport: true }, ], }, -} +}; diff --git a/frontend/.gitignore b/frontend/.gitignore index a547bf3..1cac559 100644 --- a/frontend/.gitignore +++ b/frontend/.gitignore @@ -22,3 +22,4 @@ dist-ssr *.njsproj *.sln *.sw? +.env \ No newline at end of file diff --git a/frontend/README.md b/frontend/README.md index 0d6babe..bb15685 100644 --- a/frontend/README.md +++ b/frontend/README.md @@ -17,12 +17,12 @@ If you are developing a production application, we recommend updating the config export default { // other rules... parserOptions: { - ecmaVersion: 'latest', - sourceType: 'module', - project: ['./tsconfig.json', './tsconfig.node.json'], + ecmaVersion: "latest", + sourceType: "module", + project: ["./tsconfig.json", "./tsconfig.node.json"], tsconfigRootDir: __dirname, }, -} +}; ``` - Replace `plugin:@typescript-eslint/recommended` to `plugin:@typescript-eslint/recommended-type-checked` or `plugin:@typescript-eslint/strict-type-checked` diff --git a/backend/data/fault0.csv b/frontend/data/fault0.csv similarity index 100% rename from backend/data/fault0.csv rename to frontend/data/fault0.csv diff --git a/backend/data/fault1.csv b/frontend/data/fault1.csv similarity index 100% rename from backend/data/fault1.csv rename to frontend/data/fault1.csv diff --git a/backend/data/fault10.csv b/frontend/data/fault10.csv similarity index 100% rename from backend/data/fault10.csv rename to frontend/data/fault10.csv diff --git a/backend/data/fault11.csv b/frontend/data/fault11.csv similarity index 100% rename from backend/data/fault11.csv rename to frontend/data/fault11.csv diff --git a/backend/data/fault12.csv b/frontend/data/fault12.csv similarity index 100% rename from backend/data/fault12.csv rename to frontend/data/fault12.csv diff --git a/backend/data/fault13.csv b/frontend/data/fault13.csv similarity index 100% rename from backend/data/fault13.csv rename to frontend/data/fault13.csv diff --git a/backend/data/fault14.csv b/frontend/data/fault14.csv similarity index 100% rename from backend/data/fault14.csv rename to frontend/data/fault14.csv diff --git a/backend/data/fault15.csv b/frontend/data/fault15.csv similarity index 100% rename from backend/data/fault15.csv rename to frontend/data/fault15.csv diff --git a/backend/data/fault16.csv b/frontend/data/fault16.csv similarity index 100% rename from backend/data/fault16.csv rename to frontend/data/fault16.csv diff --git a/backend/data/fault17.csv b/frontend/data/fault17.csv similarity index 100% rename from backend/data/fault17.csv rename to frontend/data/fault17.csv diff --git a/backend/data/fault18.csv b/frontend/data/fault18.csv similarity index 100% rename from backend/data/fault18.csv rename to frontend/data/fault18.csv diff --git a/backend/data/fault19.csv b/frontend/data/fault19.csv similarity index 100% rename from backend/data/fault19.csv rename to frontend/data/fault19.csv diff --git a/backend/data/fault2.csv b/frontend/data/fault2.csv similarity index 100% rename from backend/data/fault2.csv rename to frontend/data/fault2.csv diff --git a/backend/data/fault20.csv b/frontend/data/fault20.csv similarity index 100% rename from backend/data/fault20.csv rename to frontend/data/fault20.csv diff --git a/backend/data/fault3.csv b/frontend/data/fault3.csv similarity index 100% rename from backend/data/fault3.csv rename to frontend/data/fault3.csv diff --git a/backend/data/fault4.csv b/frontend/data/fault4.csv similarity index 100% rename from backend/data/fault4.csv rename to frontend/data/fault4.csv diff --git a/backend/data/fault5.csv b/frontend/data/fault5.csv similarity index 100% rename from backend/data/fault5.csv rename to frontend/data/fault5.csv diff --git a/backend/data/fault6.csv b/frontend/data/fault6.csv similarity index 100% rename from backend/data/fault6.csv rename to frontend/data/fault6.csv diff --git a/backend/data/fault7.csv b/frontend/data/fault7.csv similarity index 100% rename from backend/data/fault7.csv rename to frontend/data/fault7.csv diff --git a/backend/data/fault8.csv b/frontend/data/fault8.csv similarity index 100% rename from backend/data/fault8.csv rename to frontend/data/fault8.csv diff --git a/backend/data/fault9.csv b/frontend/data/fault9.csv similarity index 100% rename from backend/data/fault9.csv rename to frontend/data/fault9.csv diff --git a/frontend/index.html b/frontend/index.html index 70c6f27..e4b78ea 100644 --- a/frontend/index.html +++ b/frontend/index.html @@ -1,16 +1,13 @@ - - - - - - TEP-LLM demo - - - -
- - - - \ No newline at end of file + + + + + Vite + React + TS + + +
+ + + diff --git a/frontend/package.json b/frontend/package.json index d9411aa..d3737d4 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -1,5 +1,5 @@ { - "name": "tep_llm_ui", + "name": "frontend", "private": true, "version": "0.0.0", "type": "module", @@ -10,35 +10,35 @@ "preview": "vite preview" }, "dependencies": { - "@mantine/core": "^7.2.2", - "@mantine/hooks": "^7.2.2", - "@mantine/notifications": "^7.2.2", - "@mantine/tiptap": "^7.2.2", - "@tabler/icons-react": "^2.42.0", - "@tiptap/extension-link": "^2.1.13", - "@tiptap/react": "^2.1.13", - "@tiptap/starter-kit": "^2.1.13", - "dotenv": "^16.3.1", + "@mantine/charts": "^7.11.2", + "@mantine/core": "^7.11.2", + "@mantine/hooks": "^7.11.2", + "@microsoft/fetch-event-source": "^2.0.1", + "@tabler/icons-react": "^3.6.0", + "marked": "^13.0.0", + "papaparse": "^5.4.1", "react": "^18.2.0", "react-dom": "^18.2.0", - "react-markdown": "^9.0.1", - "react-router-dom": "^6.20.1", - "recharts": "^2.10.3", - "socket.io-client": "^4.7.2" + "react-router-dom": "^6.23.1", + "recharts": "^2.13.0-alpha.4" }, "devDependencies": { - "@types/react": "^18.2.37", - "@types/react-dom": "^18.2.15", - "@typescript-eslint/eslint-plugin": "^6.10.0", - "@typescript-eslint/parser": "^6.10.0", + "@types/papaparse": "^5.3.14", + "@types/react": "^18.2.66", + "@types/react-dom": "^18.2.22", + "@typescript-eslint/eslint-plugin": "^7.2.0", + "@typescript-eslint/parser": "^7.2.0", "@vitejs/plugin-react-swc": "^3.5.0", - "eslint": "^8.53.0", + "eslint": "^8.57.0", + "eslint-config-prettier": "^9.1.0", "eslint-plugin-react-hooks": "^4.6.0", - "eslint-plugin-react-refresh": "^0.4.4", - "postcss": "^8.4.32", - "postcss-preset-mantine": "^1.11.0", + "eslint-plugin-react-refresh": "^0.4.6", + "postcss": "^8.4.38", + "postcss-preset-mantine": "^1.17.0", "postcss-simple-vars": "^7.0.1", + "prettier": "3.3.3", "typescript": "^5.2.2", - "vite": "^5.0.0" - } + "vite": "^5.2.0" + }, + "packageManager": "yarn@1.22.22+sha512.a6b2f7906b721bba3d67d4aff083df04dad64c399707841b7acf00f6b133b7ac24255f2652fa22ae3534329dc6180534e98d17432037ff6fd140556e2bb3137e" } diff --git a/frontend/postcss.config.cjs b/frontend/postcss.config.cjs index 13ed8b0..e817f56 100644 --- a/frontend/postcss.config.cjs +++ b/frontend/postcss.config.cjs @@ -1,14 +1,14 @@ module.exports = { - plugins: { - 'postcss-preset-mantine': {}, - 'postcss-simple-vars': { - variables: { - 'mantine-breakpoint-xs': '36em', - 'mantine-breakpoint-sm': '48em', - 'mantine-breakpoint-md': '62em', - 'mantine-breakpoint-lg': '75em', - 'mantine-breakpoint-xl': '88em', - }, - }, + plugins: { + "postcss-preset-mantine": {}, + "postcss-simple-vars": { + variables: { + "mantine-breakpoint-xs": "36em", + "mantine-breakpoint-sm": "48em", + "mantine-breakpoint-md": "62em", + "mantine-breakpoint-lg": "75em", + "mantine-breakpoint-xl": "88em", + }, }, -}; \ No newline at end of file + }, +}; diff --git a/frontend/public/fault0.csv b/frontend/public/fault0.csv new file mode 100644 index 0000000..581286b --- /dev/null +++ b/frontend/public/fault0.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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0000000..1bda683 --- /dev/null +++ b/frontend/public/fault1.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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/dev/null +++ b/frontend/public/fault10.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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0000000..6d001f4 --- /dev/null +++ b/frontend/public/fault11.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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0000000..7aecc10 --- /dev/null +++ b/frontend/public/fault12.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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+++ b/frontend/public/fault13.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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+++ b/frontend/public/fault14.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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--- /dev/null +++ b/frontend/public/fault15.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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0000000..3f4ee5c --- /dev/null +++ b/frontend/public/fault16.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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0000000..ddcf6e9 --- /dev/null +++ b/frontend/public/fault17.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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/dev/null +++ b/frontend/public/fault18.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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b/frontend/public/fault19.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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--- /dev/null +++ b/frontend/public/fault2.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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+++ b/frontend/public/fault20.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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/dev/null +++ b/frontend/public/fault3.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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0000000..61a96b8 --- /dev/null +++ b/frontend/public/fault4.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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--- /dev/null +++ b/frontend/public/fault5.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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0000000..efb628f --- /dev/null +++ b/frontend/public/fault6.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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b/frontend/public/fault7.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant 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0000000..534cfab --- /dev/null +++ b/frontend/public/fault8.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor 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+++ b/frontend/public/fault9.csv @@ -0,0 +1,501 @@ +time,A Feed,D Feed,E Feed,A and C Feed,Recycle Flow,Reactor Feed Rate,Reactor Pressure,Reactor Level,Reactor Temperature,Purge Rate,Product Sep Temp,Product Sep Level,Product Sep Pressure,Product Sep Underflow,Stripper Level,Stripper Pressure,Stripper Underflow,Stripper Temp,Stripper Steam Flow,Compressor Work,Reactor Coolant Temp,Separator Coolant Temp,D feed load,E feed load,A feed load,A and C feed load,Compressor recycle valve,Purge valve,Separator liquid load,Stripper liquid load,Stripper steam valve,Reactor coolant load,Condenser coolant load,t2_A Feed,t2_D Feed,t2_E Feed,t2_A and C Feed,t2_Recycle Flow,t2_Reactor Feed Rate,t2_Reactor Pressure,t2_Reactor Level,t2_Reactor Temperature,t2_Purge Rate,t2_Product Sep Temp,t2_Product Sep Level,t2_Product Sep Pressure,t2_Product Sep Underflow,t2_Stripper Level,t2_Stripper Pressure,t2_Stripper Underflow,t2_Stripper Temp,t2_Stripper Steam Flow,t2_Compressor Work,t2_Reactor Coolant Temp,t2_Separator Coolant Temp,t2_D feed load,t2_E feed load,t2_A feed load,t2_A and C feed load,t2_Compressor recycle valve,t2_Purge valve,t2_Separator liquid load,t2_Stripper liquid load,t2_Stripper steam valve,t2_Reactor coolant load,t2_Condenser coolant load,anomaly,t2_stat 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0000000..e7b8dfb --- /dev/null +++ b/frontend/public/vite.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx index 2e40d3a..1db5103 100644 --- a/frontend/src/App.tsx +++ b/frontend/src/App.tsx @@ -1,30 +1,601 @@ -import '@mantine/core/styles.css'; -import { BrowserRouter as Router, Routes, Route } from 'react-router-dom'; -import { ChatPage } from './pages/ChatPage'; -import { PlotPage } from './pages/PlotPage'; -import { FaultReports } from './pages/FaultReports'; -import { Layout } from './Layout'; -import { ChatProvider } from './ChatContext'; -import { PlotProvider } from './PlotContext'; -import { ReportProvider } from './ReportContext'; - - -export function App() { +import React, { useState, useEffect, createContext, useContext } from "react"; +import { Outlet, Link, useLocation } from "react-router-dom"; +import Papa from "papaparse"; +import { + ActionIcon, + AppShell, + Box, + Burger, + Group, + NavLink, + Select, + Slider, + Text, +} from "@mantine/core"; +import { useDisclosure } from "@mantine/hooks"; +import intro_image from "./assets/intro.json"; +import { + IconPlayerPauseFilled, + IconPlayerPlayFilled, + IconChartHistogram, + IconRobot, + IconReport, +} from "@tabler/icons-react"; +import { fetchEventSource } from "@microsoft/fetch-event-source"; + +// CONSTANTS +const fileId2fileName = [ + "./fault0.csv", + "./fault1.csv", + "./fault2.csv", + "./fault3.csv", + "./fault4.csv", + "./fault5.csv", + "./fault6.csv", + "./fault7.csv", + "./fault8.csv", + "./fault9.csv", + "./fault10.csv", + "./fault11.csv", + "./fault12.csv", + "./fault13.csv", + "./fault14.csv", + "./fault15.csv", + "./fault16.csv", + "./fault17.csv", + "./fault18.csv", + "./fault19.csv", + "./fault20.csv", +]; + +const fault_name = [ + "Normal Operation", + "Step change in A/C feed ratio, B composition constant (stream 4)", + "Step change in B composition, A/C ratio constant (stream 4)", + "Step change in D feed temperature (stream 2)", + "Step change in Reactor cooling water inlet temperature", + "Step change in Condenser cooling water inlet temperature", + "Step change in A feed loss (stream 1)", + "Step change in C header pressure loss-reduced availability (stream 4)", + "Random variation in A, B, C feed composition (stream 4)", + "Random variation in D feed temperature (stream 2)", + "Random variation in C feed temperature (stream 4)", + "Random variation in Reactor cooling water inlet temperature", + "Random variation in Condenser cooling water inlet temperature", + "Slow drift in Reaction kinetics", + "Sticking Reactor cooling water valve", + "Sticking Condenser cooling water valve", + "Unknown (16)", + "Unknown (17)", + "Unknown (18)", + "Unknown (19)", + "Unknown (20)", + // 'Constant position for the valve for stream 4', +]; + +// eslint-disable-next-line react-refresh/only-export-components +export const columnFilter: string[] = [ + "A Feed", + "D Feed", + "E Feed", + "A and C Feed", + "Recycle Flow", + "Reactor Feed Rate", + "Reactor Pressure", + "Reactor Level", + "Reactor Temperature", + "Purge Rate", + "Product Sep Temp", + "Product Sep Level", + "Product Sep Pressure", + "Product Sep Underflow", + "Stripper Level", + "Stripper Pressure", + "Stripper Underflow", + "Stripper Temp", + "Stripper Steam Flow", + "Compressor Work", + "Reactor Coolant Temp", + "Separator Coolant Temp", + "D feed load", + "E feed load", + "A feed load", + "A and C feed load", + "Compressor recycle valve", + "Purge valve", + "Separator liquid load", + "Stripper liquid load", + "Stripper steam valve", + "Reactor coolant load", + "Condenser coolant load", +]; + +// eslint-disable-next-line react-refresh/only-export-components +export const columnFilterUnits: Record = { + "A Feed": "kscmh", + "D Feed": "kg/hr", + "E Feed": "kg/hr", + "A and C Feed": "kscmh", + "Recycle Flow": "kscmh", + "Reactor Feed Rate": "kscmh", + "Reactor Pressure": "kPa gauge", + "Reactor Level": "%", + "Reactor Temperature": "Deg C", + "Purge Rate": "kscmh", + "Product Sep Temp": "Deg C", + "Product Sep Level": "%", + "Product Sep Pressure": "kPa gauge", + "Product Sep Underflow": "m3/hr", + "Stripper Level": "%", + "Stripper Pressure": "kPa gauge", + "Stripper Underflow": "m3/hr", + "Stripper Temp": "Deg C", + "Stripper Steam Flow": "kg/hr", + "Compressor Work": "kW", + "Reactor Coolant Temp": "Deg C", + "Separator Coolant Temp": "Deg C", + "D feed load": "%", + "E feed load": "%", + "A feed load": "%", + "A and C feed load": "%", + "Compressor recycle valve": "%", + "Purge valve": "%", + "Separator liquid load": "%", + "Stripper liquid load": "%", + "Stripper steam valve": "%", + "Reactor coolant load": "%", + "Condenser coolant load": "%", +}; + +const importanceFilter: string[] = [ + "t2_A Feed", + "t2_D Feed", + "t2_E Feed", + "t2_A and C Feed", + "t2_Recycle Flow", + "t2_Reactor Feed Rate", + "t2_Reactor Pressure", + "t2_Reactor Level", + "t2_Reactor Temperature", + "t2_Purge Rate", + "t2_Product Sep Temp", + "t2_Product Sep Level", + "t2_Product Sep Pressure", + "t2_Product Sep Underflow", + "t2_Stripper Level", + "t2_Stripper Pressure", + "t2_Stripper Underflow", + "t2_Stripper Temp", + "t2_Stripper Steam Flow", + "t2_Compressor Work", + "t2_Reactor Coolant Temp", + "t2_Separator Coolant Temp", + "t2_D feed load", + "t2_E feed load", + "t2_A feed load", + "t2_A and C feed load", + "t2_Compressor recycle valve", + "t2_Purge valve", + "t2_Separator liquid load", + "t2_Stripper liquid load", + "t2_Stripper steam valve", + "t2_Reactor coolant load", + "t2_Condenser coolant load", +]; + +const intro = `The process produces two products from four reactants. Also present are an inert and a byproduct making a total of eight components: +A, B, C, D, E, F, G, and H. The reactions are: + +A(g) + C(g) + D(g) - G(liq): Product 1, + +A(g) + C(g) + E(g) - H(liq): Product 2, + +A(g) + E(g) - F(liq): Byproduct, + +3D(g) - 2F(liq): Byproduct. + +All the reactions are irreversible and exothermic. The reaction rates are a function of temperature through an Arrhenius expression. +The reaction to produce G has a higher activation energy resulting in more sensitivity to temperature. +Also, the reactions are approximately first-order with respect to the reactant concentrations. + +The process has five major unit operations: the reactor, the product condenser, a vapor-liquid separator, a recycle compressor and a product stripper. +Figure showing a diagram of the process is attached. + +The gaseous reactants are fed to the reactor where they react to form liquid products. The gas phase reactions are catalyzed by a nonvolatile catalyst dissolved +in the liquid phase. The reactor has an internal cooling bundle for removing the heat of reaction. The products leave the reactor as vapors along with the unreacted feeds. +The catalyst remains in the reactor. The reactor product stream passes through a cooler for condensing the products and from there to a vapor-liquid separator. +Noncondensed components recycle back through a centrifugal compressor to the reactor feed. +Condensed components move to a product stripping column to remove remaining reactants by stripping with feed stream number 4. +Products G and H exit the stripper base and are separated in a downstream refining section which is not included in this problem. +The inert and byproduct are primarily purged from the system as a vapor from the vapor-liquid separator.`; + +const postFaultThreshold: number = 20; +// TYPES +type RowType = { [key: string]: string }; +type CSVType = RowType[]; +type Image = { image: string; name: string }; +export type ChatMessage = { + id: string; + role: "user" | "assistant"; + text: string; + images: Image[]; + explanation: boolean; +}; +export type DataPointsId = { [key: string]: number[] }; +type ChatContextId = { + conversation: ChatMessage[]; + setConversation: React.Dispatch>; +}; +export type StatContextId = { t2_stat: number; anomaly: boolean; time: string }; + +interface SimulatorInterface { + csvFile: string; + interval: number; + setCurrentRow: (row: RowType | null) => void; + pause: boolean; +} + +// CONTEXTS +const DataPointsContext = createContext({} as DataPointsId); +const ConservationContext = createContext({} as ChatContextId); +const StatContext = createContext({} as StatContextId[]); + +function Simulator({ + csvFile, + interval, + setCurrentRow, + pause, +}: SimulatorInterface) { + const [data, setData] = useState([]); + const [currentIndex, setCurrentIndex] = useState(0); + + useEffect(() => { + console.log("Simulator component mounted or csvFile changed"); + if (csvFile) { + setCurrentIndex(0); + Papa.parse(csvFile, { + complete: (result) => { + // console.log('CSV file parsed:', result.data); + setData(result.data); + }, + header: true, + download: true, + skipEmptyLines: "greedy", + }); + } + }, [csvFile]); + + useEffect(() => { + if (data.length > 0 && !pause) { + // console.log('Starting interval to update current row'); + const timer = setInterval(() => { + setCurrentRow(data[currentIndex]); + setCurrentIndex((prevIndex) => (prevIndex + 1) % data.length); + }, interval); + + return () => { + // console.log('Clearing interval'); + clearInterval(timer); + }; // Cleanup interval on component unmount + } + }, [data, currentIndex, interval, setCurrentRow, pause]); + + return null; // This component doesn't render anything +} + +// eslint-disable-next-line react-refresh/only-export-components +export const startTime = new Date(); + +function getTopKElements(datapoints: DataPointsId, topK: number) { + // Step 1: Filter the columns based on importance_filter + const filteredData: DataPointsId = {}; + for (const key of importanceFilter) { + if (datapoints[key]) { + filteredData[key] = datapoints[key]; + } + } + + // Step 2: Retrieve the last element of the arrays for the filtered columns + const lastElements: { [key: string]: number } = {}; + for (const key in filteredData) { + lastElements[key] = filteredData[key][filteredData[key].length - 1]; + } + + // Step 3: Find the top K elements based on these last elements + const sortedKeys = Object.keys(lastElements).sort( + (a, b) => lastElements[b] - lastElements[a] + ); + const topKKeys = sortedKeys.slice(0, topK).map((a) => a.slice(3)); + + return topKKeys; +} + +export default function App() { + const [opened, { toggle }] = useDisclosure(); + const [selectedFileId, setSelectedFileId] = useState(0); + const [sliderValue, setSliderValue] = useState(1); // Default interval of 1 second + const [interval, setInterval] = useState(1); // Default interval of 1 second + const [currentRow, setCurrentRow] = useState(null); + const [dataPoints, setDataPoints] = useState({}); + const [pause, setPause] = useDisclosure(false); + const [t2_stat, setT2_stat] = useState([]); + const [currentFaultId, setCurrentFaultId] = useState(null); + const [prevFaultId, setPrevFaultId] = useState(0); + const [postFaultDataCount, setPostFaultDataCount] = useState(0); + const location = useLocation(); + const intro_msg: ChatMessage = { + id: "intro", + role: "assistant", + text: intro, + images: [intro_image], + explanation: false, + }; + const [conversation, setConversation] = useState([intro_msg]); + // const [conversation, setConversation] = useState([]); + + const handleFileChange = (value: string | null) => { + setSelectedFileId(fault_name.indexOf(value ?? fault_name[0])); + }; + + async function sendFaultToBackend( + fault: { [key: string]: number[] }, + id: string + ) { + await fetchEventSource("http://localhost:8000/explain", { + method: "POST", + headers: { + "Content-Type": "application/json", + Accept: "text/event-stream", + }, + body: JSON.stringify({ data: fault, id: id }), + async onopen(res) { + if (res.ok && res.status === 200) { + console.log("Connection made ", res); + // const empty_msg: ChatMessage = { + // id: "", + // role: "assistant", + // text: "", + // images: [], + // explanation: true, + // }; + // setConversation((data) => [...data, empty_msg]); + } else if ( + res.status >= 400 && + res.status < 500 && + res.status !== 429 + ) { + console.log("Client-side error ", res); + } + }, + onmessage(event) { + const parsedData = JSON.parse(event.data); + setConversation((prevMessages) => { + const index = prevMessages.findIndex( + (message) => message.id === parsedData.id + ); + if (index !== -1) { + // Message with the same id found, update it + const updatedMessages = [...prevMessages]; + updatedMessages[index] = { + ...updatedMessages[index], + text: updatedMessages[index].text + parsedData.content, + }; + return updatedMessages; + } else { + // New message, add it to the array + const newMessage: ChatMessage = { + id: parsedData.id, + role: "assistant", + text: parsedData.content, + images: parsedData.images, + explanation: true, + }; + return [...prevMessages, newMessage]; + } + }); + }, + onclose() { + console.log("Connection closed by the server"); + }, + onerror(err) { + console.log("There was an error from server", err); + }, + }); + } + + useEffect(() => { + if (currentRow) { + setDataPoints((prevDataPoints) => { + const newDataPoints = { ...prevDataPoints }; + for (const [key, value] of Object.entries(currentRow)) { + if (!newDataPoints[key]) { + newDataPoints[key] = []; + } + const numValue = parseFloat(value); // Convert the string value to a number + if (!isNaN(numValue)) { + newDataPoints[key] = [...newDataPoints[key], numValue].slice(-30); + } + } + return newDataPoints; + }); + setT2_stat((data) => { + if ("t2_stat" in currentRow && "anomaly" in currentRow) { + const date = new Date( + startTime.getTime() + (Number(currentRow.time) * 3 * 60000) / 0.05 + ); + const timeString = date.getHours() + ":" + date.getMinutes(); + + return [ + ...data, + { + t2_stat: parseFloat(currentRow.t2_stat), + anomaly: currentRow.anomaly === "True", + time: timeString, + }, + ]; + } else { + return data; + } + }); + if (currentRow.anomaly === "True") { + if (currentFaultId == null) { + setCurrentFaultId(prevFaultId + 1); + setPostFaultDataCount(0); + } else { + setPostFaultDataCount((count) => count + 1); + if (postFaultDataCount === postFaultThreshold) { + setPause.open(); + const topKKeys = getTopKElements(dataPoints, 6); + console.log(topKKeys); + const filteredObject = topKKeys.reduce( + (acc: Record, key) => { + acc[key] = dataPoints[key].map((a) => Number(a)); + return acc; + }, + {} + ); + sendFaultToBackend(filteredObject, `Fault-${currentFaultId}`); + } + } + } else { + if (currentFaultId !== null) { + setPrevFaultId(currentFaultId); + setCurrentFaultId(null); + setPostFaultDataCount(0); + } + } + } + }, [currentRow]); + return ( - - - - - - }> - } /> - } /> - } /> - - - - - - - ) +
+
+ +
+
+ + + + + + FaultExplainer + + + + `Fault ${i + 1}`)]} - value={selectedState} - onChange={handleStateChange} - placeholder="Select state" - /> -
-
- Rate: - - - {isPaused ? : } - -
-
- - ); -} - -export function Layout() { - const [opened, { toggle }] = useDisclosure(); - - return ( - - - - - {/*
Logo
*/} - - - - - - - - - - - - ); -} \ No newline at end of file diff --git a/frontend/src/PlotContext.tsx b/frontend/src/PlotContext.tsx deleted file mode 100644 index 3e9ea15..0000000 --- a/frontend/src/PlotContext.tsx +++ /dev/null @@ -1,50 +0,0 @@ -import { createContext, useState, useContext, useEffect } from 'react'; -import { socket } from './socket'; - -export const PlotContext = createContext(null); - -export const usePlot = () => useContext(PlotContext); - -export const PlotProvider = ({ children }) => { - const [dataSeries, setDataSeries] = useState({}); - const [selectedSeries, setSelectedSeries] = useState([]); - const [deselectedKeys, setDeselectedKeys] = useState([]); - const MAX_POINTS = 30; - - useEffect(() => { - socket.on('data_update', (message) => { - // console.log(message); - const newDataPoint = JSON.parse(message.data) - const timestamp = new Date(newDataPoint.timestamp).toLocaleTimeString(); - delete newDataPoint.timestamp; // Remove timestamp from data point - setDataSeries(prevData => { - const updatedData = { ...prevData }; - Object.keys(newDataPoint).forEach(key => { - if (!updatedData[key]) { - updatedData[key] = []; - } - updatedData[key].push({ time: timestamp, value: newDataPoint[key] }); - if (updatedData[key].length > MAX_POINTS) { - updatedData[key].shift(); - } - if (!selectedSeries.includes(key) && !deselectedKeys.includes(key)) { - setSelectedSeries(prevSelected => [...prevSelected, key]); - } - }); - return updatedData; - }); - }); - - return () => { - socket.off('data_update'); - }; - }, [dataSeries, selectedSeries]); - - // Add any other functions or logic you need to manage the data series - - return ( - - {children} - - ); -}; diff --git a/frontend/src/ReportContext.tsx b/frontend/src/ReportContext.tsx deleted file mode 100644 index 19c9f02..0000000 --- a/frontend/src/ReportContext.tsx +++ /dev/null @@ -1,40 +0,0 @@ -import { createContext, useState, useContext, useEffect } from 'react'; -import { socket } from './socket'; -import configData from './config.json'; - -export const ReportContext = createContext(null); - -export const useReport = () => useContext(ReportContext); - -export const ReportProvider = ({ children }) => { - const [dataPoints, setDataPoints] = useState<{ timestamp: any; t2_stat: number; anomaly: number; }[]>([]); - const [faultHistory, setFaultHistory] = useState([]); - - useEffect(() => { - const handleT2Update = async (data: { timestamp: any; t2_stat: number; anomaly: boolean; }) => { - const localtimestamp = new Date(data.timestamp).toLocaleTimeString(); - setDataPoints((prevDataPoints) => [ - ...prevDataPoints, - { timestamp: localtimestamp, t2_stat: data.t2_stat, anomaly: data.anomaly ? data.t2_stat : 0 }, - ]); - // console.log(data); - if (data.anomaly) { - const response = await fetch(`${configData.SERVER_URL}/fault_history`); - const faultHistoryData = await response.json(); - setFaultHistory(faultHistoryData); - } - }; - - socket.on('t2_update', handleT2Update); - - return () => { - socket.off('t2_update', handleT2Update); - }; - }, []); - - return ( - - {children} - - ) -} \ No newline at end of file diff --git a/frontend/src/assets/intro.json b/frontend/src/assets/intro.json new file mode 100644 index 0000000..a5f4b8e --- /dev/null +++ b/frontend/src/assets/intro.json @@ -0,0 +1,4 @@ +{ + "name": "tep_flowsheet", + "image": 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" +} diff --git a/frontend/src/assets/react.svg b/frontend/src/assets/react.svg new file mode 100644 index 0000000..6c87de9 --- /dev/null +++ b/frontend/src/assets/react.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/assets/tep_flowsheet.png b/frontend/src/assets/tep_flowsheet.png deleted file mode 100644 index be1e16b..0000000 Binary files a/frontend/src/assets/tep_flowsheet.png and /dev/null differ diff --git a/frontend/src/components/NavbarSimple.tsx b/frontend/src/components/NavbarSimple.tsx deleted file mode 100644 index 20cdab2..0000000 --- a/frontend/src/components/NavbarSimple.tsx +++ /dev/null @@ -1,30 +0,0 @@ -import { useState, useEffect } from 'react'; -import { NavLink, Box } from '@mantine/core'; -import { Link, useLocation } from 'react-router-dom'; -import { NavbarProps } from '../types/nav'; - -export function NavbarSimple({ data }: NavbarProps) { - const [active, setActive] = useState(-1); - const location = useLocation(); - - useEffect(() => { - const activeIndex = data.findIndex(item => item.link === location.pathname); - setActive(activeIndex >= 0 ? activeIndex : 0); - }, [location]); - - const links = data.map((item, index) => ( - setActive(index)} - - /> - )); - - return ( - {links} - ); -} \ No newline at end of file diff --git a/frontend/src/components/Plot.tsx b/frontend/src/components/Plot.tsx deleted file mode 100644 index d274f1d..0000000 --- a/frontend/src/components/Plot.tsx +++ /dev/null @@ -1,29 +0,0 @@ -import { useEffect, useState } from 'react'; -import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer } from 'recharts'; - -export const ChartingComponent = ({ data, valueKey }) => { - // console.log(data); - // console.log(valueKey); - const [chartKey, setChartKey] = useState(0); - - useEffect(() => { - // When data is updated, reset the key to force re-render - setChartKey(prevKey => prevKey + 1); - }, [data]); - const viewportHeight = window.innerHeight; - const chartHeight = viewportHeight * 0.2; - - return ( -
- - - - - - - - -
- ); -}; - diff --git a/frontend/src/config.json b/frontend/src/config.json deleted file mode 100644 index eca707d..0000000 --- a/frontend/src/config.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "SERVER_URL": "http://127.0.0.1:5001" -} \ No newline at end of file diff --git a/frontend/src/error-page.tsx b/frontend/src/error-page.tsx new file mode 100644 index 0000000..ea5c28a --- /dev/null +++ b/frontend/src/error-page.tsx @@ -0,0 +1,19 @@ +import { useRouteError } from "react-router-dom"; + +export default function ErrorPage() { + const error: unknown = useRouteError(); + console.error(error); + + return ( +
+

Oops!

+

Sorry, an unexpected error has occurred.

+

+ + {(error as { statusText?: string })?.statusText || + (error as Error)?.message} + +

+
+ ); +} diff --git a/frontend/src/main.tsx b/frontend/src/main.tsx index 0b6c239..a382057 100644 --- a/frontend/src/main.tsx +++ b/frontend/src/main.tsx @@ -1,16 +1,41 @@ -import React from 'react' -import ReactDOM from 'react-dom/client' -import { App } from './App.tsx' -import { MantineProvider, createTheme } from '@mantine/core'; +import ReactDOM from "react-dom/client"; +import { MantineProvider } from "@mantine/core"; +import App from "./App.tsx"; +import QuestionsPage from "./pages/ChatPage.tsx"; +import HistoryPage from "./pages/FaultReports.tsx"; +import DataPage from "./pages/PlotPage.tsx"; +import ErrorPage from "./error-page.tsx"; +import { createBrowserRouter, RouterProvider } from "react-router-dom"; +import "@mantine/core/styles.css"; +import "@mantine/charts/styles.css"; -const theme = createTheme({ - /** Put your mantine theme override here */ -}); +const router = createBrowserRouter([ + { + path: "/", + element: , + errorElement: , + children: [ + { + path: "plot", + element: , + }, + { + // path: "chat", + index: true, + element: , + }, + { + path: "history", + element: , + }, + ], + }, +]); -ReactDOM.createRoot(document.getElementById('root')!).render( - - - - - , -) +ReactDOM.createRoot(document.getElementById("root")!).render( + // + + + + // , +); diff --git a/frontend/src/pages/ChatPage.tsx b/frontend/src/pages/ChatPage.tsx index 644e936..16c233f 100644 --- a/frontend/src/pages/ChatPage.tsx +++ b/frontend/src/pages/ChatPage.tsx @@ -1,217 +1,190 @@ -// import { useState, useEffect, useRef } from 'react'; -// import { ScrollArea, Textarea, Button, Box } from '@mantine/core'; -// import { useChat } from '../ChatContext'; -// import { socket } from '../socket'; - - -// export function ChatPage() { -// const { messages, setMessages } = useChat(); -// // const [messages, setMessages] = useState<{ text: string, isUser: boolean }[]>([]); // Store chat messages -// const [newMessage, setNewMessage] = useState(''); // Store new message input -// const scrollAreaRef = useRef(null); // Ref to scroll to the end of the chat -// const [isWaitingForReply, setIsWaitingForReply] = useState(false); - -// useEffect(() => { -// scrollAreaRef.current!.scrollTo({ top: scrollAreaRef.current!.scrollHeight, behavior: 'smooth' }); -// }, [messages]); - -// useEffect(() => { -// const receiveMessage = (data) => { -// if (typeof data === 'string') { -// // Regular text message -// const msg = { text: data, isUser: false }; -// setMessages(messages => [...messages, msg]); -// } else if (data.images && data.message) { -// // Fault analysis message with graphs and text -// const combinedMessage = { -// text: ( -//
-//
-// {data.images.map((graph, index) => ( -// {`Graph -// ))} -//
-//

{data.message}

-//
-// ), -// isUser: false -// }; -// setMessages(messages => [...messages, combinedMessage]); -// } -// setIsWaitingForReply(false); // Server reply received, allow sending messages again -// }; - -// socket.on('chat_reply', receiveMessage); - -// return () => { -// socket.off('chat_reply', receiveMessage); -// }; -// }, []); - - -// // Function to send a message -// const sendMessage = () => { -// if (newMessage.trim() && !isWaitingForReply) { -// setMessages(messages => [...messages, { text: newMessage, isUser: true }]); -// socket.emit('chat_message', newMessage); -// setNewMessage(''); -// setIsWaitingForReply(true); // Start waiting for server reply - -// // setTimeout(() => { -// // // Simulate server response -// // const serverMessage = "SERVER MESSAGE"; // Replace with dynamic server message if needed -// // setMessages(messages => [...messages, { text: serverMessage, isUser: false }]); -// // setIsWaitingForReply(false); // Server reply received, allow sending messages again -// // }, 1000); // Adjust the delay as needed to simulate server response time -// } -// }; - -// return ( -// -// -// -// {messages.map((message, index) => ( -// -// -// {message.text} -// -// -// ))} -// -// - -// -//