🐦 Tweet Generator for Research Labs & LLM Learners

Easily draft a connected thread of up to 5 tweets to share new papers and findings. Perfect for research labs and anyone learning to use local LLMs like LLaMA 3 — explore prompt design, app building, and local inference all in one simple project.

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⚙️ How It Works

• Runs locally: Powered by Ollama serving LLaMA 3 models on your machine—no API keys, no data leaves your system.
• Flexible interfaces: Use the Streamlit web app or the command-line tool.
• Ideal for: Science communication and LLM experimentation.

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📋 Prerequisites

• Python 3.8 or higher
• Ollama installed

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🚀 Installation

1. Install Ollama:

   • Windows/macOS: Download Ollama
   • Linux:

     curl https://ollama.ai/install.sh | sh

2. Pull the Llama 3 model:

   ollama pull llama3

3. Install Python dependencies:

   pip install -r requirements.txt

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🔍 Test Your Ollama Setup

Check your Ollama installation:

python test_ollama.py

This script will:

• Test the Ollama connection
• Verify the model is available
• Run a sample prompt

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🛠️ Troubleshooting Ollama

Port error with ollama serve?
Ollama may already be running. Try:

# Windows
taskkill /F /IM ollama.exe

# Linux/Mac
pkill ollama

Then restart ollama serve.

Ollama not responding?

• Ensure the service is running
• Restart your computer if needed
• Confirm the llama3 model is pulled:

  ollama pull llama3

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🎮 Running the Application

🌐 Web Interface (Streamlit)

1. Start Ollama
2. Launch the app:

   streamlit run app.py

3. Open your browser to the displayed URL (usually http://localhost:8501)

💻 Command Line Interface

1. Start Ollama
2. Run:

   python cli.py

3. Follow the prompts

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📖 Usage

Web Interface

1. Enter your text
2. Select number of tweets (1–5)
3. Click Generate Tweets
4. View your thread

Command Line

1. Run the program
2. Enter your text (press Enter twice to finish)
3. Enter number of tweets
4. View your thread
5. Optionally, generate more tweets

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⚠️ Note

Make sure Ollama is running before starting the app. The application uses LangChain’s Ollama integration to communicate with your local Ollama instance.