🧠 FallacyLens (Groq Edition)

FallacyLens is an AI-powered logical fallacy detection toolkit for arguments, essays, and debates.

This version uses the Groq Chat Completions API (e.g. llama-3.3-70b-versatile) as the reasoning engine. Your Python code controls the prompt, parsing, and presentation.

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🔴 Live demo

Try the public demo on Streamlit Cloud:

👉 https://fallacylens-groq-wiqilee.streamlit.app

⚠️ This demo uses a shared Groq API key.
Please avoid automated or excessive requests.

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✨ Features

• Logical fallacy span detection with severity + confidence
• Inline highlighting for detected fallacies in your text
• Clarity / Persuasion / Reliability scores (0–100)
• Assistant tools: rewrite, teacher feedback, persuasion optimizer, bias review
• Batch CSV analysis (column: text)
• Multi-model comparison across Groq-hosted models

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🧰 Tech Stack

• Python
• Streamlit (demo UI)
• FastAPI (service API)
• Groq Chat Completions API (LLM inference)

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⚙️ Setup

1. Create a Groq API key at https://console.groq.com.

2. Export your key as an environment variable:

   export GROQ_API_KEY="YOUR_REAL_GROQ_API_KEY"

   For quick local testing only, you can also edit fallacylens/detector.py and replace the string "YOUR_GROQ_API_KEY_HERE" with your real key, but you must not commit that to GitHub.

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Streamlit demo:

   streamlit run demo/app.py

5. Run the FastAPI service:

   uvicorn api.main:app --reload

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🗂️ Project Structure

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├─ api/                 # FastAPI service
├─ demo/                # Streamlit demo app
├─ fallacylens/         # Core library (detector, models, taxonomy)
├─ requirements.txt
└─ README.md

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🔐 Security Notes

• Never commit secrets (API keys, .env, or Streamlit secrets).
• Use environment variables (recommended) or your deployment platform’s secret manager.
• This repo includes a .gitignore that helps prevent accidental commits of credentials.

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👤 Author

Built by Wiqi Lee
X / Twitter: @wiqi_lee

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📄 License

MIT — see LICENSE.