RAG System with Sentiment, Reward Scoring & LLM Switch

A flexible, production-ready Retrieval-Augmented Generation (RAG) pipeline built with FAISS, Hugging Face Transformers, and Gradio. This project includes real-time sentiment analysis, reward scoring simulation (RLHF-like), user-selectable LLMs (GPT-2 or LLaMA via Hugging Face Inference API), and local SQLite database logging.

🚀 Features

• ✅ Hugging Face datasets loading (IMDB)
• 🧠 Tokenization + Embeddings via Transformers
• 📊 Vector similarity using FAISS
• 🧮 Real-time Sentiment Analysis (positive, negative, neutral)
• 🎯 Reward Score Simulation (RLHF-style)
• 🧩 SQLite database to save and log interactions
• 🔁 Dynamic model switching between:
  • GPT-2 (local)
  • LLaMA (via Hugging Face Inference API)
• 🌈 Color-coded sentiment feedback
• 🖼️ Modern Gradio UI (Colab-compatible)

📦 Installation

Make sure you're using Python 3.10+.

pip install -r requirements.txt
If using LLaMA via Hugging Face Inference API:

bash
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huggingface-cli login
💡 Usage
bash
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python script.py
The app will launch on your browser with a Gradio interface.

📂 Project Structure
graphql
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├── script.py              # Main script
├── requirements.txt       # Dependencies
└── interaction_logs.db    # Auto-created SQLite log file
📊 Example Use-Case
Enter a movie review or sentence.

Choose GPT-2 or LLaMA.

Click generate.

See:

Answer (generated)

Sentiment with color

RLHF-style reward score

Optionally, save the chat for future analysis.

🧠 Tech Stack
🤗 Transformers

🔍 FAISS (Facebook AI Similarity Search)

🔤 Hugging Face Datasets

📊 SQLite (lightweight database)

🎛️ Gradio for UI

🧪 GPT-2 / LLaMA for generation

✅ To-Do / Improvements
 Add real reward model (e.g., PPO RLHF)

 Multi-turn memory

 Hugging Face Spaces deployment

 Authenticated dashboard for logs

📜 License
MIT License