hbenayed1976/maliki_mufti

This interactive application, built with Streamlit, provides answers using a corpus of Tunisian fatwas, a Q&A dataset, and the Qur’an. It leverages a Retrieval-Augmented Generation (RAG) approach with embedding models (AraBERT, MARBERT, MiniLM) and the Gemini LLM. The system employs a FAISS vector database to ensure fast and relevant information # 🕌 Fatwa & Qur’an Consultation System (RAG + Gemini) This project is a **Streamlit-based application** designed to provide answers to user questions using: - 📖 Fatwas (`fatwa-tounisia.pdf`) - ❓ Q&A dataset (`qa.txt`) - 📜 Qur’an (`tafri3.pdf`) (created by dr. Amir Baklouti) It leverages **Retrieval-Augmented Generation (RAG)** with: - **Embeddings:** AraBERT, MARBERT, multilingual MiniLM - **LLM:** Google Gemini API - **Vector Database:** FAISS - **Interface:** Streamlit Chat UI --- ## ✨ Features - 🔎 **Context-aware Q&A** with Fatwa, Qur’an, and Q&A datasets - 📜 **Qur’an verse detection** (with surah & ayah metadata) - 🧠 **Multiple embedding models** for evaluation (AraBERT, MARBERT, MiniLM) - 🌐 **Arabic text reshaping** for proper display - ⚡ **Gemini LLM integration** (`gemini-2.5-flash) - 📚 **Source attribution** (Fatwa / Qur’an / Q&A) in every answer - 💬 **Interactive chat interface** with persistent history --- ## 📂 Repository Structure ├── rag_gemini_embeddings.py # Main Streamlit application ├── fatwa-tounisia.pdf # ├── qa.txt # Q&A dataset ├── tafri3.pdf # Qur’an dataset ├── README.md # Project documentation