⚖️ Pakistan Legal Precedent & Case Management AI System

An intelligent AI-powered legal assistant designed to automate key tasks in the legal domain — including case classification, case prioritization, and legal precedent retrieval using Retrieval-Augmented Generation (RAG).
This tool combines NLP pipelines, machine learning, and LLM-based reasoning to support faster and smarter legal decision-making.

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🚀 Features

🧾 Case Classification

Automatically classifies uploaded or entered legal case text into Civil, Criminal, or Constitutional categories using trained ML pipelines.

⏳ Case Prioritization

Predicts the urgency level of a case (High, Medium, Low) to help manage workload efficiently.

📚 Legal Precedent Search (RAG)

Implements a Retrieval-Augmented Generation (RAG) pipeline that retrieves relevant past legal precedents using vector embeddings and LLM-powered summarization.

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🧠 Tech Stack

• Python 3.13+
• Streamlit – for the user interface
• Scikit-learn – for ML classification and stacking pipelines
• LangChain + ChromaDB – for document retrieval and embeddings
• HuggingFace Embeddings – for text vectorization
• ChatGroq API – for LLM integration
• Pickle Pipelines – for pre-trained ML models (voting_pipeline.pkl, stacking_pipeline.pkl, etc.)

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📂 Project Structure

├── app.py # Main Streamlit app
├── Case Cateogarization/ # Classification model + encoder
│ ├── voting_pipeline.pkl
│ └── label_encoder.pkl
├── Case Prioritization/ # Priority prediction model + encoder
│ ├── stacking_pipeline.pkl
│ └── label_encoder.pkl
├── Legal_Precedent_Search/ # RAG configuration files
│ ├── embeddings_config.pkl
│ ├── llm_config.pkl
│ ├── prompt_template.pkl
│ └── chroma_db/ # Vector database
├── .env # Groq API Key
└── requirements.txt # Dependencies