app.py

import json
import gradio as gr
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.schema import Document
from huggingface_hub import InferenceClient
import os

# ✅ Step 1: Load and Chunk JSON with Metadata
file_path = "pdf_data.json"
documents = []

splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)

try:
    with open(file_path, "r", encoding="utf-8") as f:
        data = json.load(f)
        for item in data:
            if "text" in item:
                section = "PPC" if "punishment" in item["text"].lower() or "section" in item["text"].lower() else "other"
                law_type = "criminal" if section == "PPC" else "general"
                chunks = splitter.split_text(item["text"])
                for chunk in chunks:
                    documents.append(Document(
                        page_content=chunk,
                        metadata={"section": section, "law_type": law_type}
                    ))
except Exception as e:
    print(f"❌ Failed to load: {e}")

print(f"✅ Loaded {len(documents)} chunks with metadata")

# ✅ Step 2: Create Embeddings & FAISS Vector Store
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
db = FAISS.from_documents(documents, embedding_model)

# ✅ Step 3: Load Zephyr-7B via Hugging Face Inference API
client = InferenceClient(
    model="HuggingFaceH4/zephyr-7b-beta",
    token=os.getenv("HF_TOKEN")  # set your token in environment variable
)

# ✅ Step 4: QA Function using chat_completion with formatting
def ask_law_bot(query):
    try:
        results = db.similarity_search(query, k=5, filter={"section": "PPC"})
        if not results:
            return "❌ No relevant content found for this topic."

        context = "\n\n".join([doc.page_content for doc in results if len(doc.page_content.strip()) > 100])

        prompt = f"""You are a legal assistant helping users understand Pakistani law.
Respond to the question using the given legal context. Your answer must follow these rules:
- Use numbered bullet points (1. 2. 3.)
- Reference relevant law sections like (section 220(b))
- Be concise, clear, and avoid repetition
- Use "YES" or "NO" if the question requires binary response

Context:
{context}

Question: {query}
Answer:"""

        response = client.chat_completion(
            messages=[
                {"role": "system", "content": "You are a helpful and concise legal assistant for Pakistani law."},
                {"role": "user", "content": prompt}
            ],
            max_tokens=512
        )

        return response.choices[0].message["content"].strip()

    except Exception as e:
        return f"❌ Error: {e}"

# ✅ Step 5: Gradio UI
gr.Interface(
    fn=ask_law_bot,
    inputs=gr.Textbox(lines=2, placeholder="e.g., What is the punishment for theft?"),
    outputs=gr.Textbox(label="📘 Legal Answer"),
    title="⚖️ Ask Pakistan Law — Powered by Zephyr 7B",
    description="Ask questions from Pakistan's law using FAISS retrieval + Zephyr-7B via Hugging Face API.",
    examples=[
        "What is the punishment for theft?",
        "What are the duties of the Commission?",
        "What is the process of appeal under this law?"
    ]
).launch(share=True, debug=True)