Spaces:
Runtime error
Runtime error
File size: 3,397 Bytes
d3bc1c4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
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)
|