app.py

import os
import gradio as gr
import torch
from datasets import load_dataset
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer, AutoModelForCausalLM
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS

# === 1. Build the FAISS vectorstore from CUAD ===
print(" Loading CUAD and building index...")
#new
from datasets import load_dataset

cuad_data = load_dataset("lex_glue", "cuad")

texts = [item["text"] for item in cuad_data["train"] if "text" in item]

splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
docs = splitter.create_documents(texts)

embedding_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectorstore = FAISS.from_documents(docs, embedding_model)

# === 2. Model setup ===
USE_LLAMA = os.environ.get("USE_LLAMA", "false").lower() == "true"
HF_TOKEN = os.environ.get("HF_TOKEN")

def load_llama():
    tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf", use_auth_token=True)
    model = AutoModelForCausalLM.from_pretrained(
        "meta-llama/Llama-2-7b-chat-hf",
        device_map="auto",
        torch_dtype=torch.float16
    )
    return tokenizer, model

def generate_llama_response(prompt):
    inputs = llama_tokenizer(prompt, return_tensors="pt").to("cuda")
    outputs = llama_model.generate(**inputs, max_new_tokens=300)
    return llama_tokenizer.decode(outputs[0], skip_special_tokens=True)

def generate_mistral_response(prompt):
    return mistral_client.text_generation(prompt=prompt, max_new_tokens=300).strip()

if USE_LLAMA:
    llama_tokenizer, llama_model = load_llama()
    generate_response = generate_llama_response
else:
    mistral_client = InferenceClient(
        model="mistralai/Mistral-7B-Instruct-v0.1",
        token=HF_TOKEN
    )
    generate_response = generate_mistral_response

# === 3. Main QA function ===
def answer_question(user_query):
    docs = vectorstore.similarity_search(user_query, k=3)
    context = "\n".join([doc.page_content for doc in docs])
    prompt = f"""[Context]
{context}

[User Question]
{user_query}

[Answer]
"""
    return generate_response(prompt)

# === 4. Gradio UI ===
iface = gr.Interface(
    fn=answer_question,
    inputs=gr.Textbox(placeholder="Ask a question about your contract..."),
    outputs=gr.Textbox(label="Answer"),
    title="LawCounsel AI",
    description="Ask clause-specific questions from CUAD-trained contracts. Powered by RAG using Mistral or LLaMA.",
)

iface.launch()