app.py

import gradio as gr
import os
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
import requests

# Paths
PROCESSED_DATA_DIR = "data/preprocessed/"
API_URL = "https://api-inference.huggingface.co/models/gpt2"

# Load Hugging Face API key from environment variable
HUGGINGFACE_API_KEY = os.getenv("HF_API_TOKEN")
if not HUGGINGFACE_API_KEY:
    raise ValueError("Hugging Face API token is not set. Please ensure HF_API_TOKEN is added as a secret.")

headers = {"Authorization": f"Bearer {HUGGINGFACE_API_KEY}"}

# Load FAISS Index
def load_faiss_index(processed_data_dir):
    """
    Load the FAISS index and embedding model.
    """
    embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
    vector_store = FAISS.load_local(
        processed_data_dir, embedding_model, allow_dangerous_deserialization=True
    )
    return vector_store

vector_store = load_faiss_index(PROCESSED_DATA_DIR)

# Query GPT-2 Model via Hugging Face API
def query_huggingface_api(prompt):
    """
    Query the Hugging Face GPT-2 model via the Inference API.
    """
    response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
    if response.status_code == 200:
        return response.json()[0]["generated_text"]
    else:
        return f"Error {response.status_code}: {response.text}"

# Generate Response
def generate_response(query):
    """
    Generate a response using FAISS and GPT-2.
    """
    retriever = vector_store.as_retriever()
    retrieved_chunks = retriever.get_relevant_documents(query)

    if not retrieved_chunks:
        return "No relevant documents found."

    # Combine retrieved chunks into context
    context = "\n\n".join([doc.page_content for doc in retrieved_chunks[:3]])[:1500]
    prompt = (
        f"You are a legal expert specializing in business laws and the legal environment. "
        f"Using the following context, answer the question concisely and accurately.\n\n"
        f"Context:\n{context}\n\nQuestion: {query}\n\nAnswer:"
    )
    return query_huggingface_api(prompt)

# Gradio Interface for QA Bot
def qa_bot(query):
    """
    Gradio wrapper function for the QA Bot.
    """
    return generate_response(query)

# Define Gradio Interface
demo = gr.Interface(
    fn=qa_bot,
    inputs=gr.Textbox(label="Enter your question:"),
    outputs=gr.Textbox(label="Answer"),
    title="Business Law QA Bot",
    description="Ask questions about business laws and the legal environment. Powered by FAISS and GPT-2.",
)

if __name__ == "__main__":
    demo.launch()