main.py

import os
import time
import json
from flask import Flask, request, jsonify, render_template
from flask_cors import CORS
from dotenv import load_dotenv
import logging
from langchain_groq import ChatGroq
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains import create_retrieval_chain
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFDirectoryLoader
from langchain_huggingface import HuggingFaceEmbeddings

logging.basicConfig(level=logging.DEBUG)

# ==========================================================
#  Load environment variables
# ==========================================================
load_dotenv()
groq_api_key = os.getenv("GROQ_API_KEY")

if not groq_api_key:
    raise ValueError("❌ GROQ_API_KEY not found. Please set it in your .env file or as an environment variable.")

# ==========================================================
#  Initialize LLM
# ==========================================================
llm = ChatGroq(groq_api_key=groq_api_key, model_name="llama-3.1-8b-instant")

# ==========================================================
#  Function: Load / Build Retrieval Chain
# ==========================================================
def load_retrieval_chain():
    """
    Loads or builds the FAISS vector index and creates a retrieval chain.
    This is now lazy-loaded to prevent Gunicorn worker boot crashes.
    """
    print("🔄 Initializing retrieval chain...")

    prompt_template = """
You are a friendly and helpful hotel assistant.
Your role is to provide clear, welcoming, and professional responses to guest questions.
You MUST respond in a valid JSON format.

The JSON object must have two keys:
1. "intent": (string) This will always be "qa" for this version.
2. "response": (string) Your natural language, conversational response to the user.

RULES:
- Base your answers ONLY on the provided context. If the information is not in the context,
  politely say "I'm sorry, I don't have that information, but I can connect you with our front desk for assistance."
- Do not make up information.

<context>
{context}
<context>

Question: {input}

Your JSON Response:
"""
    prompt = ChatPromptTemplate.from_template(prompt_template)

    # --- Load Embeddings ---
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

    # --- Create or Load FAISS Vectorstore ---
    if not os.path.exists("data"):
        os.makedirs("data")
        print("⚠️ 'data' folder created. Please add your PDFs and restart.")
        raise ValueError("No PDFs found in 'data' folder.")

    if os.path.exists("faiss_index"):
        print("✅ Loading existing FAISS index...")
        vectors = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
    else:
        print("📄 Loading PDFs and building FAISS index (first-time setup)...")
        loader = PyPDFDirectoryLoader("data")
        docs = loader.load()
        if not docs:
            raise ValueError("No PDF documents found in 'data' folder.")
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        final_docs = text_splitter.split_documents(docs[:50])
        vectors = FAISS.from_documents(final_docs, embeddings)
        vectors.save_local("faiss_index")
        print("💾 FAISS index saved to 'faiss_index' for future runs.")

    # --- Create Chains ---
    retriever = vectors.as_retriever()
    document_chain = create_stuff_documents_chain(llm, prompt)
    retrieval_chain = create_retrieval_chain(retriever, document_chain)

    print("✅ Retrieval chain initialized successfully.")
    return retrieval_chain

# ==========================================================
#  Flask App Setup
# ==========================================================
app = Flask(__name__)
CORS(app)

retrieval_chain = None  # Lazy-load later

@app.before_request
def init_retrieval():
    """Initialize retrieval chain after Flask starts (prevents Gunicorn crash)."""
    global retrieval_chain
    if retrieval_chain is None:
        try:
            retrieval_chain = load_retrieval_chain()
        except Exception as e:
            print(f"❌ Failed to initialize retrieval chain: {e}")
            retrieval_chain = None

# ==========================================================
#  Routes
# ==========================================================
@app.route("/")
def index():
    """Serve main web page."""
    return render_template("index.html")

@app.route("/chat", methods=["POST"])
def chat():
    """Main chat endpoint."""
    global retrieval_chain

    if retrieval_chain is None:
        return jsonify({"error": "Vector database not initialized. Try again in a few seconds."}), 500

    try:
        user_input = request.json.get("message")
        app.logger.info(f"Received user input: {user_input}")
        data = request.json
        user_query = data.get("query")
        if not user_query:
            return jsonify({"error": "No query provided"}), 400

        print(f"💬 Received query: {user_query}")
        start = time.process_time()

        # Run retrieval chain
        response = retrieval_chain.invoke({'input': user_query})
        elapsed = time.process_time() - start
        print(f"⏱️ Response time: {elapsed:.3f} sec")

        # Parse LLM JSON
        try:
            llm_output_str = response['answer']
            parsed = json.loads(llm_output_str)
            parsed["context"] = [doc.page_content for doc in response['context']]
            return jsonify(parsed)
        except json.JSONDecodeError:
            print(f"⚠️ Invalid JSON from LLM: {response.get('answer', '')}")
            return jsonify({"intent": "qa", "response": "I'm sorry, I had a small glitch. Could you rephrase that?"})
    except Exception as e:
        print(f"❌ Error during chat request: {e}")
        return jsonify({"error": str(e)}), 500

# ==========================================================
#  App Runner (for local debugging)
# ==========================================================
if __name__ == "__main__":
    print("🚀 Starting Flask development server...")
    app.run(host="0.0.0.0", port=7860, debug=True)