Update main.py

main.py

@@ -1,43 +1,39 @@
 import os
 import time
-import json
+import json # Import JSON for parsing
 from flask import Flask, request, jsonify, render_template
-from flask_cors import CORS
-from dotenv import load_dotenv
-
+from flask_cors import CORS 
 from langchain_groq import ChatGroq
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain.chains.combine_documents import create_stuff_documents_chain
+# from langchain.chains 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
+from dotenv import load_dotenv
 
-# ==========================================================
-#  Load environment variables
-# ==========================================================
+# Load environment variables
 load_dotenv()
+
+# --- LLM and API Key Setup ---
 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.")
+    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.
+    Loads the vector database and creates the retrieval chain.
+    This function runs once when the server starts.
     """
-    print("🔄 Initializing retrieval chain...")
-
+    print("Loading vector database... This may take a moment.")
+    
+    # --- PROMPT TEMPLATE - Reverted to simple stateless version ---
     prompt_template = """
 You are a friendly and helpful hotel assistant.
 Your role is to provide clear, welcoming, and professional responses to guest questions.
@@ -61,104 +57,108 @@ 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()
+    loader = PyPDFDirectoryLoader("data")
+    docs = loader.load()
+    
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    final_documents = text_splitter.split_documents(docs[:50])
+    
+    vectors = FAISS.from_documents(final_documents, embeddings)
+    
+    print("Vector database loaded successfully.")
+    
     document_chain = create_stuff_documents_chain(llm, prompt)
+    
+    # 1. Create the retriever from the vector store
+    retriever = vectors.as_retriever()
+    # 2. Create the retrieval chain
     retrieval_chain = create_retrieval_chain(retriever, document_chain)
-
-    print("✅ Retrieval chain initialized successfully.")
+    
     return retrieval_chain
 
-# ==========================================================
-#  Flask App Setup
-# ==========================================================
+# --- Flask App Initialization ---
 app = Flask(__name__)
-CORS(app)
+CORS(app)  # Enable CORS for all routes in your app
 
-retrieval_chain = None  # Lazy-load later
+# Load the retrieval chain ONCE when the app starts
+try:
+    retrieval_chain = load_retrieval_chain()
+except Exception as e:
+    print(f"Failed to load vector database on startup: {e}")
+    retrieval_chain = None
 
-@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
-# ==========================================================
+# --- NEW ROUTE TO SERVE YOUR WEBPAGE ---
 @app.route("/")
 def index():
-    """Serve main web page."""
-    return render_template("index.html")
+    """
+    Serves the index.html file from the 'templates' folder.
+    """
+    return render_template('index.html')
 
 @app.route("/chat", methods=["POST"])
 def chat():
-    """Main chat endpoint."""
-    global retrieval_chain
-
+    """
+    The main chat endpoint.
+    Receives a JSON with "query" and returns a JSON with "intent" and "response".
+    """
     if retrieval_chain is None:
-        return jsonify({"error": "Vector database not initialized. Try again in a few seconds."}), 500
+        return jsonify({"error": "Vector database is not initialized. Check server logs."}), 500
 
     try:
         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()
+        print(f"Received query: {user_query}")
 
-        # Run retrieval chain
+        start = time.process_time()
+        
+        # Invoke the chain with the user's query
         response = retrieval_chain.invoke({'input': user_query})
-        elapsed = time.process_time() - start
-        print(f"⏱️ Response time: {elapsed:.3f} sec")
+        
+        response_time = time.process_time() - start
+        print(f"Response time: {response_time:.4f} seconds")
 
-        # Parse LLM JSON
+        # --- Parse the JSON response from the LLM ---
         try:
+            # The LLM's answer is in the 'answer' field
             llm_output_str = response['answer']
-            parsed = json.loads(llm_output_str)
-            parsed["context"] = [doc.page_content for doc in response['context']]
-            return jsonify(parsed)
+            # The LLM output itself is a JSON string, so we parse it.
+            parsed_response = json.loads(llm_output_str)
+            
+            # We can also add the RAG context for debugging
+            parsed_response["context"] = [doc.page_content for doc in response['context']]
+            
+            print(f"LLM Response: {parsed_response}")
+            
+            return jsonify(parsed_response)
+
         except json.JSONDecodeError:
-            print(f"⚠️ Invalid JSON from LLM: {response.get('answer', '')}")
+            print(f"Error: LLM did not return valid JSON. Response was: {llm_output_str}")
             return jsonify({"intent": "qa", "response": "I'm sorry, I had a small glitch. Could you rephrase that?"})
+        except Exception as e:
+            print(f"Error parsing LLM response: {e}")
+            return jsonify({"intent": "qa", "response": "I'm sorry, I'm having trouble processing that request."})
+
     except Exception as e:
-        print(f"❌ Error during chat request: {e}")
+        print(f"Error processing request: {e}")
         return jsonify({"error": str(e)}), 500
 
-# ==========================================================
-#  App Runner (for local debugging)
-# ==========================================================
+# --- /book ENDPOINT REMOVED ---
+
+# --- Run the Flask Server ---
 if __name__ == "__main__":
-    print("🚀 Starting Flask development server...")
-    app.run(host="0.0.0.0", port=5000, debug=True)
+    # Ensure a 'data' directory exists
+    if not os.path.exists("data"):
+        os.makedirs("data")
+        print("Created 'data' directory. Please add your PDF files here and restart the server.")
+        
+    # init_db() call removed
+        
+    print("Starting Flask server...")
+    # Running on 0.0.0.0 makes it accessible on your network, ready for EC2
+    app.run(debug=True, host="0.0.0.0", port=7860)