Update main.py

main.py

@@ -1,164 +1,164 @@
-import os
-import time
-import json # Import JSON for parsing
-from flask import Flask, request, jsonify, render_template
-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_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_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.")
-
-llm = ChatGroq(groq_api_key=groq_api_key, model_name="llama-3.1-8b-instant")
-
-
-def load_retrieval_chain():
-    """
-    Loads the vector database and creates the retrieval chain.
-    This function runs once when the server starts.
-    """
-    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.
-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)
-    
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-    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)
-    
-    return retrieval_chain
-
-# --- Flask App Initialization ---
-app = Flask(__name__)
-CORS(app)  # Enable CORS for all routes in your app
-
-# 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
-
-# --- NEW ROUTE TO SERVE YOUR WEBPAGE ---
-@app.route("/")
-def index():
-    """
-    Serves the index.html file from the 'templates' folder.
-    """
-    return render_template('index.html')
-
-@app.route("/chat", methods=["POST"])
-def chat():
-    """
-    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 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()
-        
-        # Invoke the chain with the user's query
-        response = retrieval_chain.invoke({'input': user_query})
-        
-        response_time = time.process_time() - start
-        print(f"Response time: {response_time:.4f} seconds")
-
-        # --- Parse the JSON response from the LLM ---
-        try:
-            # The LLM's answer is in the 'answer' field
-            llm_output_str = response['answer']
-            # 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"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 processing request: {e}")
-        return jsonify({"error": str(e)}), 500
-
-# --- /book ENDPOINT REMOVED ---
-
-# --- Run the Flask Server ---
-if __name__ == "__main__":
-    # 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=5000)
-
+import os
+import time
+import json # Import JSON for parsing
+from flask import Flask, request, jsonify, render_template
+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_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_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.")
+
+llm = ChatGroq(groq_api_key=groq_api_key, model_name="llama-3.1-8b-instant")
+
+
+def load_retrieval_chain():
+    """
+    Loads the vector database and creates the retrieval chain.
+    This function runs once when the server starts.
+    """
+    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.
+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)
+    
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    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)
+    
+    return retrieval_chain
+
+# --- Flask App Initialization ---
+app = Flask(__name__)
+CORS(app)  # Enable CORS for all routes in your app
+
+# 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
+
+# --- NEW ROUTE TO SERVE YOUR WEBPAGE ---
+@app.route("/")
+def index():
+    """
+    Serves the index.html file from the 'templates' folder.
+    """
+    return render_template('index.html')
+
+@app.route("/chat", methods=["POST"])
+def chat():
+    """
+    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 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()
+        
+        # Invoke the chain with the user's query
+        response = retrieval_chain.invoke({'input': user_query})
+        
+        response_time = time.process_time() - start
+        print(f"Response time: {response_time:.4f} seconds")
+
+        # --- Parse the JSON response from the LLM ---
+        try:
+            # The LLM's answer is in the 'answer' field
+            llm_output_str = response['answer']
+            # 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"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 processing request: {e}")
+        return jsonify({"error": str(e)}), 500
+
+# --- /book ENDPOINT REMOVED ---
+
+# --- Run the Flask Server ---
+if __name__ == "__main__":
+    # 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)
+