Update main.py

main.py

@@ -1,10 +1,12 @@
 import os
+import shutil
 from flask import Flask, render_template, request, jsonify
 from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
 from llama_index.llms.huggingface import HuggingFaceInferenceAPI
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from huggingface_hub import InferenceClient
 
+# Initialize environment and settings
 repo_id = "meta-llama/Meta-Llama-3-8B-Instruct"
 llm_client = InferenceClient(
     model=repo_id,
@@ -12,7 +14,6 @@ llm_client = InferenceClient(
 )
 
 os.environ["HF_TOKEN"] = os.getenv("HF_TOKEN")
-# Configure Llama index settings
 Settings.llm = HuggingFaceInferenceAPI(
     model_name="meta-llama/Meta-Llama-3-8B-Instruct",
     tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
@@ -31,54 +32,47 @@ PDF_DIRECTORY = 'data'
 # Ensure directories exist
 os.makedirs(PDF_DIRECTORY, exist_ok=True)
 os.makedirs(PERSIST_DIR, exist_ok=True)
+
 chat_history = []
 current_chat_history = []
 
-# def data_ingestion_from_directory():
-#     documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
-#     storage_context = StorageContext.from_defaults()
-#     index = VectorStoreIndex.from_documents(documents)
-#     index.storage_context.persist(persist_dir=PERSIST_DIR)
 def data_ingestion_from_directory():
     # Clear previous data by removing the persist directory
     if os.path.exists(PERSIST_DIR):
-        shutil.rmtree(PERSIST_DIR)  # Remove the persist directory and all its contents
+        shutil.rmtree(PERSIST_DIR)
     
-    # Recreate the persist directory after removal
     os.makedirs(PERSIST_DIR, exist_ok=True)
-    
-    # Load new documents from the directory
     new_documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
     
-    # Create a new index with the new documents
-    index = VectorStoreIndex.from_documents(new_documents)
+    if not new_documents:
+        print("No documents were found or loaded.")
+        return
     
-    # Persist the new index
+    index = VectorStoreIndex.from_documents(new_documents)
     index.storage_context.persist(persist_dir=PERSIST_DIR)
+    print("Persist data cleared and updated with new data.")
 
 def handle_query(query):
     chat_text_qa_msgs = [
-        (
-            "user",
-            """
-            You are the Taj Hotel chatbot and your name is Taj Hotel Helper. Your goal is to provide accurate, professional, and helpful answers to user queries based on the given Taj hotel's data. Always ensure your responses are clear and concise. Give response within 10-15 words only. You need to give an answer in the same language used by the user.       
-            {context_str}
-            Question:
-            {query_str}
-            """
-        )
+        ("user", """
+        You are the Taj Hotel chatbot and your name is Taj Hotel Helper. Your goal is to provide accurate, professional, and helpful answers to user queries based on the given Taj hotel's data. Always ensure your responses are clear and concise. Give response within 10-15 words only. You need to give an answer in the same language used by the user.       
+        {context_str}
+        Question:
+        {query_str}
+        """)
     ]
     text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
     
     storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
     index = load_index_from_storage(storage_context)
+    
     context_str = ""
     for past_query, response in reversed(current_chat_history):
         if past_query.strip():
             context_str += f"User asked: '{past_query}'\nBot answered: '{response}'\n"
 
     query_engine = index.as_query_engine(text_qa_template=text_qa_template, context_str=context_str)
-    print(query)
+    print(f"User query: {query}")
     answer = query_engine.query(query)
 
     if hasattr(answer, 'response'):
@@ -87,43 +81,23 @@ def handle_query(query):
         response = answer['response']
     else:
         response = "Sorry, I couldn't find an answer."
+    
     current_chat_history.append((query, response))
     return response
 
 app = Flask(__name__)
 
-# Initialize Gradio Client once for efficiency
-try:
-    client = Client("Gopikanth123/llama2")  # Replace with your Gradio model URL
-except Exception as e:
-    print(f"Error initializing Gradio client: {str(e)}")
-    client = None
-
-# # Function to fetch the response from Gradio model
-# def generate_response(query):
-#     if client is None:
-#         return "Model is unavailable at the moment. Please try again later."
-#     try:
-#         result = client.predict(query=query, api_name="/predict")
-#         return result
-#     except Exception as e:
-#         return f"Error fetching the response: {str(e)}"
-# Generate Response
 def generate_response(query):
     try:
-        # Call the handle_query function to get the response
         bot_response = handle_query(query)
         return bot_response
     except Exception as e:
         return f"Error fetching the response: {str(e)}"
 
-
-# Route for the homepage
 @app.route('/')
 def index():
     return render_template('index.html')
 
-# Route to handle chatbot messages
 @app.route('/chat', methods=['POST'])
 def chat():
     try: