Update app.py

app.py

@@ -1,659 +1,932 @@
-# import os
-# import time
-# import pandas as pd
-# import gradio as gr
-# from langchain_groq import ChatGroq
-# from langchain_huggingface import HuggingFaceEmbeddings
-# from langchain_community.vectorstores import Chroma
-# from langchain_core.prompts import PromptTemplate
-# from langchain_core.output_parsers import StrOutputParser
-# from langchain_core.runnables import RunnablePassthrough
-# from PyPDF2 import PdfReader
-
-
-# # Configuration constants
-# COLLECTION_NAME = "GBVRS"
-# DATA_FOLDER = "./"
-# APP_VERSION = "v1.0.0"
-# APP_NAME = "Ijwi ry'Ubufasha"
-# MAX_HISTORY_MESSAGES = 8  # Limit history to avoid token limits
-
-# # Global variables for application state
-# llm = None
-# embed_model = None
-# vectorstore = None
-# retriever = None
-# rag_chain = None
-
-# # User session management
-# class UserSession:
-#     def __init__(self, session_id, llm):
-#         """Initialize a user session with unique ID and language model."""
-#         self.session_id = session_id
-#         self.user_info = {"Nickname": "Guest"}
-#         self.conversation_history = []
-#         self.llm = llm
-#         self.welcome_message = None
-#         self.last_activity = time.time()
-        
-#     def set_user(self, user_info):
-#         """Set user information and generate welcome message."""
-#         self.user_info = user_info
-#         self.generate_welcome_message()
-        
-#         # Initialize conversation history with welcome message
-#         welcome = self.get_welcome_message()
-#         self.conversation_history = [
-#             {"role": "assistant", "content": welcome},
-#         ]
-        
-#     def get_user(self):
-#         """Get current user information."""
-#         return self.user_info
-    
-#     def generate_welcome_message(self):
-#         """Generate a dynamic welcome message using the LLM."""
-#         try:
-#             nickname = self.user_info.get("Nickname", "Guest")
+# # import os
+# # import time
+# # import pandas as pd
+# # import gradio as gr
+# # from langchain_groq import ChatGroq
+# # from langchain_huggingface import HuggingFaceEmbeddings
+# # from langchain_community.vectorstores import Chroma
+# # from langchain_core.prompts import PromptTemplate
+# # from langchain_core.output_parsers import StrOutputParser
+# # from langchain_core.runnables import RunnablePassthrough
+# # from PyPDF2 import PdfReader
+
+
+# # # Configuration constants
+# # COLLECTION_NAME = "GBVRS"
+# # DATA_FOLDER = "./"
+# # APP_VERSION = "v1.0.0"
+# # APP_NAME = "Ijwi ry'Ubufasha"
+# # MAX_HISTORY_MESSAGES = 8  # Limit history to avoid token limits
+
+# # # Global variables for application state
+# # llm = None
+# # embed_model = None
+# # vectorstore = None
+# # retriever = None
+# # rag_chain = None
+
+# # # User session management
+# # class UserSession:
+# #     def __init__(self, session_id, llm):
+# #         """Initialize a user session with unique ID and language model."""
+# #         self.session_id = session_id
+# #         self.user_info = {"Nickname": "Guest"}
+# #         self.conversation_history = []
+# #         self.llm = llm
+# #         self.welcome_message = None
+# #         self.last_activity = time.time()
+        
+# #     def set_user(self, user_info):
+# #         """Set user information and generate welcome message."""
+# #         self.user_info = user_info
+# #         self.generate_welcome_message()
+        
+# #         # Initialize conversation history with welcome message
+# #         welcome = self.get_welcome_message()
+# #         self.conversation_history = [
+# #             {"role": "assistant", "content": welcome},
+# #         ]
+        
+# #     def get_user(self):
+# #         """Get current user information."""
+# #         return self.user_info
+    
+# #     def generate_welcome_message(self):
+# #         """Generate a dynamic welcome message using the LLM."""
+# #         try:
+# #             nickname = self.user_info.get("Nickname", "Guest")
             
-#             # Use the LLM to generate the message
-#             prompt = (
-#                 f"Create a brief and warm welcome message for {nickname} that's about 1-2 sentences. "
-#                 f"Emphasize this is a safe space for discussing gender-based violence issues "
-#                 f"and that we provide support and resources. Keep it warm and reassuring."
-#             )
+# #             # Use the LLM to generate the message
+# #             prompt = (
+# #                 f"Create a brief and warm welcome message for {nickname} that's about 1-2 sentences. "
+# #                 f"Emphasize this is a safe space for discussing gender-based violence issues "
+# #                 f"and that we provide support and resources. Keep it warm and reassuring."
+# #             )
             
-#             response = self.llm.invoke(prompt)
-#             welcome = response.content.strip()
+# #             response = self.llm.invoke(prompt)
+# #             welcome = response.content.strip()
             
-#             # Format the message with HTML styling
-#             self.welcome_message = (
-#                 f"<div style='font-size: 18px; color: #4E6BBF;'>"
-#                 f"{welcome}"
-#                 f"</div>"
-#             )
-#         except Exception as e:
-#             # Fallback welcome message
-#             nickname = self.user_info.get("Nickname", "Guest")
-#             self.welcome_message = (
-#                 f"<div style='font-size: 18px; color: #4E6BBF;'>"
-#                 f"Welcome, {nickname}! You're in a safe space. We're here to provide support with "
-#                 f"gender-based violence issues and connect you with resources that can help."
-#                 f"</div>"
-#             )
-    
-#     def get_welcome_message(self):
-#         """Get the formatted welcome message."""
-#         if not self.welcome_message:
-#             self.generate_welcome_message()
-#         return self.welcome_message
-    
-#     def add_to_history(self, role, message):
-#         """Add a message to the conversation history."""
-#         self.conversation_history.append({"role": role, "content": message})
-#         self.last_activity = time.time()
-        
-#         # Trim history if it gets too long
-#         if len(self.conversation_history) > MAX_HISTORY_MESSAGES * 2:  # Keep pairs of messages
-#             # Keep the first message (welcome) and the most recent messages
-#             self.conversation_history = [self.conversation_history[0]] + self.conversation_history[-MAX_HISTORY_MESSAGES*2+1:]
-    
-#     def get_conversation_history(self):
-#         """Get the full conversation history."""
-#         return self.conversation_history
-    
-#     def get_formatted_history(self):
-#         """Get conversation history formatted as a string for the LLM."""
-#         # Skip the welcome message and only include the last few exchanges
-#         recent_history = self.conversation_history[1:] if len(self.conversation_history) > 1 else []
-        
-#         # Limit to last MAX_HISTORY_MESSAGES exchanges
-#         if len(recent_history) > MAX_HISTORY_MESSAGES * 2:
-#             recent_history = recent_history[-MAX_HISTORY_MESSAGES*2:]
+# #             # Format the message with HTML styling
+# #             self.welcome_message = (
+# #                 f"<div style='font-size: 18px; color: #4E6BBF;'>"
+# #                 f"{welcome}"
+# #                 f"</div>"
+# #             )
+# #         except Exception as e:
+# #             # Fallback welcome message
+# #             nickname = self.user_info.get("Nickname", "Guest")
+# #             self.welcome_message = (
+# #                 f"<div style='font-size: 18px; color: #4E6BBF;'>"
+# #                 f"Welcome, {nickname}! You're in a safe space. We're here to provide support with "
+# #                 f"gender-based violence issues and connect you with resources that can help."
+# #                 f"</div>"
+# #             )
+    
+# #     def get_welcome_message(self):
+# #         """Get the formatted welcome message."""
+# #         if not self.welcome_message:
+# #             self.generate_welcome_message()
+# #         return self.welcome_message
+    
+# #     def add_to_history(self, role, message):
+# #         """Add a message to the conversation history."""
+# #         self.conversation_history.append({"role": role, "content": message})
+# #         self.last_activity = time.time()
+        
+# #         # Trim history if it gets too long
+# #         if len(self.conversation_history) > MAX_HISTORY_MESSAGES * 2:  # Keep pairs of messages
+# #             # Keep the first message (welcome) and the most recent messages
+# #             self.conversation_history = [self.conversation_history[0]] + self.conversation_history[-MAX_HISTORY_MESSAGES*2+1:]
+    
+# #     def get_conversation_history(self):
+# #         """Get the full conversation history."""
+# #         return self.conversation_history
+    
+# #     def get_formatted_history(self):
+# #         """Get conversation history formatted as a string for the LLM."""
+# #         # Skip the welcome message and only include the last few exchanges
+# #         recent_history = self.conversation_history[1:] if len(self.conversation_history) > 1 else []
+        
+# #         # Limit to last MAX_HISTORY_MESSAGES exchanges
+# #         if len(recent_history) > MAX_HISTORY_MESSAGES * 2:
+# #             recent_history = recent_history[-MAX_HISTORY_MESSAGES*2:]
             
-#         formatted_history = ""
-#         for entry in recent_history:
-#             role = "User" if entry["role"] == "user" else "Assistant"
-#             # Truncate very long messages to avoid token limits
-#             content = entry["content"]
-#             if len(content) > 500:  # Limit message length
-#                 content = content[:500] + "..."
-#             formatted_history += f"{role}: {content}\n\n"
+# #         formatted_history = ""
+# #         for entry in recent_history:
+# #             role = "User" if entry["role"] == "user" else "Assistant"
+# #             # Truncate very long messages to avoid token limits
+# #             content = entry["content"]
+# #             if len(content) > 500:  # Limit message length
+# #                 content = content[:500] + "..."
+# #             formatted_history += f"{role}: {content}\n\n"
             
-#         return formatted_history
-    
-#     def is_expired(self, timeout_seconds=3600):
-#         """Check if the session has been inactive for too long."""
-#         return (time.time() - self.last_activity) > timeout_seconds
-
-# # Session manager to handle multiple users
-# class SessionManager:
-#     def __init__(self):
-#         """Initialize the session manager."""
-#         self.sessions = {}
-#         self.session_timeout = 3600  # 1 hour timeout
-        
-#     def get_session(self, session_id):
-#         """Get an existing session or create a new one."""
-#         # Clean expired sessions first
-#         self._clean_expired_sessions()
-        
-#         # Create new session if needed
-#         if session_id not in self.sessions:
-#             self.sessions[session_id] = UserSession(session_id, llm)
+# #         return formatted_history
+    
+# #     def is_expired(self, timeout_seconds=3600):
+# #         """Check if the session has been inactive for too long."""
+# #         return (time.time() - self.last_activity) > timeout_seconds
+
+# # # Session manager to handle multiple users
+# # class SessionManager:
+# #     def __init__(self):
+# #         """Initialize the session manager."""
+# #         self.sessions = {}
+# #         self.session_timeout = 3600  # 1 hour timeout
+        
+# #     def get_session(self, session_id):
+# #         """Get an existing session or create a new one."""
+# #         # Clean expired sessions first
+# #         self._clean_expired_sessions()
+        
+# #         # Create new session if needed
+# #         if session_id not in self.sessions:
+# #             self.sessions[session_id] = UserSession(session_id, llm)
             
-#         return self.sessions[session_id]
-    
-#     def _clean_expired_sessions(self):
-#         """Remove expired sessions to free up memory."""
-#         expired_keys = []
-#         for key, session in self.sessions.items():
-#             if session.is_expired(self.session_timeout):
-#                 expired_keys.append(key)
+# #         return self.sessions[session_id]
+    
+# #     def _clean_expired_sessions(self):
+# #         """Remove expired sessions to free up memory."""
+# #         expired_keys = []
+# #         for key, session in self.sessions.items():
+# #             if session.is_expired(self.session_timeout):
+# #                 expired_keys.append(key)
                 
-#         for key in expired_keys:
-#             del self.sessions[key]
+# #         for key in expired_keys:
+# #             del self.sessions[key]
 
-# # Initialize the session manager
-# session_manager = SessionManager()
+# # # Initialize the session manager
+# # session_manager = SessionManager()
 
-# def initialize_assistant():
-#     """Initialize the assistant with necessary components and configurations."""
-#     global llm, embed_model, vectorstore, retriever, rag_chain
+# # def initialize_assistant():
+# #     """Initialize the assistant with necessary components and configurations."""
+# #     global llm, embed_model, vectorstore, retriever, rag_chain
     
-#     # Initialize API key - try both possible key names
-#     groq_api_key = os.environ.get('GBV') or os.environ.get('GBV')
-#     if not groq_api_key:
-#         print("WARNING: No GROQ API key found in userdata.")
+# #     # Initialize API key - try both possible key names
+# #     groq_api_key = os.environ.get('GBV') or os.environ.get('GBV')
+# #     if not groq_api_key:
+# #         print("WARNING: No GROQ API key found in userdata.")
         
-#     # Initialize LLM - Default to Llama model which is more widely available
-#     llm = ChatGroq(
-#         model="llama-3.3-70b-versatile",  # More reliable than whisper model
-#         api_key=groq_api_key
-#     )
+# #     # Initialize LLM - Default to Llama model which is more widely available
+# #     llm = ChatGroq(
+# #         model="llama-3.3-70b-versatile",  # More reliable than whisper model
+# #         api_key=groq_api_key
+# #     )
     
-#     # Set up embedding model
-#     try:
-#         embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
-#     except Exception as e:
-#         # Fallback to smaller model
-#         embed_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+# #     # Set up embedding model
+# #     try:
+# #         embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
+# #     except Exception as e:
+# #         # Fallback to smaller model
+# #         embed_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
         
-#     # Process data and create vector store
-#     print("Processing data files...")
-#     data = process_data_files()
+# #     # Process data and create vector store
+# #     print("Processing data files...")
+# #     data = process_data_files()
     
-#     print("Creating vector store...")
-#     vectorstore = create_vectorstore(data)
-#     retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
+# #     print("Creating vector store...")
+# #     vectorstore = create_vectorstore(data)
+# #     retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
     
-#     # Create RAG chain
-#     print("Setting up RAG chain...")
-#     rag_chain = create_rag_chain()
+# #     # Create RAG chain
+# #     print("Setting up RAG chain...")
+# #     rag_chain = create_rag_chain()
     
-#     print(f"✅ {APP_NAME} initialized successfully")
+# #     print(f"✅ {APP_NAME} initialized successfully")
 
-# def process_data_files():
-#     """Process all data files from the specified folder."""
-#     context_data = []
+# # def process_data_files():
+# #     """Process all data files from the specified folder."""
+# #     context_data = []
     
-#     try:
-#         if not os.path.exists(DATA_FOLDER):
-#             print(f"WARNING: Data folder does not exist: {DATA_FOLDER}")
-#             return context_data
+# #     try:
+# #         if not os.path.exists(DATA_FOLDER):
+# #             print(f"WARNING: Data folder does not exist: {DATA_FOLDER}")
+# #             return context_data
             
-#         # Get list of data files
-#         all_files = os.listdir(DATA_FOLDER)
-#         data_files = [f for f in all_files if f.lower().endswith(('.csv', '.xlsx', '.xls'))]
+# #         # Get list of data files
+# #         all_files = os.listdir(DATA_FOLDER)
+# #         data_files = [f for f in all_files if f.lower().endswith(('.csv', '.xlsx', '.xls'))]
         
-#         if not data_files:
-#             print(f"WARNING: No data files found in: {DATA_FOLDER}")
-#             return context_data
+# #         if not data_files:
+# #             print(f"WARNING: No data files found in: {DATA_FOLDER}")
+# #             return context_data
             
-#         # Process each file
-#         for index, file_name in enumerate(data_files, 1):
-#             print(f"Processing file {index}/{len(data_files)}: {file_name}")
-#             file_path = os.path.join(DATA_FOLDER, file_name)
+# #         # Process each file
+# #         for index, file_name in enumerate(data_files, 1):
+# #             print(f"Processing file {index}/{len(data_files)}: {file_name}")
+# #             file_path = os.path.join(DATA_FOLDER, file_name)
             
-#             try:
-#                 # Read file based on extension
-#                 if file_name.lower().endswith('.csv'):
-#                     df = pd.read_csv(file_path)
-#                 else:
-#                     df = pd.read_excel(file_path)
+# #             try:
+# #                 # Read file based on extension
+# #                 if file_name.lower().endswith('.csv'):
+# #                     df = pd.read_csv(file_path)
+# #                 else:
+# #                     df = pd.read_excel(file_path)
                 
-#                 # Check if column 3 exists (source data is in third column)
-#                 if df.shape[1] > 2:
-#                     column_data = df.iloc[:, 2].dropna().astype(str).tolist()
+# #                 # Check if column 3 exists (source data is in third column)
+# #                 if df.shape[1] > 2:
+# #                     column_data = df.iloc[:, 2].dropna().astype(str).tolist()
                     
-#                     # Each row becomes one chunk with metadata
-#                     for i, text in enumerate(column_data):
-#                         if text and len(text.strip()) > 0:
-#                             context_data.append({
-#                                 "page_content": text, 
-#                                 "metadata": {
-#                                     "source": file_name, 
-#                                     "row": i+1
-#                                 }
-#                             })
-#                 else:
-#                     print(f"WARNING: File {file_name} has fewer than 3 columns.")
+# #                     # Each row becomes one chunk with metadata
+# #                     for i, text in enumerate(column_data):
+# #                         if text and len(text.strip()) > 0:
+# #                             context_data.append({
+# #                                 "page_content": text, 
+# #                                 "metadata": {
+# #                                     "source": file_name, 
+# #                                     "row": i+1
+# #                                 }
+# #                             })
+# #                 else:
+# #                     print(f"WARNING: File {file_name} has fewer than 3 columns.")
                     
-#             except Exception as e:
-#                 print(f"ERROR processing file {file_name}: {e}")
+# #             except Exception as e:
+# #                 print(f"ERROR processing file {file_name}: {e}")
         
-#         print(f"✅ Created {len(context_data)} chunks from {len(data_files)} files.")
+# #         print(f"✅ Created {len(context_data)} chunks from {len(data_files)} files.")
         
-#     except Exception as e:
-#         print(f"ERROR accessing data folder: {e}")
+# #     except Exception as e:
+# #         print(f"ERROR accessing data folder: {e}")
         
-#     return context_data
+# #     return context_data
     
-# def create_vectorstore(data):
-#     """
-#     Creates and returns a Chroma vector store populated with the provided data.
-
-#     Parameters:
-#         data (list): A list of dictionaries, each containing 'page_content' and 'metadata'.
-
-#     Returns:
-#         Chroma: The populated Chroma vector store instance.
-#     """
-#     # Initialize the vector store
-#     vectorstore = Chroma(
-#         collection_name=COLLECTION_NAME,
-#         embedding_function=embed_model,
-#         persist_directory="./"
-#     )
-
-#     if not data:
-#         print("⚠️ No data provided. Returning an empty vector store.")
-#         return vectorstore
-
-#     try:
-#         # Extract text and metadata from the data
-#         texts = [doc["page_content"] for doc in data]
-
-#         # Add the texts and metadata to the vector store
-#         vectorstore.add_texts(texts)
-#     except Exception as e:
-#         print(f"❌ Failed to add documents to vector store: {e}")
-
-#     # Fix: Return vectorstore instead of vs
-#     return vectorstore  # Changed from 'return vs' to 'return vectorstore'
-
-
-# def create_rag_chain():
-#     """Create the RAG chain for processing user queries."""
-#     # Define the prompt template
-#     template = """
-#      You are a compassionate and supportive AI assistant specializing in helping individuals affected by Gender-Based Violence (GBV). Your responses must be based EXCLUSIVELY on the information provided in the context. Your primary goal is to provide emotionally intelligent support while maintaining appropriate boundaries.
+# # def create_vectorstore(data):
+# #     """
+# #     Creates and returns a Chroma vector store populated with the provided data.
+
+# #     Parameters:
+# #         data (list): A list of dictionaries, each containing 'page_content' and 'metadata'.
+
+# #     Returns:
+# #         Chroma: The populated Chroma vector store instance.
+# #     """
+# #     # Initialize the vector store
+# #     vectorstore = Chroma(
+# #         collection_name=COLLECTION_NAME,
+# #         embedding_function=embed_model,
+# #         persist_directory="./"
+# #     )
+
+# #     if not data:
+# #         print("⚠️ No data provided. Returning an empty vector store.")
+# #         return vectorstore
+
+# #     try:
+# #         # Extract text and metadata from the data
+# #         texts = [doc["page_content"] for doc in data]
+
+# #         # Add the texts and metadata to the vector store
+# #         vectorstore.add_texts(texts)
+# #     except Exception as e:
+# #         print(f"❌ Failed to add documents to vector store: {e}")
+
+# #     # Fix: Return vectorstore instead of vs
+# #     return vectorstore  # Changed from 'return vs' to 'return vectorstore'
+
+
+# # def create_rag_chain():
+# #     """Create the RAG chain for processing user queries."""
+# #     # Define the prompt template
+# #     template = """
+# #      You are a compassionate and supportive AI assistant specializing in helping individuals affected by Gender-Based Violence (GBV). Your responses must be based EXCLUSIVELY on the information provided in the context. Your primary goal is to provide emotionally intelligent support while maintaining appropriate boundaries.
         
-#         **Previous conversation:** {conversation_history}
-#         **Context information:** {context}
-#         **User's Question:** {question}
+# #         **Previous conversation:** {conversation_history}
+# #         **Context information:** {context}
+# #         **User's Question:** {question}
         
-#         When responding follow these guidelines:
+# #         When responding follow these guidelines:
         
-#         1. **Strict Context Adherence**
-#            - Only use information that appears in the provided {context}
-#            - If the answer is not found in the context, state "I don't have that information in my available resources" rather than generating a response
+# #         1. **Strict Context Adherence**
+# #            - Only use information that appears in the provided {context}
+# #            - If the answer is not found in the context, state "I don't have that information in my available resources" rather than generating a response
         
-#         2. **Personalized Communication**
-#            - Avoid contractions (e.g., use I am instead of I'm)
-#            - Incorporate thoughtful pauses or reflective questions when the conversation involves difficult topics
-#            - Use selective emojis (😊, 🤗, ❤️) only when tone-appropriate and not during crisis discussions
-#            - Balance warmth with professionalism
+# #         2. **Personalized Communication**
+# #            - Avoid contractions (e.g., use I am instead of I'm)
+# #            - Incorporate thoughtful pauses or reflective questions when the conversation involves difficult topics
+# #            - Use selective emojis (😊, 🤗, ❤️) only when tone-appropriate and not during crisis discussions
+# #            - Balance warmth with professionalism
                 
-#         3. **Emotional Intelligence** 
-#            - Validate feelings without judgment 
-#            - Offer reassurance when appropriate, always centered on empowerment
-#            - Adjust your tone based on the emotional state conveyed
+# #         3. **Emotional Intelligence** 
+# #            - Validate feelings without judgment 
+# #            - Offer reassurance when appropriate, always centered on empowerment
+# #            - Adjust your tone based on the emotional state conveyed
             
-#         4. **Conversation Management**
-#            - Refer to {conversation_history} to maintain continuity and avoid repetition
-#            - Use clear paragraph breaks for readability
+# #         4. **Conversation Management**
+# #            - Refer to {conversation_history} to maintain continuity and avoid repetition
+# #            - Use clear paragraph breaks for readability
                 
-#         5. **Information Delivery**
-#            - Extract only relevant information from {context} that directly addresses the question
-#            - Present information in accessible, non-technical language
-#            - When information is unavailable, respond with: "I don't have that specific information right now, {first_name}. Would it be helpful if I focus on [alternative support option]?"
-        
-#         6. **Safety and Ethics**
-#            - Do not generate any speculative content or advice not supported by the context
-#            - If the context contains safety information, prioritize sharing that information
-        
-#         Your response must come entirely from the provided context, maintaining the supportive tone while never introducing information from outside the provided materials.
-#         **Context:** {context}
-#         **User's Question:** {question}
-#         **Your Response:**
-#     """
-
-    
-#     rag_prompt = PromptTemplate.from_template(template)
-    
-#     def get_context_and_question(query_with_session):
-#         # Extract query and session_id
-#         query = query_with_session["query"]
-#         session_id = query_with_session["session_id"]
-        
-#         # Get the user session
-#         session = session_manager.get_session(session_id)
-#         user_info = session.get_user()
-#         first_name = user_info.get("Nickname", "User")
-#         conversation_hist = session.get_formatted_history()
-        
-#         try:
-#             # Retrieve relevant documents
-#             retrieved_docs = retriever.invoke(query)
-#             context_str = format_context(retrieved_docs)
-#         except Exception as e:
-#             print(f"ERROR retrieving documents: {e}")
-#             context_str = "No relevant information found."
-        
-#         # Return the combined inputs for the prompt
-#         return {
-#             "context": context_str,
-#             "question": query,
-#             "first_name": first_name,
-#             "conversation_history": conversation_hist
-#         }
-    
-#     # Build the chain
-#     try:
-#         chain = (
-#             RunnablePassthrough() 
-#             | get_context_and_question 
-#             | rag_prompt 
-#             | llm 
-#             | StrOutputParser()
-#         )
-#         return chain
-#     except Exception as e:
-#         print(f"ERROR creating RAG chain: {e}")
-        
-#         # Return a simple function as fallback
-#         def fallback_chain(query_with_session):
-#             session_id = query_with_session["session_id"]
-#             session = session_manager.get_session(session_id)
-#             nickname = session.get_user().get("Nickname", "there")
-#             return f"I'm here to help you, {nickname}, but I'm experiencing some technical difficulties right now. Please try again shortly."
-            
-#         return fallback_chain
-
-# def format_context(retrieved_docs):
-#     """Format retrieved documents into a string context."""
-#     if not retrieved_docs:
-#         return "No relevant information available."
-#     return "\n\n".join([doc.page_content for doc in retrieved_docs])
+# #         5. **Information Delivery**
+# #            - Extract only relevant information from {context} that directly addresses the question
+# #            - Present information in accessible, non-technical language
+# #            - When information is unavailable, respond with: "I don't have that specific information right now, {first_name}. Would it be helpful if I focus on [alternative support option]?"
+        
+# #         6. **Safety and Ethics**
+# #            - Do not generate any speculative content or advice not supported by the context
+# #            - If the context contains safety information, prioritize sharing that information
+        
+# #         Your response must come entirely from the provided context, maintaining the supportive tone while never introducing information from outside the provided materials.
+# #         **Context:** {context}
+# #         **User's Question:** {question}
+# #         **Your Response:**
+# #     """
 
-# def rag_memory_stream(message, history, session_id):
-#     """Process user message and generate response with memory."""
-#     # Get the user session
-#     session = session_manager.get_session(session_id)
     
-#     # Add user message to history
-#     session.add_to_history("user", message)
+# #     rag_prompt = PromptTemplate.from_template(template)
+    
+# #     def get_context_and_question(query_with_session):
+# #         # Extract query and session_id
+# #         query = query_with_session["query"]
+# #         session_id = query_with_session["session_id"]
+        
+# #         # Get the user session
+# #         session = session_manager.get_session(session_id)
+# #         user_info = session.get_user()
+# #         first_name = user_info.get("Nickname", "User")
+# #         conversation_hist = session.get_formatted_history()
+        
+# #         try:
+# #             # Retrieve relevant documents
+# #             retrieved_docs = retriever.invoke(query)
+# #             context_str = format_context(retrieved_docs)
+# #         except Exception as e:
+# #             print(f"ERROR retrieving documents: {e}")
+# #             context_str = "No relevant information found."
+        
+# #         # Return the combined inputs for the prompt
+# #         return {
+# #             "context": context_str,
+# #             "question": query,
+# #             "first_name": first_name,
+# #             "conversation_history": conversation_hist
+# #         }
+    
+# #     # Build the chain
+# #     try:
+# #         chain = (
+# #             RunnablePassthrough() 
+# #             | get_context_and_question 
+# #             | rag_prompt 
+# #             | llm 
+# #             | StrOutputParser()
+# #         )
+# #         return chain
+# #     except Exception as e:
+# #         print(f"ERROR creating RAG chain: {e}")
+        
+# #         # Return a simple function as fallback
+# #         def fallback_chain(query_with_session):
+# #             session_id = query_with_session["session_id"]
+# #             session = session_manager.get_session(session_id)
+# #             nickname = session.get_user().get("Nickname", "there")
+# #             return f"I'm here to help you, {nickname}, but I'm experiencing some technical difficulties right now. Please try again shortly."
+            
+# #         return fallback_chain
+
+# # def format_context(retrieved_docs):
+# #     """Format retrieved documents into a string context."""
+# #     if not retrieved_docs:
+# #         return "No relevant information available."
+# #     return "\n\n".join([doc.page_content for doc in retrieved_docs])
+
+# # def rag_memory_stream(message, history, session_id):
+# #     """Process user message and generate response with memory."""
+# #     # Get the user session
+# #     session = session_manager.get_session(session_id)
+    
+# #     # Add user message to history
+# #     session.add_to_history("user", message)
 
-#     try:
-#         # Get response from RAG chain
-#         print(f"Processing message for session {session_id}: {message[:50]}...")
+# #     try:
+# #         # Get response from RAG chain
+# #         print(f"Processing message for session {session_id}: {message[:50]}...")
         
-#         # Pass both query and session_id to the chain
-#         response = rag_chain.invoke({
-#             "query": message,
-#             "session_id": session_id
-#         })
+# #         # Pass both query and session_id to the chain
+# #         response = rag_chain.invoke({
+# #             "query": message,
+# #             "session_id": session_id
+# #         })
         
-#         print(f"Generated response: {response[:50]}...")
+# #         print(f"Generated response: {response[:50]}...")
         
-#         # Add assistant response to history
-#         session.add_to_history("assistant", response)
+# #         # Add assistant response to history
+# #         session.add_to_history("assistant", response)
         
-#         # Yield the response
-#         yield response
+# #         # Yield the response
+# #         yield response
         
-#     except Exception as e:
-#         import traceback
-#         print(f"ERROR in rag_memory_stream: {e}")
-#         print(f"Detailed error: {traceback.format_exc()}")
-        
-#         nickname = session.get_user().get("Nickname", "there")
-#         error_msg = f"I'm sorry, {nickname}. I encountered an error processing your request. Let's try a different question."
-#         session.add_to_history("assistant", error_msg)
-#         yield error_msg
-
-# def collect_user_info(nickname, session_id):
-#     """Store user details and initialize session."""
-#     if not nickname or nickname.strip() == "":
-#         return "Nickname is required to proceed.", gr.update(visible=False), gr.update(visible=True), []
-
-#     # Store user info for chat session
-#     user_info = {
-#         "Nickname": nickname.strip(),
-#         "timestamp": time.strftime("%Y-%m-%d %H:%M:%S")
-#     }
-
-#     # Get the session and set user info
-#     session = session_manager.get_session(session_id)
-#     session.set_user(user_info)
-
-#     # Generate welcome message
-#     welcome_message = session.get_welcome_message()
-
-#     # Return welcome message and update UI
-#     return welcome_message, gr.update(visible=True), gr.update(visible=False), [(None, welcome_message)]
-
-# def get_css():
-#     """Define CSS for the UI."""
-#     return """
-#     :root {
-#         --primary: #4E6BBF;
-#         --primary-light: #697BBF;
-#         --text-primary: #333333;
-#         --text-secondary: #666666;
-#         --background: #F9FAFC;
-#         --card-bg: #FFFFFF;
-#         --border: #E1E5F0;
-#         --shadow: rgba(0, 0, 0, 0.05);
-#     }
-
-#     body, .gradio-container {
-#         margin: 0;
-#         padding: 0;
-#         width: 100vw;
-#         height: 100vh;
-#         display: flex;
-#         flex-direction: column;
-#         justify-content: center;
-#         align-items: center;
-#         background: var(--background);
-#         color: var(--text-primary);
-#         font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-#     }
-
-#     .gradio-container {
-#         max-width: 100%;
-#         max-height: 100%;
-#     }
-
-#     .gr-box {
-#         background: var(--card-bg);
-#         color: var(--text-primary);
-#         border-radius: 12px;
-#         padding: 2rem;
-#         border: 1px solid var(--border);
-#         box-shadow: 0 4px 12px var(--shadow);
-#     }
-
-#     .gr-button-primary {
-#         background: var(--primary);
-#         color: white;
-#         padding: 12px 24px;
-#         border-radius: 8px;
-#         transition: all 0.3s ease;
-#         border: none;
-#         font-weight: bold;
-#     }
-
-#     .gr-button-primary:hover {
-#         transform: translateY(-1px);
-#         box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
-#         background: var(--primary-light);
-#     }
-
-#     footer {
-#         text-align: center;
-#         color: var(--text-secondary);
-#         padding: 1rem;
-#         font-size: 0.9em;
-#     }
-
-#     .gr-markdown h2 {
-#         color: var(--primary);
-#         margin-bottom: 0.5rem;
-#         font-size: 1.8em;
-#     }
-
-#     .gr-markdown h3 {
-#         color: var(--text-secondary);
-#         margin-bottom: 1.5rem;
-#         font-weight: normal;
-#     }
-
-#     #chatbot_container .chat-title h1, 
-#     #chatbot_container .empty-chatbot {
-#         color: var(--primary);
-#     }
-
-#     #input_nickname {
-#         padding: 12px;
-#         border-radius: 8px;
-#         border: 1px solid var(--border);
-#         background: var(--card-bg);
-#         transition: all 0.3s ease;
-#     }
-    
-#     #input_nickname:focus {
-#         border-color: var(--primary);
-#         box-shadow: 0 0 0 2px rgba(78, 107, 191, 0.2);
-#         outline: none;
-#     }
-
-#     .chatbot-container .message.user {
-#         background: #E8F0FE;
-#         border-radius: 12px 12px 0 12px;
-#     }
-
-#     .chatbot-container .message.bot {
-#         background: #F5F7FF;
-#         border-radius: 12px 12px 12px 0;
-#     }
-#     """
-
-# def create_ui():
-#     """Create and configure the Gradio UI."""
-#     with gr.Blocks(css=get_css(), theme=gr.themes.Soft()) as demo:
-#         # Create a unique session ID for this browser tab
-#         session_id = gr.State(value=f"session_{int(time.time())}_{os.urandom(4).hex()}")
-        
-#         # Registration section
-#         with gr.Column(visible=True, elem_id="registration_container") as registration_container:
-#             gr.Markdown(f"## Welcome to {APP_NAME}")
-#             gr.Markdown("### Your privacy is important to us. Please provide a nickname to continue.")
-
-#             with gr.Row():
-#                 first_name = gr.Textbox(
-#                     label="Nickname",
-#                     placeholder="Enter your nickname",
-#                     scale=1,
-#                     elem_id="input_nickname"
-#                 )
-
-#             with gr.Row():
-#                 submit_btn = gr.Button("Start Chatting", variant="primary", scale=2)
-
-#             response_message = gr.Markdown()
-
-#         # Chatbot section (initially hidden)
-#         with gr.Column(visible=False, elem_id="chatbot_container") as chatbot_container:
-#             # Create a custom chat interface to pass session_id to our function
-#             chatbot = gr.Chatbot(
-#                 elem_id="chatbot", 
-#                 height=500,
-#                 show_label=False
-#             )
+# #     except Exception as e:
+# #         import traceback
+# #         print(f"ERROR in rag_memory_stream: {e}")
+# #         print(f"Detailed error: {traceback.format_exc()}")
+        
+# #         nickname = session.get_user().get("Nickname", "there")
+# #         error_msg = f"I'm sorry, {nickname}. I encountered an error processing your request. Let's try a different question."
+# #         session.add_to_history("assistant", error_msg)
+# #         yield error_msg
+
+# # def collect_user_info(nickname, session_id):
+# #     """Store user details and initialize session."""
+# #     if not nickname or nickname.strip() == "":
+# #         return "Nickname is required to proceed.", gr.update(visible=False), gr.update(visible=True), []
+
+# #     # Store user info for chat session
+# #     user_info = {
+# #         "Nickname": nickname.strip(),
+# #         "timestamp": time.strftime("%Y-%m-%d %H:%M:%S")
+# #     }
+
+# #     # Get the session and set user info
+# #     session = session_manager.get_session(session_id)
+# #     session.set_user(user_info)
+
+# #     # Generate welcome message
+# #     welcome_message = session.get_welcome_message()
+
+# #     # Return welcome message and update UI
+# #     return welcome_message, gr.update(visible=True), gr.update(visible=False), [(None, welcome_message)]
+
+# # def get_css():
+# #     """Define CSS for the UI."""
+# #     return """
+# #     :root {
+# #         --primary: #4E6BBF;
+# #         --primary-light: #697BBF;
+# #         --text-primary: #333333;
+# #         --text-secondary: #666666;
+# #         --background: #F9FAFC;
+# #         --card-bg: #FFFFFF;
+# #         --border: #E1E5F0;
+# #         --shadow: rgba(0, 0, 0, 0.05);
+# #     }
+
+# #     body, .gradio-container {
+# #         margin: 0;
+# #         padding: 0;
+# #         width: 100vw;
+# #         height: 100vh;
+# #         display: flex;
+# #         flex-direction: column;
+# #         justify-content: center;
+# #         align-items: center;
+# #         background: var(--background);
+# #         color: var(--text-primary);
+# #         font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+# #     }
+
+# #     .gradio-container {
+# #         max-width: 100%;
+# #         max-height: 100%;
+# #     }
+
+# #     .gr-box {
+# #         background: var(--card-bg);
+# #         color: var(--text-primary);
+# #         border-radius: 12px;
+# #         padding: 2rem;
+# #         border: 1px solid var(--border);
+# #         box-shadow: 0 4px 12px var(--shadow);
+# #     }
+
+# #     .gr-button-primary {
+# #         background: var(--primary);
+# #         color: white;
+# #         padding: 12px 24px;
+# #         border-radius: 8px;
+# #         transition: all 0.3s ease;
+# #         border: none;
+# #         font-weight: bold;
+# #     }
+
+# #     .gr-button-primary:hover {
+# #         transform: translateY(-1px);
+# #         box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
+# #         background: var(--primary-light);
+# #     }
+
+# #     footer {
+# #         text-align: center;
+# #         color: var(--text-secondary);
+# #         padding: 1rem;
+# #         font-size: 0.9em;
+# #     }
+
+# #     .gr-markdown h2 {
+# #         color: var(--primary);
+# #         margin-bottom: 0.5rem;
+# #         font-size: 1.8em;
+# #     }
+
+# #     .gr-markdown h3 {
+# #         color: var(--text-secondary);
+# #         margin-bottom: 1.5rem;
+# #         font-weight: normal;
+# #     }
+
+# #     #chatbot_container .chat-title h1, 
+# #     #chatbot_container .empty-chatbot {
+# #         color: var(--primary);
+# #     }
+
+# #     #input_nickname {
+# #         padding: 12px;
+# #         border-radius: 8px;
+# #         border: 1px solid var(--border);
+# #         background: var(--card-bg);
+# #         transition: all 0.3s ease;
+# #     }
+    
+# #     #input_nickname:focus {
+# #         border-color: var(--primary);
+# #         box-shadow: 0 0 0 2px rgba(78, 107, 191, 0.2);
+# #         outline: none;
+# #     }
+
+# #     .chatbot-container .message.user {
+# #         background: #E8F0FE;
+# #         border-radius: 12px 12px 0 12px;
+# #     }
+
+# #     .chatbot-container .message.bot {
+# #         background: #F5F7FF;
+# #         border-radius: 12px 12px 12px 0;
+# #     }
+# #     """
+
+# # def create_ui():
+# #     """Create and configure the Gradio UI."""
+# #     with gr.Blocks(css=get_css(), theme=gr.themes.Soft()) as demo:
+# #         # Create a unique session ID for this browser tab
+# #         session_id = gr.State(value=f"session_{int(time.time())}_{os.urandom(4).hex()}")
+        
+# #         # Registration section
+# #         with gr.Column(visible=True, elem_id="registration_container") as registration_container:
+# #             gr.Markdown(f"## Welcome to {APP_NAME}")
+# #             gr.Markdown("### Your privacy is important to us. Please provide a nickname to continue.")
+
+# #             with gr.Row():
+# #                 first_name = gr.Textbox(
+# #                     label="Nickname",
+# #                     placeholder="Enter your nickname",
+# #                     scale=1,
+# #                     elem_id="input_nickname"
+# #                 )
+
+# #             with gr.Row():
+# #                 submit_btn = gr.Button("Start Chatting", variant="primary", scale=2)
+
+# #             response_message = gr.Markdown()
+
+# #         # Chatbot section (initially hidden)
+# #         with gr.Column(visible=False, elem_id="chatbot_container") as chatbot_container:
+# #             # Create a custom chat interface to pass session_id to our function
+# #             chatbot = gr.Chatbot(
+# #                 elem_id="chatbot", 
+# #                 height=500,
+# #                 show_label=False
+# #             )
+            
+# #             with gr.Row():
+# #                 msg = gr.Textbox(
+# #                     placeholder="Type your message here...",
+# #                     show_label=False,
+# #                     container=False,
+# #                     scale=9
+# #                 )
+# #                 submit = gr.Button("Send", scale=1, variant="primary")
             
-#             with gr.Row():
-#                 msg = gr.Textbox(
-#                     placeholder="Type your message here...",
-#                     show_label=False,
-#                     container=False,
-#                     scale=9
-#                 )
-#                 submit = gr.Button("Send", scale=1, variant="primary")
+# #             examples = gr.Examples(
+# #                 examples=[
+# #                     "What resources are available for GBV victims?",
+# #                     "How can I report an incident?",
+# #                     "What are my legal rights?",
+# #                     "I need help, what should I do first?"
+# #                 ],
+# #                 inputs=msg
+# #             )
+
+# #             # Footer with version info
+# #             gr.Markdown(f"{APP_NAME} {APP_VERSION} © 2025")
             
-#             examples = gr.Examples(
-#                 examples=[
-#                     "What resources are available for GBV victims?",
-#                     "How can I report an incident?",
-#                     "What are my legal rights?",
-#                     "I need help, what should I do first?"
-#                 ],
-#                 inputs=msg
-#             )
-
-#             # Footer with version info
-#             gr.Markdown(f"{APP_NAME} {APP_VERSION} © 2025")
+# #             # Handle chat message submission
+# #             def respond(message, chat_history, session_id):
+# #                 bot_message = ""
+# #                 for chunk in rag_memory_stream(message, chat_history, session_id):
+# #                     bot_message += chunk
+# #                 chat_history.append((message, bot_message))
+# #                 return "", chat_history
             
-#             # Handle chat message submission
-#             def respond(message, chat_history, session_id):
-#                 bot_message = ""
-#                 for chunk in rag_memory_stream(message, chat_history, session_id):
-#                     bot_message += chunk
-#                 chat_history.append((message, bot_message))
-#                 return "", chat_history
+# #             msg.submit(respond, [msg, chatbot, session_id], [msg, chatbot])
+# #             submit.click(respond, [msg, chatbot, session_id], [msg, chatbot])
+
+# #         # Handle user registration
+# #         submit_btn.click(
+# #             collect_user_info,
+# #             inputs=[first_name, session_id],
+# #             outputs=[response_message, chatbot_container, registration_container, chatbot]
+# #         )
+
+# #     return demo
+
+# # def launch_app():
+# #     """Launch the Gradio interface."""
+# #     ui = create_ui()
+# #     ui.launch(share=True)
+
+# # # Main execution
+# # if __name__ == "__main__":
+# #     try:
+# #         # Initialize and launch the assistant
+# #         initialize_assistant()
+# #         launch_app()
+# #     except Exception as e:
+# #         import traceback
+# #         print(f"❌ Fatal error initializing GBV Assistant: {e}")
+# #         print(traceback.format_exc())
+        
+# #         # Create a minimal emergency UI to display the error
+# #         with gr.Blocks() as error_demo:
+# #             gr.Markdown("## System Error")
+# #             gr.Markdown(f"An error occurred while initializing the application: {str(e)}")
+# #             gr.Markdown("Please check your configuration and try again.")
             
-#             msg.submit(respond, [msg, chatbot, session_id], [msg, chatbot])
-#             submit.click(respond, [msg, chatbot, session_id], [msg, chatbot])
+# #         error_demo.launch(share=True, inbrowser=True, debug=True)
 
-#         # Handle user registration
-#         submit_btn.click(
-#             collect_user_info,
-#             inputs=[first_name, session_id],
-#             outputs=[response_message, chatbot_container, registration_container, chatbot]
-#         )
 
-#     return demo
 
-# def launch_app():
-#     """Launch the Gradio interface."""
-#     ui = create_ui()
-#     ui.launch(share=True)
+# ############################################################################################################
 
-# # Main execution
-# if __name__ == "__main__":
+
+# import os
+# from langchain_groq import ChatGroq
+# from langchain.prompts import ChatPromptTemplate, PromptTemplate
+# from langchain.output_parsers import ResponseSchema, StructuredOutputParser
+# from urllib.parse import urljoin, urlparse
+# import requests
+# from io import BytesIO
+# from langchain_chroma import Chroma
+# import requests
+# from bs4 import BeautifulSoup
+# from langchain_core.prompts import ChatPromptTemplate
+# import gradio as gr
+# from PyPDF2 import PdfReader
+# from langchain_huggingface import HuggingFaceEmbeddings
+
+# groq_api_key= os.environ.get('GBV')
+
+# embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
+
+# def scrape_websites(base_urls):
 #     try:
-#         # Initialize and launch the assistant
-#         initialize_assistant()
-#         launch_app()
+#         visited_links = set()  # To avoid revisiting the same link
+#         content_by_url = {}  # Store content from each URL
+
+#         for base_url in base_urls:
+#             if not base_url.strip():
+#                 continue  # Skip empty or invalid URLs
+
+#             print(f"Scraping base URL: {base_url}")
+#             html_content = fetch_page_content(base_url)
+#             if html_content:
+#                 cleaned_content = clean_body_content(html_content)
+#                 content_by_url[base_url] = cleaned_content
+#                 visited_links.add(base_url)
+
+#                 # Extract and process all internal links
+#                 soup = BeautifulSoup(html_content, "html.parser")
+#                 links = extract_internal_links(base_url, soup)
+
+#                 for link in links:
+#                     if link not in visited_links:
+#                         print(f"Scraping link: {link}")
+#                         page_content = fetch_page_content(link)
+#                         if page_content:
+#                             cleaned_content = clean_body_content(page_content)
+#                             content_by_url[link] = cleaned_content
+#                             visited_links.add(link)
+
+#                         # If the link is a PDF file, extract its content
+#                         if link.lower().endswith('.pdf'):
+#                             print(f"Extracting PDF content from: {link}")
+#                             pdf_content = extract_pdf_text(link)
+#                             if pdf_content:
+#                                 content_by_url[link] = pdf_content
+
+#         return content_by_url
+
 #     except Exception as e:
-#         import traceback
-#         print(f"❌ Fatal error initializing GBV Assistant: {e}")
-#         print(traceback.format_exc())
-        
-#         # Create a minimal emergency UI to display the error
-#         with gr.Blocks() as error_demo:
-#             gr.Markdown("## System Error")
-#             gr.Markdown(f"An error occurred while initializing the application: {str(e)}")
-#             gr.Markdown("Please check your configuration and try again.")
-            
-#         error_demo.launch(share=True, inbrowser=True, debug=True)
+#         print(f"Error during scraping: {e}")
+#         return {}
+
+
+# def fetch_page_content(url):
+#     try:
+#         response = requests.get(url, timeout=10)
+#         response.raise_for_status()
+#         return response.text
+#     except requests.exceptions.RequestException as e:
+#         print(f"Error fetching {url}: {e}")
+#         return None
+
+
+# def extract_internal_links(base_url, soup):
+#     links = set()
+#     for anchor in soup.find_all("a", href=True):
+#         href = anchor["href"]
+#         full_url = urljoin(base_url, href)
+#         if is_internal_link(base_url, full_url):
+#             links.add(full_url)
+#     return links
+
 
+# def is_internal_link(base_url, link_url):
+#     base_netloc = urlparse(base_url).netloc
+#     link_netloc = urlparse(link_url).netloc
+#     return base_netloc == link_netloc
 
 
-############################################################################################################
+# def extract_pdf_text(pdf_url):
+#     try:
+#         response = requests.get(pdf_url)
+#         response.raise_for_status()
+#         with BytesIO(response.content) as file:
+#             reader = PdfReader(file)
+#             pdf_text = ""
+#             for page in reader.pages:
+#                 pdf_text += page.extract_text()
+
+#         return pdf_text if pdf_text else None
+#     except requests.exceptions.RequestException as e:
+#         print(f"Error fetching PDF {pdf_url}: {e}")
+#         return None
+#     except Exception as e:
+#         print(f"Error reading PDF {pdf_url}: {e}")
+#         return None
+
 
+# def clean_body_content(html_content):
+#     soup = BeautifulSoup(html_content, "html.parser")
+
+    
+#     for script_or_style in soup(["script", "style"]):
+#         script_or_style.extract()
+
+    
+#     cleaned_content = soup.get_text(separator="\n")
+#     cleaned_content = "\n".join(
+#         line.strip() for line in cleaned_content.splitlines() if line.strip()
+#     )
+#     return cleaned_content
+
+
+# if __name__ == "__main__":
+#     website = ["https://haguruka.org.rw/"
+              
+#                ] 
+#     all_content = scrape_websites(website)
+
+#     temp_list = []
+#     for url, content in all_content.items():
+#         temp_list.append((url, content)) 
+
+    
+# processed_texts = []
+
+    
+# for element in temp_list:
+#     if isinstance(element, tuple):
+#         url, content = element  
+#         processed_texts.append(f"url: {url}, content: {content}")
+#     elif isinstance(element, str):
+#         processed_texts.append(element)
+#     else:
+#         processed_texts.append(str(element))
+
+# def chunk_string(s, chunk_size=1000):
+#     return [s[i:i+chunk_size] for i in range(0, len(s), chunk_size)]
+
+# chunked_texts = []
+
+# for text in processed_texts:
+#   chunked_texts.extend(chunk_string(text))
+
+
+    
+
+
+# vectorstore = Chroma(
+#     collection_name="GBVR_Dataset",
+#     embedding_function=embed_model,
+#     persist_directory="./",
+# )
+
+# vectorstore.get().keys()
+
+# vectorstore.add_texts(chunked_texts)
+
+
+# template = ("""
+#     You are a friendly, intelligent, and conversational AI assistant designed to provide accurate, engaging, and human-like responses based on the given context. Your goal is to extract relevant details from the provided context: {context} and assist the user effectively. Follow these guidelines:
+
+#     1. **Warm & Natural Interaction**  
+#        - If the user greets you (e.g., "Hello," "Hi," "Good morning"), respond warmly and acknowledge them.  
+#        - Example responses:  
+#          - "😊 Good morning! How can I assist you today?"  
+#          - "Hello! What can I do for you? 🚀"  
+
+#     2. **Precise Information Extraction**  
+#        - Provide only the relevant details from the given context: {context}.  
+#        - Do not generate extra content or assumptions beyond the provided information.  
+
+#     3. **Conversational & Engaging Tone**  
+#        - Keep responses friendly, natural, and engaging.  
+#        - Use occasional emojis (e.g., 😊, 🚀) to make interactions more lively.  
+
+#     4. **Awareness of Real-Time Context**  
+#        - If necessary, acknowledge the current date and time to show awareness of real-world updates.  
+
+#     5. **Handling Missing Information**  
+#        - If no relevant information exists in the context, respond politely:  
+#          - "I don't have that information at the moment, but I'm happy to help with something else! 😊"  
+
+#     6. **Personalized Interaction**  
+#        - If user history is available, tailor responses based on their previous interactions for a more natural and engaging conversation.  
+
+#     7. **Direct, Concise Responses**  
+#        - If the user requests specific data, provide only the requested details without unnecessary explanations unless asked.  
+
+#     8. **Extracting Relevant Links**  
+#        - If the user asks for a link related to their request `{question}`, extract the most relevant URL from `{context}` and provide it directly.  
+#        - Example response:  
+#          - "Here is the link you requested: [URL]"  
+
+#     **Context:** {context}  
+#     **User's Question:** {question}  
+#     **Your Response:**  
+# """)
+
+
+# rag_prompt = PromptTemplate.from_template(template)
+
+# retriever = vectorstore.as_retriever()
+
+# from langchain_core.output_parsers import StrOutputParser
+# from langchain_core.runnables import RunnablePassthrough
+
+# llm = ChatGroq(model="llama-3.3-70b-versatile", api_key=groq_api_key )
+
+# rag_chain = (
+#     {"context": retriever, "question": RunnablePassthrough()}
+#     | rag_prompt
+#     | llm
+#     | StrOutputParser()
+# )
+
+
+# # Define the RAG memory stream function
+# def rag_memory_stream(message, history):
+#     partial_text = ""
+#     for new_text in rag_chain.stream(message):  # Replace with actual streaming logic
+#         partial_text += new_text
+#         yield partial_text
+
+# # Title with emojis
+# title = "GBVR Chatbot"
+
+
+# # Custom CSS for styling the interface
+# custom_css = """
+# body {
+#     font-family: "Arial", serif;
+# }
+# .gradio-container {
+#     font-family: "Times New Roman", serif;
+# }
+# .gr-button {
+#     background-color: #007bff; /* Blue button */
+#     color: white;
+#     border: none;
+#     border-radius: 5px;
+#     font-size: 16px;
+#     padding: 10px 20px;
+#     cursor: pointer;
+# }
+# .gr-textbox:focus, .gr-button:focus {
+#     outline: none; /* Remove outline focus for a cleaner look */
+# }
+
+# """
+
+# # Create the Chat Interface
+# demo = gr.ChatInterface(
+#     fn=rag_memory_stream,
+#     title=title,
+#     fill_height=True,
+#     theme="soft",
+#     css=custom_css, # Apply the custom CSS
+# )
+
+# # Launch the app
+# if __name__ == "__main__":
+#     demo.launch(share=True, inbrowser=True, debug=True)
 
 import os
 from langchain_groq import ChatGroq
@@ -669,9 +942,14 @@ from langchain_core.prompts import ChatPromptTemplate
 import gradio as gr
 from PyPDF2 import PdfReader
 from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_core.messages import HumanMessage, AIMessage
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
 
-groq_api_key= os.environ.get('GBV')
+# Set up environment variables
+groq_api_key = os.environ.get('GBV')
 
+# Initialize embedding model
 embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
 
 def scrape_websites(base_urls):
@@ -764,12 +1042,12 @@ def extract_pdf_text(pdf_url):
 
 def clean_body_content(html_content):
     soup = BeautifulSoup(html_content, "html.parser")
-
     
+    # Remove scripts and styles
     for script_or_style in soup(["script", "style"]):
         script_or_style.extract()
-
     
+    # Get cleaned text
     cleaned_content = soup.get_text(separator="\n")
     cleaned_content = "\n".join(
         line.strip() for line in cleaned_content.splitlines() if line.strip()
@@ -777,54 +1055,91 @@ def clean_body_content(html_content):
     return cleaned_content
 
 
-if __name__ == "__main__":
-    website = ["https://haguruka.org.rw/"
-              
-               ] 
-    all_content = scrape_websites(website)
-
-    temp_list = []
-    for url, content in all_content.items():
-        temp_list.append((url, content)) 
-
-    
-processed_texts = []
-
-    
-for element in temp_list:
-    if isinstance(element, tuple):
-        url, content = element  
-        processed_texts.append(f"url: {url}, content: {content}")
-    elif isinstance(element, str):
-        processed_texts.append(element)
-    else:
-        processed_texts.append(str(element))
-
 def chunk_string(s, chunk_size=1000):
     return [s[i:i+chunk_size] for i in range(0, len(s), chunk_size)]
 
-chunked_texts = []
 
-for text in processed_texts:
-  chunked_texts.extend(chunk_string(text))
+# Setup vectorstore for RAG
+def setup_vectorstore():
+    if __name__ == "__main__":
+        website = ["https://haguruka.org.rw/"] 
+        all_content = scrape_websites(website)
 
+        temp_list = []
+        for url, content in all_content.items():
+            temp_list.append((url, content)) 
 
+        processed_texts = []
+        
+        for element in temp_list:
+            if isinstance(element, tuple):
+                url, content = element  
+                processed_texts.append(f"url: {url}, content: {content}")
+            elif isinstance(element, str):
+                processed_texts.append(element)
+            else:
+                processed_texts.append(str(element))
+
+        chunked_texts = []
+        for text in processed_texts:
+            chunked_texts.extend(chunk_string(text))
+
+        vectorstore = Chroma(
+            collection_name="GBVR_Dataset",
+            embedding_function=embed_model,
+            persist_directory="./",
+        )
+
+        vectorstore.add_texts(chunked_texts)
+        return vectorstore
+    else:
+        # If imported as a module, just load the existing vectorstore
+        vectorstore = Chroma(
+            collection_name="GBVR_Dataset",
+            embedding_function=embed_model,
+            persist_directory="./",
+        )
+        return vectorstore
+
+
+# Session Manager class to handle conversation history
+class SessionManager:
+    def __init__(self):
+        self.sessions = {}
     
+    def get_session(self, session_id):
+        if session_id not in self.sessions:
+            self.sessions[session_id] = []
+        return self.sessions[session_id]
+    
+    def add_message(self, session_id, role, content):
+        session = self.get_session(session_id)
+        if role == "human":
+            session.append(HumanMessage(content=content))
+        elif role == "ai":
+            session.append(AIMessage(content=content))
+    
+    def get_history_as_string(self, session_id, max_turns=5):
+        """Convert recent conversation history to string format for context"""
+        session = self.get_session(session_id)
+        
+        # Get the most recent conversations (limited to max_turns)
+        recent_messages = session[-max_turns*2:] if len(session) > max_turns*2 else session
+        
+        history_str = ""
+        for msg in recent_messages:
+            role = "User" if isinstance(msg, HumanMessage) else "Assistant"
+            history_str += f"{role}: {msg.content}\n"
+        
+        return history_str.strip()
 
 
-vectorstore = Chroma(
-    collection_name="GBVR_Dataset",
-    embedding_function=embed_model,
-    persist_directory="./",
-)
-
-vectorstore.get().keys()
-
-vectorstore.add_texts(chunked_texts)
-
+# Initialize session manager
+session_manager = SessionManager()
 
-template = ("""
-    You are a friendly, intelligent, and conversational AI assistant designed to provide accurate, engaging, and human-like responses based on the given context. Your goal is to extract relevant details from the provided context: {context} and assist the user effectively. Follow these guidelines:
+# Modified template to include conversation history
+template = """
+    You are a friendly, intelligent, and conversational AI assistant designed to provide accurate, engaging, and human-like responses based on the given context. Your goal is to extract relevant details from the provided context and assist the user effectively. Follow these guidelines:
 
     1. **Warm & Natural Interaction**  
        - If the user greets you (e.g., "Hello," "Hi," "Good morning"), respond warmly and acknowledge them.  
@@ -833,7 +1148,7 @@ template = ("""
          - "Hello! What can I do for you? 🚀"  
 
     2. **Precise Information Extraction**  
-       - Provide only the relevant details from the given context: {context}.  
+       - Provide only the relevant details from the given context.  
        - Do not generate extra content or assumptions beyond the provided information.  
 
     3. **Conversational & Engaging Tone**  
@@ -848,49 +1163,134 @@ template = ("""
          - "I don't have that information at the moment, but I'm happy to help with something else! 😊"  
 
     6. **Personalized Interaction**  
-       - If user history is available, tailor responses based on their previous interactions for a more natural and engaging conversation.  
+       - Use the conversation history to provide more personalized and contextually relevant responses.
 
     7. **Direct, Concise Responses**  
        - If the user requests specific data, provide only the requested details without unnecessary explanations unless asked.  
 
     8. **Extracting Relevant Links**  
-       - If the user asks for a link related to their request `{question}`, extract the most relevant URL from `{context}` and provide it directly.  
+       - If the user asks for a link related to their request, extract the most relevant URL from the context and provide it directly.  
        - Example response:  
          - "Here is the link you requested: [URL]"  
 
-    **Context:** {context}  
-    **User's Question:** {question}  
-    **Your Response:**  
-""")
-
+    **Context from knowledge base:** {context}  
+    
+    **Previous conversation history:** 
+    {history}
+    
+    **Current User's Question:** {question}  
+    
+    **Your Response:**
+"""
 
+# Create prompt template with history
 rag_prompt = PromptTemplate.from_template(template)
 
-retriever = vectorstore.as_retriever()
-
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough
+# Initialize Groq LLM
+llm = ChatGroq(model="llama-3.3-70b-versatile", api_key=groq_api_key)
 
-llm = ChatGroq(model="llama-3.3-70b-versatile", api_key=groq_api_key )
+# Define the RAG chain with session history
+def get_rag_chain(vectorstore):
+    retriever = vectorstore.as_retriever()
+    
+    def rag_chain_with_history(query, session_id):
+        # Get conversation history
+        history = session_manager.get_history_as_string(session_id)
+        
+        # Get relevant documents from retriever
+        retrieved_docs = retriever.invoke(query)
+        context = "\n".join([doc.page_content for doc in retrieved_docs])
+        
+        # Create the prompt with context and history
+        prompt = rag_prompt.format(
+            context=context,
+            history=history,
+            question=query
+        )
+        
+        # Generate response
+        response = llm.invoke(prompt)
+        
+        # Add to session history
+        session_manager.add_message(session_id, "human", query)
+        session_manager.add_message(session_id, "ai", response.content)
+        
+        return response.content
+    
+    return rag_chain_with_history
 
-rag_chain = (
-    {"context": retriever, "question": RunnablePassthrough()}
-    | rag_prompt
-    | llm
-    | StrOutputParser()
-)
+# Initialize the vectorstore
+vectorstore = setup_vectorstore()
 
+# Get the RAG chain
+rag_chain_fn = get_rag_chain(vectorstore)
 
-# Define the RAG memory stream function
-def rag_memory_stream(message, history):
-    partial_text = ""
-    for new_text in rag_chain.stream(message):  # Replace with actual streaming logic
-        partial_text += new_text
-        yield partial_text
+# Define the streaming function for Gradio
+def rag_memory_stream(message, history, session_id=None):
+    if session_id is None:
+        # Generate a simple session ID if none provided
+        # In a production app, you would use something more sophisticated
+        session_id = "default_session"
+    
+    # Process the message and get the response
+    response = rag_chain_fn(message, session_id)
+    
+    # Stream the response word by word
+    words = response.split()
+    partial_response = ""
+    
+    for word in words:
+        partial_response += word + " "
+        yield partial_response.strip()
+
+# Create the Chat Interface with session management
+def create_chat_interface():
+    with gr.Blocks(theme="soft", css=custom_css) as demo:
+        gr.Markdown(f"# {title}")
+        
+        # Hidden session ID - in a real app, this would be managed by authentication
+        session_id = gr.State(value="default_session")
+        
+        chatbot = gr.Chatbot(height=600)
+        msg = gr.Textbox(
+            placeholder="Ask me anything about GBV resources...",
+            container=False,
+            scale=7
+        )
+        
+        def user_input(message, chat_history, session_id_val):
+            if message.strip() == "":
+                return "", chat_history
+                
+            chat_history.append([message, None])
+            return "", chat_history
+        
+        def bot_response(chat_history, session_id_val):
+            if chat_history and chat_history[-1][1] is None:
+                user_message = chat_history[-1][0]
+                bot_message = ""
+                
+                for chunk in rag_memory_stream(user_message, chat_history, session_id_val):
+                    bot_message = chunk
+                    chat_history[-1][1] = bot_message
+                    yield chat_history
+        
+        send = gr.Button("Send", variant="primary", scale=1)
+        clear = gr.Button("Clear Chat", variant="secondary")
+        
+        # Event handlers
+        send_event = msg.submit(user_input, [msg, chatbot, session_id], [msg, chatbot]).then(
+            bot_response, [chatbot, session_id], chatbot
+        )
+        send.click(user_input, [msg, chatbot, session_id], [msg, chatbot]).then(
+            bot_response, [chatbot, session_id], chatbot
+        )
+        clear.click(lambda: [], outputs=[chatbot])
+        
+    return demo
 
 # Title with emojis
-title = "GBVR Chatbot"
-
+title = "🤖 GBVR Chatbot"
 
 # Custom CSS for styling the interface
 custom_css = """
@@ -912,18 +1312,9 @@ body {
 .gr-textbox:focus, .gr-button:focus {
     outline: none; /* Remove outline focus for a cleaner look */
 }
-
 """
 
-# Create the Chat Interface
-demo = gr.ChatInterface(
-    fn=rag_memory_stream,
-    title=title,
-    fill_height=True,
-    theme="soft",
-    css=custom_css, # Apply the custom CSS
-)
-
 # Launch the app
 if __name__ == "__main__":
+    demo = create_chat_interface()
     demo.launch(share=True, inbrowser=True, debug=True)