Update app.py

app.py

@@ -1,651 +1,973 @@
-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()
+# 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()
+#     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},
-        ]
+#         # 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 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")
+#     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>"
-            )
+#             # 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 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()
+#     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:]
+#         # 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_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 []
+#     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:]
+#         # 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
+#         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 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()
+#     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)
+#         # 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]
+#         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)
+#     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]?"
+#         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
+#         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:**
-    """
+#         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)
+#     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"]
+#     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()
+#         # 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."
+#         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
-        }
+#         # 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}")
+#     # 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 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)
+#         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)
+#     # 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()}")
+#     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;
-    }
+#         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()}")
+#     #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
-            )
+#         # 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://www.rra.gov.rw/en/publications",
+               "https://www.rra.gov.rw/en/customs-services",
+              
+              
+               ] 
+    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="R_R_A",
+    embedding_function=embed_model,
+    persist_directory="./",
+)
+
+vectorstore.get().keys()
+
+vectorstore.add_texts(chunked_texts)
+
+
+# template = ("""
+#     You are a friendly and intelligent chatbot designed to assist users in a conversational and human-like manner. Your goal is to provide accurate, helpful, and engaging responses from the provided context: {context} while maintaining a natural tone. Follow these guidelines:
+
+#     1. **Greetings:** If the user greets you (e.g., "Morning," "Hello," "Hi"), respond warmly and acknowledge the greeting. For example:
+#        - "😊 Good morning! How can I assist you today?"
+#        - "Hello! What can I do for you? 🚀"
+#     2. **Extract Information:** If the user asks for specific information, extract only the relevant details from the provided context: {context}.
+#     3. **Human-like Interaction:** Respond in a warm, conversational tone. Use emojis occasionally to make the interaction more engaging (e.g., 😊, 🚀).
+#     4. **Stay Updated:** Acknowledge the current date and time to show you are aware of real-time updates.
+#     5. **No Extra Content:** If no information matches the user's request, respond politely: "I don't have that information at the moment, but I'm happy to help with something else! 😊"
+#     6. **Personalized Interaction:** Use the user's historical interactions (if available) to tailor your responses and make the conversation more personalized.
+#     7. **Direct Data Only:** If the user requests specific data, provide only the requested information without additional explanations unless asked.
+
+#     Context: {context}
+#     User's Question: {question}
+#     Your Response:
+# """)
+
+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 = "RRA Chatbot"
+
+# Short description for the examples section
+examples = [
+    " Can you help me with the tax rates on vehicle importation?",
+    " What is TIN deregistration? What about Tax account deactivation?",
+    "When do I receive my registration certificate?"
+]
+
+
+# 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 */
+}
+
+/* Custom CSS for the examples section */
+.gr-examples {
+    font-size: 30px; /* Increase font size of examples */
+    background-color: #f9f9f9; /* Light background color */
+    border-radius: 30px; /* Rounded corners */
+}
+
+.gr-examples .example {
+    background-color: white; /* White background for each example */
+    cursor: pointer; /* Change cursor to pointer on hover */
+    transition: background-color 0.3s ease; /* Smooth hover effect */
+}
+
+.gr-examples .example:hover {
+    background-color: #f1f1f1; /* Light gray background on hover */
+}
+"""
+
+# Create the Chat Interface
+demo = gr.ChatInterface(
+    fn=rag_memory_stream,
+    title=title,
+    examples=examples,  # Display the short description and example questions
+    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)