Update app.py

app.py

@@ -23,11 +23,142 @@ from langchain_core.prompts import ChatPromptTemplate
 import gradio as gr
 from PyPDF2 import PdfReader
 
-groq_api_key= os.environ.get('grop_API_KEY')
 
-template = """
-    **Role**: Compassionate Regal Assistance and GBV Support Specialist with Emotional Awareness.
-    You are a friendly and empathetic chatbot designed to assist users in a conversational and human-like manner. Your goal is to provide accurate, helpful, and emotionally supportive responses based on the provided context: {context}. Follow these guidelines:
+# Configuration constants
+COLLECTION_NAME = "GBVRs"
+DATA_FOLDER = "./"
+APP_VERSION = "v1.0.0"
+APP_NAME = "Ijwi ry'Ubufasha Chatbot"
+MAX_HISTORY_MESSAGES = 10
+
+# Global state
+current_user = None
+welcome_message = None
+conversation_history = []
+llm = None
+embed_model = None
+vectorstore = None
+retriever = None
+rag_chain = None
+
+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')
+    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
+    )
+    
+    # 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
+    data = process_data_files()
+    
+    vectorstore = create_vectorstore(data)
+    retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
+    
+    # Create RAG chain
+    rag_chain = create_rag_chain()
+    
+
+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
+            
+        # 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
+            
+        # 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)
+                
+                # 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.")
+                    
+            except Exception as e:
+                print(f"ERROR processing file {file_name}: {e}")
+        
+        print(f"✅ Created {len(context_data)} chunks from {len(data_files)} files.")
+        
+    except Exception as e:
+        print(f"ERROR accessing data folder: {e}")
+        
+    return context_data
+
+def create_vectorstore(data):
+    """Create a vector store from the processed data."""
+    vs = Chroma(
+        collection_name=COLLECTION_NAME,
+        embedding_function=embed_model,
+    )
+    
+    if not data:
+        print("WARNING: No data available to create vector store.")
+        return vs
+        
+    # Extract text content and metadata
+    texts = [doc["page_content"] for doc in data]
+    metadatas = [doc["metadata"] for doc in data]
+    
+    try:
+        # Add data to vector store
+        vs.add_texts(texts, metadatas=metadatas)
+        print(f"✅ Added {len(texts)} documents to vector store.")
+    except Exception as e:
+        print(f"ERROR adding texts to vector store: {e}")
+        
+    return vs
+
+def create_rag_chain():
+    """Create the RAG chain for processing user queries."""
+    # Define the prompt template
+    template = """
+    **Role**: Compassionate GBV Support Specialist.
+    You are a friendly and empathetic chatbot designed to assist users with gender-based violence support. Your goal is to provide accurate, helpful, and emotionally supportive responses based on the provided context: {context}. Follow these guidelines:
 
     1. **Emotional Awareness**
       - Acknowledge the user's emotions and respond with empathy.
@@ -35,41 +166,23 @@ template = """
       - If the user expresses negative emotions, offer comfort and reassurance.
 
     2. **Contextual Interaction**
-      - Begin with a warm and empathetic welcome message.
       - Extract precise details from the provided context: {context}.
       - Respond directly to the user's question: {question}.
       - Only provide detailed information if user requests it.
       - Remember the user's name is {first_name}.
 
     3. **Communication Guidelines**
-      - Maintain a warm, conversational tone (avoid over-familiarity).
+      - Maintain a warm, conversational tone.
       - Use occasional emojis for engagement (e.g., 😊, 🤗, ❤️).
       - Provide clear, concise, and emotionally supportive information.
 
-    4. **Response Strategies**
-      - Greet users naturally and ask about their wellbeing (e.g., "Welcome, {first_name}! 😊 How are you feeling today?", "Hello {first_name}! 🤗 What's on your mind?").
-      - Always start with a check-in about the user's wellbeing or current situation.
-      - Provide a concise summary with only relevant information.
-      - Avoid generating content beyond the context.
-      - Handle missing information transparently.
-
-    5. **No Extra Content**
-      - If no information in {context} matches the user's request {question} :
+    4. **No Extra Content**
+      - If no information in {context} matches the user's request {question}:
         * Respond politely: "I don't have that information at the moment, {first_name}. 😊"
         * Offer alternative assistance options.
       - Strictly avoid generating unsupported content.
-      - Prevent information padding or speculation.
-
-    6. **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]"
 
-    7. **Real-Time Awareness**
-      - Acknowledge the current context when appropriate.
-      - Stay focused on the user's immediate needs.
-
-    8. **Previous Conversation Context**
+    5. **Previous Conversation Context**
       - Consider the conversation history: {conversation_history}
       - Maintain continuity with previous exchanges.
 
@@ -77,304 +190,202 @@ template = """
     **User's Question:** {question}
     **Your Response:**
     """
-
-# Set up embedding model
-embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
-
-# Process data from Drive
-def process_data_files():
-    folder_path = "./"
-    context_data = []
     
-    # Get list of data files
-    all_files = os.listdir(folder_path)
-    data_files = [f for f in all_files if f.lower().endswith(('.csv', '.xlsx', '.xls'))]
+    rag_prompt = PromptTemplate.from_template(template)
     
-    # Process each file
-    for index, file_name in enumerate(data_files, 1):
-        file_path = os.path.join(folder_path, file_name)
+    def get_context_and_question(query):
+        # Get user info and conversation history
+        user_info = get_user() or {}
+        first_name = user_info.get("Nickname", "User")
+        conversation_hist = get_formatted_history()
         
         try:
-            # Read file
-            if file_name.lower().endswith('.csv'):
-                df = pd.read_csv(file_path)
-            else:
-                df = pd.read_excel(file_path)
-            
-            # Check if column 3 exists
-            if df.shape[1] > 2:
-                column_data = df.iloc[:, 2].dropna().astype(str).tolist()
-                
-                # Each row becomes one chunk
-                for i, text in enumerate(column_data):
-                    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.")
-                
+            # Retrieve relevant documents
+            retrieved_docs = retriever.invoke(query)
+            context_str = format_context(retrieved_docs)
         except Exception as e:
-            print(f"Error processing file {file_name}: {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 context_data
+    # 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):
+            return f"I'm here to help you, but I'm experiencing some technical difficulties right now. Please try again shortly."
+            
+        return fallback_chain
 
-# Create vectorstore
-def create_vectorstore(data):
-    # Extract just the text content from each Document object in the list
-    cleaned_texts = [doc["page_content"] for doc in data]
-    metadatas = [doc["metadata"] for doc in data]
-    
-    # Create vector store
-    vectorstore = Chroma(
-        collection_name="GBVRS",
-        embedding_function=embed_model,
-    )
+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 set_user(user_info):
+    """Set current user and initialize welcome message."""
+    global current_user, conversation_history
+    current_user = user_info
+    generate_welcome_message(user_info.get("Nickname", "Guest"))
     
-    # Add data to vector store
-    vectorstore.add_texts(cleaned_texts, metadatas=metadatas)
-    return vectorstore
-
-# User session management
-class UserSession:
-    def __init__(self, llm):
-        self.current_user = None
-        self.welcome_message = None
-        self.conversation_history = []
-        self.llm = llm
-
-    def set_user(self, user_info):
-        self.current_user = user_info
-        self.set_welcome_message(user_info.get("Nickname", "Guest"))
-        # Initialize conversation history with welcome message
-        welcome = self.get_welcome_message()
-        self.conversation_history = [
-            {"role": "assistant", "content": welcome},
-        ]
-
-    def get_user(self):
-        return self.current_user
-
-    def set_welcome_message(self, nickname):
-        """Set a dynamic welcome message using the LLM."""
-        # Define a prompt for the LLM to generate a welcome message
+    # Initialize conversation history with welcome message
+    welcome = get_welcome_message()
+    conversation_history = [
+        {"role": "assistant", "content": welcome},
+    ]
+
+def get_user():
+    """Get current user information."""
+    global current_user
+    return current_user or {"Nickname": "Guest"}
+
+def generate_welcome_message(nickname):
+    """Generate a dynamic welcome message using the LLM."""
+    global welcome_message
+    try:
+        # Use the LLM to generate the message
         prompt = (
-            f"Create a very brief welcome message for {nickname} that fits in 3 lines. "
-            f"The message should: "
-            f"1. Welcome {nickname} warmly and professionally. "
-            f"2. Emphasize that this is a safe and trusted space. "
-            f"3. Highlight specialized support for gender-based violence (GBV) and legal assistance. "
-            f"4. Use a tone that is warm, reassuring, and professional. "
-            f"5. Keep the message concise and impactful, ensuring it fits within the character limit."
+            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
-        response = self.llm.invoke(prompt)
-        welcome = response.content
-
+        
+        response = llm.invoke(prompt)
+        welcome = response.content.strip()
+        
         # Format the message with HTML styling
-        self.welcome_message = (
-            f"<div style='font-size: 24px; font-weight: bold; color: #2E86C1;'>"
-            f"<div style='font-size: 20px;'>"
+        welcome_message = (
+            f"<div style='font-size: 18px; color: #4E6BBF;'>"
             f"{welcome}"
             f"</div>"
         )
+    except Exception as e:
+        # Fallback welcome message
+        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):
-        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})
-
-    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"""
-        formatted_history = ""
-        for entry in self.conversation_history:
-            role = "User" if entry["role"] == "user" else "Assistant"
-            formatted_history += f"{role}: {entry['content']}\n\n"
-        return formatted_history
-
-# Format context from documents
-def format_context(retrieved_docs):
-    return "\n".join([doc.page_content for doc in retrieved_docs])
-
-# RAG Chain creation with updated approach
-def create_rag_chain(retriever, template, api_key):
-    llm = ChatGroq(model="llama-3.3-70b-versatile", api_key=api_key)
-    rag_prompt = PromptTemplate.from_template(template)
+def get_welcome_message():
+    """Get the formatted welcome message."""
+    global welcome_message
+    if not welcome_message:
+        nickname = get_user().get("Nickname", "Guest")
+        generate_welcome_message(nickname)
+    return welcome_message
+
+def add_to_history(role, message):
+    """Add a message to the conversation history."""
+    global conversation_history
+    conversation_history.append({"role": role, "content": message})
     
-    # Define the RAG chain using the recommended approach
-    def get_context_and_question(query):
-        # Get user info from the session
-        user_info = user_session.get_user() or {}
-        first_name = user_info.get("Nickname", "User")
-        
-        # Get conversation history
-        conversation_history = user_session.get_formatted_history()
+    # Trim history if it gets too long
+    if len(conversation_history) > MAX_HISTORY_MESSAGES * 2:  # Keep pairs of messages
+        # Keep the first message (welcome) and the most recent messages
+        conversation_history = [conversation_history[0]] + conversation_history[-MAX_HISTORY_MESSAGES*2+1:]
+
+def get_conversation_history():
+    """Get the full conversation history."""
+    return conversation_history
+
+def get_formatted_history():
+    """Get conversation history formatted as a string for the LLM."""
+    # Skip the welcome message and only include the last few exchanges
+    recent_history = conversation_history[1:] if len(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:]
         
-        # Retrieve documents
-        retrieved_docs = retriever.invoke(query)
-        context_str = format_context(retrieved_docs)
+    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 the combined inputs for the prompt
-        return {
-            "context": context_str,
-            "question": query,
-            "first_name": first_name,
-            "conversation_history": conversation_history
-        }
-    
-    # Build the chain
-    rag_chain = (
-        RunnablePassthrough() 
-        | get_context_and_question 
-        | rag_prompt 
-        | llm 
-        | StrOutputParser()
-    )
-    
-    return rag_chain
+    return formatted_history
 
-# RAG memory function for user interaction (without translation)
 def rag_memory_stream(message, history):
+    """Process user message and generate response with memory."""
     # Add user message to history
-    user_session.add_to_history("user", message)
-
-    # Get response from RAG chain
-    response = rag_chain.invoke(message)
+    add_to_history("user", message)
 
-    # Add assistant response to history
-    user_session.add_to_history("assistant", response)
-
-    # Yield the response
-    yield response
-
-# Add initial message to start the conversation
-def add_initial_message(chatbot):
-    return chatbot
+    try:
+        # Get response from RAG chain
+        print(f"Processing message: {message[:50]}...")
+        response = rag_chain.invoke(message)
+        print(f"Generated response: {response[:50]}...")
+        
+        # Add assistant response to history
+        add_to_history("assistant", 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()}")
+        
+        error_msg = f"I'm sorry, {get_user().get('Nickname', 'there')}. I encountered an error processing your request. Let's try a different question."
+        add_to_history("assistant", error_msg)
+        yield error_msg
 
-# Store user details and handle session
 def collect_user_info(nickname):
-    if not nickname:
+    """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,
+        "Nickname": nickname.strip(),
         "timestamp": time.strftime("%Y-%m-%d %H:%M:%S")
     }
 
     # Set user in session
-    user_session.set_user(user_info)
+    set_user(user_info)
 
     # Generate welcome message
-    welcome_message = user_session.get_welcome_message()
-
-    # Add initial message to start the conversation
-    chat_history = add_initial_message([(None, welcome_message)])
+    welcome_message = get_welcome_message()
 
     # Return welcome message and update UI
-    return welcome_message, gr.update(visible=True), gr.update(visible=False), chat_history
+    return welcome_message, gr.update(visible=True), gr.update(visible=False), [(None, welcome_message)]
 
-# Gradio Interface Setup with improved UX
-def chatbot_interface():
-    global template, rag_chain
-
-    template = """
-    **Role**: Compassionate Regal Assistance and GBV Support Specialist with Emotional Awareness.
-    You are a friendly and empathetic chatbot designed to assist users in a conversational and human-like manner. Your goal is to provide accurate, helpful, and emotionally supportive responses based on the provided context: {context}. Follow these guidelines:
-
-    1. **Emotional Awareness**
-      - Acknowledge the user's emotions and respond with empathy.
-      - Use phrases like "I understand how you feel," "That sounds challenging," or "I'm here to support you."
-      - If the user expresses negative emotions, offer comfort and reassurance.
-
-    2. **Contextual Interaction**
-      - Begin with a warm and empathetic welcome message.
-      - Extract precise details from the provided context: {context}.
-      - Respond directly to the user's question: {question}.
-      - Only provide detailed information if user requests it.
-      - Remember the user's name is {first_name}.
-
-    3. **Communication Guidelines**
-      - Maintain a warm, conversational tone (avoid over-familiarity).
-      - Use occasional emojis for engagement (e.g., 😊, 🤗, ❤️).
-      - Provide clear, concise, and emotionally supportive information.
-
-    4. **Response Strategies**
-      - Greet users naturally and ask about their wellbeing (e.g., "Welcome, {first_name}! 😊 How are you feeling today?", "Hello {first_name}! 🤗 What's on your mind?").
-      - Always start with a check-in about the user's wellbeing or current situation.
-      - Provide a concise summary with only relevant information.
-      - Avoid generating content beyond the context.
-      - Handle missing information transparently.
-
-    5. **No Extra Content**
-      - If no information in {context} matches the user's request {question} :
-        * Respond politely: "I don't have that information at the moment, {first_name}. 😊"
-        * Offer alternative assistance options.
-      - Strictly avoid generating unsupported content.
-      - Prevent information padding or speculation.
-
-    6. **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]"
-
-    7. **Real-Time Awareness**
-      - Acknowledge the current context when appropriate.
-      - Stay focused on the user's immediate needs.
-
-    8. **Previous Conversation Context**
-      - Consider the conversation history: {conversation_history}
-      - Maintain continuity with previous exchanges.
-
-    **Context:** {context}
-    **User's Question:** {question}
-    **Your Response:**
-    """
-
-    with gr.Blocks() as demo:
-        # User registration section
-        with gr.Column(visible=True, elem_id="registration_container") as registration_container:
-            gr.Markdown("### Your privacy is our concern, please provide your nickname.")
-
-            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:
-            chat_interface = gr.ChatInterface(
-                fn=rag_memory_stream,
-                title="Chat with GBVR",
-                fill_height=True
-            )
-
-            # Footer with version info
-            gr.Markdown("Ijwi ry'Ubufasha v1.0.0 © 2025")
-
-        # Handle user registration
-        submit_btn.click(
-            collect_user_info,
-            inputs=[first_name],
-            outputs=[response_message, chatbot_container, registration_container, chat_interface.chatbot]
-        )
-
-    demo.css = """
+def get_css():
+    """Define CSS for the UI."""
+    return """
     :root {
-        --background: #f0f0f0;
-        --text: #000000;
+        --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 {
@@ -387,7 +398,8 @@ def chatbot_interface():
         justify-content: center;
         align-items: center;
         background: var(--background);
-        color: var(--text);
+        color: var(--text-primary);
+        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
     }
 
     .gradio-container {
@@ -396,61 +408,146 @@ def chatbot_interface():
     }
 
     .gr-box {
-        background: var(--background);
-        color: var(--text);
+        background: var(--card-bg);
+        color: var(--text-primary);
         border-radius: 12px;
         padding: 2rem;
-        border: 1px solid rgba(0, 0, 0, 0.1);
-        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
+        border: 1px solid var(--border);
+        box-shadow: 0 4px 12px var(--shadow);
     }
 
     .gr-button-primary {
-        background: var(--background);
-        color: var(--text);
+        background: var(--primary);
+        color: white;
         padding: 12px 24px;
         border-radius: 8px;
         transition: all 0.3s ease;
-        border: 1px solid rgba(0, 0, 0, 0.1);
+        border: none;
+        font-weight: bold;
     }
 
     .gr-button-primary:hover {
         transform: translateY(-1px);
-        box-shadow: 0 4px 12px rgba(0, 0, 0, 0.2);
+        box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
+        background: var(--primary-light);
     }
 
     footer {
         text-align: center;
-        color: var(--text);
-        opacity: 0.7;
+        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);
-        margin-bottom: 1rem;
+        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;
     }
 
-    .registration-markdown, .chat-title h1 {
-        color: var(--text);
+    .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:
+        # 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:
+            chat_interface = gr.ChatInterface(
+                fn=rag_memory_stream,
+                title=f"{APP_NAME} - GBV Support Assistant",
+                fill_height=True,
+                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?"
+                ]
+            )
+
+            # Footer with version info
+            gr.Markdown(f"{APP_NAME} {APP_VERSION} © 2025")
+
+        # Handle user registration
+        submit_btn.click(
+            collect_user_info,
+            inputs=[first_name],
+            outputs=[response_message, chatbot_container, registration_container, chat_interface.chatbot]
+        )
+
     return demo
 
+def launch_app():
+    """Launch the Gradio interface."""
+    ui = create_ui()
+    ui.launch(share=True)
+
 # Main execution
 if __name__ == "__main__":
-    # Process data and create vectorstore
-    data = process_data_files()
-    vectorstore = create_vectorstore(data)
-    retriever = vectorstore.as_retriever()
-    
-    # Initialize LLM for the user session
-    llm = ChatGroq(model="llama-3.3-70b-versatile", api_key=groq_api_key)
-    user_session = UserSession(llm)
-    
-    # Create RAG chain with the new approach
-    rag_chain = create_rag_chain(retriever, template, groq_api_key)
-    
-    # Launch the interface
-    chatbot_interface().launch(share=True)
+    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.")
+            
+        error_demo.launch(share=True)