Update app.py

app.py

@@ -1,142 +1,821 @@
-import torch
+import re
+import random
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import json
+import os
+from typing import Dict, List, Any
 
-# ---------- CONFIG ----------
-# Using an open-access model instead of gated Mistral
-MODEL_NAME = "tiiuae/falcon-7b-instruct"
+# Try to import AI libraries
+try:
+    import openai
+    OPENAI_AVAILABLE = True
+except ImportError:
+    OPENAI_AVAILABLE = False
 
 try:
-    # Preload model and tokenizer
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-    
-    # Load model with appropriate settings
-    model = AutoModelForCausalLM.from_pretrained(
-        MODEL_NAME,
-        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-        device_map="auto" if torch.cuda.is_available() else None,
-        low_cpu_mem_usage=True
-    )
-    
-    generator = pipeline(
-        "text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        max_new_tokens=512,
-        temperature=0.5,
-        do_sample=True
-    )
+    from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+    import torch
+    TRANSFORMERS_AVAILABLE = True
+except ImportError:
+    TRANSFORMERS_AVAILABLE = False
+
+# Try to import sentence transformers for semantic search
+try:
+    from sentence_transformers import SentenceTransformer
+    import numpy as np
+    SENTENCE_TRANSFORMERS_AVAILABLE = True
+except ImportError:
+    SENTENCE_TRANSFORMERS_AVAILABLE = False
+
+class ChatAssistant:
+    def __init__(self):
+        self.name = "AI Assistant"
+        self.user_name = ""
+        self.conversation_history = []
+        self.model_loaded = False
+        self.generator = None
+        self.tokenizer = None
+        self.model = None
+        self.embedding_model = None
+        
+        # Initialize embedding model for semantic search
+        self.init_embedding_model()
+        
+    def init_embedding_model(self):
+        """Initialize embedding model for semantic search"""
+        if SENTENCE_TRANSFORMERS_AVAILABLE:
+            try:
+                self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+            except Exception as e:
+                print(f"Failed to load embedding model: {e}")
+                self.embedding_model = None
+        else:
+            self.embedding_model = None
     
-except Exception as e:
-    print(f"Error loading model: {str(e)}")
-    # Fallback to CPU if GPU fails
-    model = AutoModelForCausalLM.from_pretrained(
-        MODEL_NAME,
-        torch_dtype=torch.float32,
-        device_map=None
-    )
-    generator = pipeline(
-        "text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        max_new_tokens=512,
-        temperature=0.5,
-        do_sample=True
-    )
-
-# ---------- TECH FILTER ----------
-def is_tech_query(message: str) -> bool:
-    tech_keywords = [
-        "python", "java", "javascript", "html", "css", "react", "angular",
-        "node", "machine learning", "deep learning", "ai", "api", "code",
-        "debug", "error", "technology", "computer", "programming", "software",
-        "hardware", "cybersecurity", "database", "sql", "devops", "cloud",
-        "algorithm", "backend", "frontend", "server", "linux", "windows",
-        "docker", "kubernetes", "git", "github", "vscode", "pycharm",
-        "tensorflow", "pytorch", "neural network", "blockchain", "web3",
-        "smart contract", "ethereum", "bitcoin", "cryptography", "encryption"
-    ]
-    return any(k in message.lower() for k in tech_keywords)
-
-# ---------- CHAT FUNCTION ----------
-def chat_with_model(message, history):
-    if not is_tech_query(message):
-        return history + [[message, "⚠️ I can only answer technology-related queries. Please ask about programming, AI, cybersecurity, or other tech topics."]]
-
-    conversation = ""
-    for user_msg, bot_msg in history:
-        conversation += f"User: {user_msg}\nAssistant: {bot_msg}\n"
-    conversation += f"User: {message}\nAssistant:"
-
-    try:
-        output = generator(conversation, pad_token_id=tokenizer.eos_token_id)[0]["generated_text"]
-        if "Assistant:" in output:
-            answer = output.split("Assistant:")[-1].strip()
+    def load_model(self):
+        """Load AI model for advanced queries"""
+        if self.model_loaded:
+            return True
+            
+        if TRANSFORMERS_AVAILABLE:
+            try:
+                # Use a smaller, more reliable model
+                model_name = "microsoft/DialoGPT-medium"
+                
+                self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+                self.model = AutoModelForCausalLM.from_pretrained(
+                    model_name,
+                    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+                    device_map="auto" if torch.cuda.is_available() else None,
+                    low_cpu_mem_usage=True
+                )
+                
+                # Add pad token if not present
+                if self.tokenizer.pad_token is None:
+                    self.tokenizer.pad_token = self.tokenizer.eos_token
+                
+                self.generator = pipeline(
+                    "text-generation",
+                    model=self.model,
+                    tokenizer=self.tokenizer,
+                    device=0 if torch.cuda.is_available() else -1,
+                    return_full_text=False
+                )
+                
+                self.model_loaded = True
+                print("✅ AI model loaded successfully!")
+                return True
+                
+            except Exception as e:
+                print(f"⚠️ Could not load AI model: {str(e)}")
+                return False
         else:
-            answer = output.strip()
-        
-        # Clean up response
-        answer = answer.split("User:")[0].strip()  # Remove any following user prompts
-        return history + [[message, answer]]
+            print("🔧 Install transformers and torch for AI features")
+            return False
     
-    except Exception as e:
-        error_msg = f"❌ Error generating response: {str(e)}"
-        return history + [[message, error_msg]]
-
-# ---------- LOGIN + UI ----------
-session_state = {"authenticated": False}
-
-def login(username, password):
-    valid_credentials = {
-        "admin": "admin123",
-        "techuser": "techpass",
-        "guest": "guest123"
-    }
+    def generate_response(self, query: str, context: str = "") -> str:
+        """Generate conversational AI response"""
+        if not self.model_loaded:
+            if not self.load_model():
+                return self.generate_fallback_response(query, context)
+        
+        try:
+            # Create a conversational prompt with different response styles
+            tone = random.choice([
+                "friendly and conversational",
+                "professional and informative",
+                "detailed and analytical",
+                "concise and to-the-point",
+                "enthusiastic and encouraging"
+            ])
+            
+            system_prompt = f"""You are an advanced AI assistant designed to help with a wide range of topics. 
+Respond in a {tone} tone, adapting to the user's needs. 
+
+Guidelines:
+1. For greetings: Be warm and welcoming
+2. For questions: Provide thoughtful, well-reasoned answers
+3. For complex topics: Break down into simpler parts
+4. For creative requests: Be imaginative and engaging
+5. For technical queries: Be precise and accurate
+6. Vary your responses - don't be repetitive"""
+
+            user_prompt = f"""User's question: {query}
+
+Context from previous conversation: {context}
+
+Please provide a helpful response that addresses the user's needs while following the guidelines above."""
+
+            # Generate response
+            full_prompt = f"{system_prompt}\n\nUser: {user_prompt}\nAssistant:"
+            
+            response = self.generator(
+                full_prompt,
+                max_new_tokens=250,
+                do_sample=True,
+                temperature=0.7 + random.random() * 0.3,  # Vary temperature
+                top_p=0.9,
+                pad_token_id=self.tokenizer.eos_token_id,
+                repetition_penalty=1.2,
+                no_repeat_ngram_size=3
+            )
+            
+            if response and len(response) > 0:
+                generated_text = response[0]["generated_text"]
+                # Extract only the assistant's response
+                if "Assistant:" in generated_text:
+                    ai_response = generated_text.split("Assistant:")[-1].strip()
+                    if len(ai_response) > 20:
+                        return ai_response
+                        
+        except Exception as e:
+            print(f"AI generation error: {e}")
+        
+        # Fallback response
+        return self.generate_fallback_response(query, context)
     
-    if username in valid_credentials and password == valid_credentials[username]:
-        session_state["authenticated"] = True
-        return gr.update(visible=False), gr.update(visible=True), ""
-    else:
-        return gr.update(), gr.update(visible=False), "❌ Invalid credentials. Try admin/admin123 or techuser/techpass"
-
-def logout():
-    session_state["authenticated"] = False
-    return gr.update(visible=True), gr.update(visible=False), "Logged out successfully"
-
-with gr.Blocks(css="""
-    .gradio-container {max-width: 750px; margin: auto;}
-    .chatbot {min-height: 500px;}
-    """) as demo:
+    def generate_fallback_response(self, query: str, context: str) -> str:
+        """Generate fallback response when model fails"""
+        query_lower = query.lower()
+        
+        greetings = ["Hi there!", "Hello!", "Greetings!", "Nice to see you!"]
+        farewells = ["Goodbye!", "Farewell!", "See you later!", "Have a great day!"]
+        
+        if any(word in query_lower for word in ['hi', 'hello', 'hey', 'greetings']):
+            return random.choice(greetings) + " How can I assist you today?"
+        
+        elif any(word in query_lower for word in ['bye', 'goodbye', 'see you', 'farewell']):
+            return random.choice(farewells)
+        
+        elif '?' in query or any(word in query_lower for word in ['what', 'how', 'why', 'when']):
+            return self.answer_question(query)
+        
+        elif any(word in query_lower for word in ['thank', 'thanks', 'appreciate']):
+            return random.choice([
+                "You're very welcome!",
+                "Happy to help!",
+                "My pleasure!",
+                "Glad I could assist you!"
+            ])
+        
+        else:
+            return random.choice([
+                "I'd be happy to help with that. Could you tell me more about what you're looking for?",
+                "That's an interesting topic. Here's what I can share about that...",
+                "Let me provide some information about that for you.",
+                "I can certainly help with that. Here's what you should know..."
+            ])
     
-    # Login Page
-    with gr.Group(visible=True) as login_group:
-        gr.Markdown("# 🔐 Tech Chatbot Login")
-        with gr.Row():
-            username = gr.Textbox(label="Username", placeholder="Enter your username")
-            password = gr.Textbox(label="Password", type="password", placeholder="Enter your password")
-        with gr.Row():
-            login_btn = gr.Button("Login", variant="primary")
-        login_status = gr.Markdown("")
+    def answer_question(self, query: str) -> str:
+        """Generate answer for different types of questions"""
+        responses = {
+            'science': [
+                "In scientific terms, this can be explained as...",
+                "The scientific perspective on this is...",
+                "From a scientific standpoint..."
+            ],
+            'technology': [
+                "Technologically speaking, the current state is...",
+                "In the tech world, this is handled by...",
+                "The technical approach would be..."
+            ],
+            'history': [
+                "Historically, this dates back to...",
+                "The historical context for this is...",
+                "Looking back through history..."
+            ],
+            'general': [
+                "That's a great question. Here's what I know...",
+                "I can provide some insights on that...",
+                "Let me explain that for you..."
+            ]
+        }
+        
+        category = 'general'
+        if any(word in query.lower() for word in ['science', 'physics', 'biology', 'chemistry']):
+            category = 'science'
+        elif any(word in query.lower() for word in ['tech', 'computer', 'software', 'hardware']):
+            category = 'technology'
+        elif any(word in query.lower() for word in ['history', 'historical', 'past']):
+            category = 'history'
+        
+        return random.choice(responses[category])
 
-    # Chatbot Page
-    with gr.Group(visible=False) as chat_group:
-        gr.Markdown("# 💻 Tech Assistant")
-        chatbot = gr.Chatbot(height=500)
-        with gr.Row():
-            msg = gr.Textbox(placeholder="Ask about programming, AI, cybersecurity...", 
-                            label="Your Tech Question", scale=4)
-            submit_btn = gr.Button("Send", variant="primary", scale=1)
-        with gr.Row():
-            clear = gr.Button("Clear Chat")
-            logout_btn = gr.Button("Logout")
+    def get_user_name(self, message):
+        name_patterns = [
+            r"my name is (\w+)",
+            r"i'm (\w+)",
+            r"i am (\w+)",
+            r"call me (\w+)"
+        ]
+        
+        for pattern in name_patterns:
+            match = re.search(pattern, message.lower())
+            if match:
+                self.user_name = match.group(1).capitalize()
+                return f"Nice to meet you, {self.user_name}! How can I assist you today?"
+        return None
 
-        msg.submit(chat_with_model, [msg, chatbot], [chatbot])
-        submit_btn.click(chat_with_model, [msg, chatbot], [chatbot])
-        clear.click(lambda: None, None, chatbot, queue=False)
-        logout_btn.click(logout, None, [login_group, chat_group, login_status])
+    def process_message(self, message: str) -> str:
+        """Main method to process user messages and generate AI responses"""
+        # Check if the user is introducing themselves
+        name_response = self.get_user_name(message)
+        if name_response:
+            return name_response
+        
+        # Generate AI response
+        context = "\n".join([f"User: {msg[0]}\nAssistant: {msg[1]}" for msg in self.conversation_history[-3:]])
+        ai_response = self.generate_response(message, context)
+        
+        if ai_response and len(ai_response.strip()) > 10:
+            return ai_response
+        else:
+            # Final fallback
+            return self.generate_fallback_response(message, "")
+
+# Initialize the assistant
+assistant = ChatAssistant()
+
+def chat_response(message, history):
+    """Generate response for Gradio chat interface"""
+    if not message.strip():
+        return "Please enter a message!"
+    
+    response = assistant.process_message(message)
+    return response
 
-    # Button Logic
-    login_btn.click(login, [username, password], [login_group, chat_group, login_status])
+def greet():
+    greetings = [
+        "Hello! I'm your AI assistant. How can I help you today?",
+        "Hi there! What would you like to know or discuss?",
+        "Greetings! I'm here to assist with any questions you might have.",
+        "Welcome! I'm ready to help with information, ideas, or just a friendly chat."
+    ]
+    return random.choice(greetings)
+
+# Create Gradio interface
+def create_interface():
+    with gr.Blocks(
+        title="💬 AI Assistant - Your Intelligent Helper",
+        theme=gr.themes.Base(
+            primary_hue="blue",
+            secondary_hue="indigo",
+            neutral_hue="gray",
+            font=[gr.themes.GoogleFont("Inter"), "Arial", "sans-serif"]
+        ),
+        css="""
+        /* Main Container Styling */
+        .gradio-container {
+            max-width: 1400px !important;
+            margin: 0 auto !important;
+            padding: 20px !important;
+            background: linear-gradient(135deg, #f0f5ff 0%, #e8f0fe 100%) !important;
+        }
+        
+        /* Header Styling */
+        .main-header {
+            text-align: center;
+            padding: 30px 20px;
+            background: linear-gradient(135deg, #1a237e 0%, #303f9f 50%, #3949ab 100%);
+            border-radius: 15px;
+            margin-bottom: 25px;
+            color: white !important;
+            box-shadow: 0 8px 32px rgba(26, 35, 126, 0.3);
+            border: 1px solid rgba(255, 255, 255, 0.2);
+        }
+        
+        .main-header h1 {
+            color: white !important;
+            font-size: 2.5rem !important;
+            font-weight: 700 !important;
+            margin-bottom: 10px !important;
+            text-shadow: 2px 2px 4px rgba(0,0,0,0.3) !important;
+        }
+        
+        .main-header p {
+            color: #e8eaf6 !important;
+            font-size: 1.2rem !important;
+            margin: 0 !important;
+            font-weight: 400 !important;
+        }
+        
+        /* Feature Cards */
+        .feature-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
+            gap: 20px;
+            margin-bottom: 25px;
+        }
+        
+        .feature-card {
+            background: white !important;
+            padding: 25px !important;
+            border-radius: 12px !important;
+            border-left: 5px solid #3f51b5 !important;
+            box-shadow: 0 4px 15px rgba(0,0,0,0.1) !important;
+            transition: transform 0.3s ease, box-shadow 0.3s ease !important;
+            color: #1a237e !important;
+        }
+        
+        .feature-card:hover {
+            transform: translateY(-5px) !important;
+            box-shadow: 0 8px 25px rgba(0,0,0,0.15) !important;
+        }
+        
+        .feature-card h3 {
+            color: #1a237e !important;
+            font-size: 1.3rem !important;
+            font-weight: 600 !important;
+            margin-bottom: 10px !important;
+        }
+        
+        .feature-card p {
+            color: #3949ab !important;
+            font-size: 1rem !important;
+            line-height: 1.5 !important;
+            margin: 0 !important;
+        }
+        
+        /* Chat Container */
+        .chat-container {
+            background: white !important;
+            border-radius: 15px !important;
+            padding: 25px !important;
+            box-shadow: 0 8px 32px rgba(0,0,0,0.1) !important;
+            margin-bottom: 20px !important;
+        }
+        
+        /* Chatbot Styling */
+        .chatbot-container {
+            border: 2px solid #e8eaf6 !important;
+            border-radius: 12px !important;
+            background: #f8fbff !important;
+        }
+        
+        /* Override Gradio's default message styling */
+        .chatbot .message-wrap {
+            background: transparent !important;
+        }
+        
+        .chatbot .message.user {
+            background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb 100%) !important;
+            color: #0d47a1 !important;
+            border: 1px solid #90caf9 !important;
+            margin-left: 15% !important;
+            padding: 15px 20px !important;
+            border-radius: 15px 15px 5px 15px !important;
+            box-shadow: 0 2px 8px rgba(13, 71, 161, 0.2) !important;
+            font-weight: 500 !important;
+        }
+        
+        .chatbot .message.bot {
+            background: linear-gradient(135deg, #e8eaf6 0%, #c5cae9 100%) !important;
+            color: #1a237e !important;
+            border: 1px solid #9fa8da !important;
+            margin-right: 15% !important;
+            padding: 15px 20px !important;
+            border-radius: 15px 15px 15px 5px !important;
+            box-shadow: 0 2px 8px rgba(26, 35, 126, 0.2) !important;
+            font-weight: 500 !important;
+        }
+        
+        /* Force text color in chat messages */
+        .chatbot .message.user * {
+            color: #0d47a1 !important;
+        }
+        
+        .chatbot .message.bot * {
+            color: #1a237e !important;
+        }
+        
+        /* Ensure chat text is always visible */
+        .gradio-chatbot .chatbot .message {
+            color: inherit !important;
+        }
+        
+        .gradio-chatbot .chatbot .message p {
+            color: inherit !important;
+            margin: 5px 0 !important;
+            line-height: 1.5 !important;
+        }
+        
+        /* User message styling */
+        .gradio-chatbot .user {
+            background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb 100%) !important;
+            color: #0d47a1 !important;
+            border: 1px solid #90caf9 !important;
+            border-radius: 15px 15px 5px 15px !important;
+            margin-left: 15% !important;
+            margin-right: 5% !important;
+        }
+        
+        /* Bot message styling */
+        .gradio-chatbot .bot {
+            background: linear-gradient(135deg, #e8eaf6 0%, #c5cae9 100%) !important;
+            color: #1a237e !important;
+            border: 1px solid #9fa8da !important;
+            border-radius: 15px 15px 15px 5px !important;
+            margin-right: 15% !important;
+            margin-left: 5% !important;
+        }
+        
+        /* Input Styling */
+        .input-container {
+            background: white !important;
+            border-radius: 12px !important;
+            border: 2px solid #e8eaf6 !important;
+            padding: 5px !important;
+            margin-top: 15px !important;
+        }
+        
+        .input-container:focus-within {
+            border-color: #3f51b5 !important;
+            box-shadow: 0 0 10px rgba(63, 81, 181, 0.2) !important;
+        }
+        
+        /* Input text styling */
+        .input-container textarea,
+        .input-container input {
+            color: #1a237e !important;
+            background: white !important;
+            border: none !important;
+            font-size: 1rem !important;
+            font-weight: 500 !important;
+        }
+        
+        .input-container textarea::placeholder,
+        .input-container input::placeholder {
+            color: #6b7280 !important;
+            opacity: 0.8 !important;
+        }
+        
+        /* Override any Gradio input styling */
+        .gradio-textbox {
+            background: white !important;
+        }
+        
+        .gradio-textbox textarea {
+            color: #1a237e !important;
+            background: white !important;
+            border: 2px solid #e8eaf6 !important;
+            border-radius: 8px !important;
+            padding: 12px !important;
+            font-size: 1rem !important;
+        }
+        
+        .gradio-textbox textarea:focus {
+            border-color: #3f51b5 !important;
+            box-shadow: 0 0 10px rgba(63, 81, 181, 0.2) !important;
+            outline: none !important;
+        }
+        
+        /* Button Styling */
+        .btn-primary {
+            background: linear-gradient(135deg, #3f51b5 0%, #3949ab 100%) !important;
+            color: white !important;
+            border: none !important;
+            border-radius: 8px !important;
+            padding: 12px 24px !important;
+            font-weight: 600 !important;
+            font-size: 1rem !important;
+            transition: all 0.3s ease !important;
+            box-shadow: 0 4px 15px rgba(63, 81, 181, 0.3) !important;
+        }
+        
+        .btn-primary:hover {
+            background: linear-gradient(135deg, #3949ab 0%, #303f9f 100%) !important;
+            transform: translateY(-2px) !important;
+            box-shadow: 0 6px 20px rgba(63, 81, 181, 0.4) !important;
+        }
+        
+        .btn-secondary {
+            background: linear-gradient(135deg, #6c757d 0%, #5a6268 100%) !important;
+            color: white !important;
+            border: none !important;
+            border-radius: 8px !important;
+            padding: 12px 24px !important;
+            font-weight: 600 !important;
+            transition: all 0.3s ease !important;
+        }
+        
+        .btn-secondary:hover {
+            background: linear-gradient(135deg, #5a6268 0%, #495057 100%) !important;
+            transform: translateY(-2px) !important;
+        }
+        
+        /* Sidebar Styling */
+        .sidebar {
+            background: white !important;
+            border-radius: 15px !important;
+            padding: 25px !important;
+            box-shadow: 0 8px 32px rgba(0,0,0,0.1) !important;
+            height: fit-content !important;
+        }
+        
+        .sidebar h3 {
+            color: #1a237e !important;
+            font-size: 1.4rem !important;
+            font-weight: 600 !important;
+            margin-bottom: 15px !important;
+            padding-bottom: 10px !important;
+            border-bottom: 2px solid #e8eaf6 !important;
+        }
+        
+        .sidebar ul {
+            list-style: none !important;
+            padding: 0 !important;
+            margin: 0 !important;
+        }
+        
+        .sidebar li {
+            color: #3949ab !important;
+            padding: 8px 0 !important;
+            border-bottom: 1px solid #f0f5ff !important;
+            font-size: 0.95rem !important;
+            line-height: 1.4 !important;
+        }
+        
+        .sidebar strong {
+            color: #1a237e !important;
+            font-weight: 600 !important;
+        }
+        
+        /* Examples Section */
+        .examples-section {
+            background: #f8fbff !important;
+            padding: 20px !important;
+            border-radius: 10px !important;
+            margin-top: 20px !important;
+            border: 1px solid #e8eaf6 !important;
+        }
+        
+        /* Footer Styling */
+        .footer {
+            text-align: center;
+            padding: 25px;
+            background: linear-gradient(135deg, #1a237e 0%, #303f9f 100%);
+            border-radius: 15px;
+            margin-top: 30px;
+            color: white !important;
+            box-shadow: 0 8px 32px rgba(26, 35, 126, 0.3);
+        }
+        
+        .footer p {
+            color: white !important;
+            margin: 5px 0 !important;
+            font-size: 1rem !important;
+        }
+        
+        .footer strong {
+            color: #e8eaf6 !important;
+            font-size: 1.2rem !important;
+        }
+        
+        /* Responsive Design */
+        @media (max-width: 768px) {
+            .gradio-container {
+                padding: 10px !important;
+            }
+            
+            .main-header {
+                padding: 20px 15px !important;
+            }
+            
+            .main-header h1 {
+                font-size: 2rem !important;
+            }
+            
+            .feature-grid {
+                grid-template-columns: 1fr !important;
+                gap: 15px !important;
+            }
+            
+            .chat-container, .sidebar {
+                padding: 15px !important;
+            }
+            
+            .feature-card {
+                padding: 20px !important;
+            }
+        }
+        
+        /* Text Visibility Fixes */
+        .gr-textbox textarea {
+            color: #1a237e !important;
+            background: white !important;
+        }
+        
+        .gr-textbox label {
+            color: #1a237e !important;
+            font-weight: 600 !important;
+        }
+        
+        /* Loading Animation */
+        .loading {
+            display: inline-block;
+            width: 20px;
+            height: 20px;
+            border: 3px solid #e8eaf6;
+            border-radius: 50%;
+            border-top-color: #3f51b5;
+            animation: spin 1s ease-in-out infinite;
+        }
+        
+        @keyframes spin {
+            to { transform: rotate(360deg); }
+        }
+        """
+    ) as iface:
+        
+        # Header
+        gr.HTML("""
+        <div class="main-header">
+            <h1>💬 AI Assistant - Your Intelligent Helper</h1>
+            <p>Conversational AI powered by advanced language models ready to assist with any topic</p>
+        </div>
+        """)
+        
+        # Feature highlights
+        gr.HTML("""
+        <div class="feature-grid">
+            <div class="feature-card">
+                <h3>🧠 Advanced AI</h3>
+                <p>State-of-the-art language model capable of understanding and responding to a wide range of topics with human-like conversation.</p>
+            </div>
+            <div class="feature-card">
+                <h3>🌐 Comprehensive Knowledge</h3>
+                <p>Access to vast information across science, technology, history, arts, and general knowledge topics.</p>
+            </div>
+            <div class="feature-card">
+                <h3>💡 Adaptive Responses</h3>
+                <p>Dynamic responses that adjust to your needs - from simple explanations to in-depth technical discussions.</p>
+            </div>
+        </div>
+        """)
+        
+        # Main chat area
+        with gr.Row(equal_height=True):
+            with gr.Column(scale=7):
+                gr.HTML('<div class="chat-container">')
+                
+                chatbot = gr.Chatbot(
+                    value=[(None, greet())],
+                    height=520,
+                    label="💬 Chat with AI Assistant",
+                    show_label=True,
+                    container=True,
+                    bubble_full_width=False,
+                    avatar_images=(
+                        "https://cdn-icons-png.flaticon.com/512/1077/1077012.png",  # User
+                        "https://cdn-icons-png.flaticon.com/512/1998/1998667.png"   # Bot
+                    ),
+                    elem_classes=["chatbot-container"]
+                )
+                
+                with gr.Row():
+                    msg = gr.Textbox(
+                        label="💬 Ask me anything...",
+                        placeholder="Example: 'Explain quantum computing' or 'Help me write a poem' or 'What's the history of Rome?'",
+                        lines=2,
+                        max_lines=4,
+                        scale=4,
+                        elem_classes=["input-container"]
+                    )
+                
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        submit_btn = gr.Button("🚀 Send Message", variant="primary", elem_classes=["btn-primary"])
+                    with gr.Column(scale=1):
+                        clear_btn = gr.Button("🗑️ Clear Chat", variant="secondary", elem_classes=["btn-secondary"])
+                
+                gr.HTML('</div>')
+            
+            with gr.Column(scale=3):
+                gr.HTML("""
+                <div class="sidebar">
+                    <h3>📚 Knowledge Areas</h3>
+                    <ul>
+                        <li><strong>🔬 Science:</strong> Physics, Chemistry, Biology</li>
+                        <li><strong>💻 Technology:</strong> AI, Programming, Gadgets</li>
+                        <li><strong>📜 History:</strong> Ancient, Medieval, Modern</li>
+                        <li><strong>🎨 Arts:</strong> Literature, Music, Visual Arts</li>
+                        <li><strong>🌍 World:</strong> Cultures, Geography, Politics</li>
+                        <li><strong>🧠 Psychology:</strong> Mind, Behavior, Relationships</li>
+                        <li><strong>💼 Business:</strong> Economics, Finance, Marketing</li>
+                    </ul>
+                    
+                    <div class="examples-section">
+                        <h3>💡 Try These Questions</h3>
+                        <ul>
+                            <li>"Explain photosynthesis simply"</li>
+                            <li>"How does blockchain work?"</li>
+                            <li>"Summarize World War 2"</li>
+                            <li>"Write a haiku about nature"</li>
+                            <li>"Python vs JavaScript comparison"</li>
+                            <li>"Tips for public speaking"</li>
+                        </ul>
+                    </div>
+                    
+                    <div style="margin-top: 20px; padding: 15px; background: #f0f5ff; border-radius: 8px; text-align: center;">
+                        <p style="margin: 0; color: #1a237e; font-weight: 600;">🤖 AI Status</p>
+                        <p style="margin: 5px 0 0 0; color: #3949ab; font-size: 0.9rem;">Ready to help!</p>
+                    </div>
+                </div>
+                """)
+        
+        # Quick action buttons
+        gr.HTML("""
+        <div style="margin: 20px 0; text-align: center;">
+            <h3 style="color: #1a237e; margin-bottom: 15px;">🚀 Quick Start Topics</h3>
+        </div>
+        """)
+        
+        with gr.Row():
+            tech_btn = gr.Button("💻 Technology", elem_classes=["btn-primary"])
+            science_btn = gr.Button("🔬 Science", elem_classes=["btn-primary"])
+            history_btn = gr.Button("📜 History", elem_classes=["btn-primary"])
+            creative_btn = gr.Button("🎨 Creative", elem_classes=["btn-primary"])
+        
+        # Function to handle chat
+        def respond(message, chat_history):
+            if not message.strip():
+                return chat_history, ""
+            
+            response = assistant.process_message(message)
+            chat_history.append((message, response))
+            return chat_history, ""
+        
+        # Function to set quick questions
+        def set_quick_question(question):
+            return question
+        
+        # Event handlers for main chat
+        submit_btn.click(respond, [msg, chatbot], [chatbot, msg])
+        msg.submit(respond, [msg, chatbot], [chatbot, msg])
+        clear_btn.click(lambda: ([(None, greet())], ""), outputs=[chatbot, msg])
+        
+        # Quick action button handlers
+        tech_btn.click(
+            set_quick_question,
+            inputs=gr.State("Explain artificial intelligence in simple terms"),
+            outputs=msg
+        )
+        
+        science_btn.click(
+            set_quick_question,
+            inputs=gr.State("What's the difference between classical and quantum physics?"),
+            outputs=msg
+        )
+        
+        history_btn.click(
+            set_quick_question,
+            inputs=gr.State("Summarize the key events of the Renaissance period"),
+            outputs=msg
+        )
+        
+        creative_btn.click(
+            set_quick_question,
+            inputs=gr.State("Write a short poem about the ocean"),
+            outputs=msg
+        )
+        
+        # Footer
+        gr.HTML("""
+        <div class="footer">
+            <p><strong>💬 AI Assistant - Your Intelligent Helper</strong></p>
+            <p>Powered by advanced language models • Comprehensive knowledge base • Adaptive conversation</p>
+            <p>© 2025 AI Assistant | Providing intelligent conversation through cutting-edge AI</p>
+        </div>
+        """)
+    
+    return iface
 
+# Create and launch the interface
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+    interface = create_interface()
+    interface.launch(
+        share=True,
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_api=False
+    )