Update app.py

app.py

@@ -1,227 +1,98 @@
 import re
 import random
 import gradio as gr
-import json
-import os
-from typing import Dict, List, Any
-
-# Try to import AI libraries
-try:
-    import openai
-    OPENAI_AVAILABLE = True
-except ImportError:
-    OPENAI_AVAILABLE = False
-
-try:
-    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
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
 
 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
+        # Load model on initialization
+        self.load_model()
     
     def load_model(self):
-        """Load AI model for advanced queries"""
+        """Load the AI model"""
         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:
-            print("🔧 Install transformers and torch for AI features")
+        try:
+            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
+            )
+            
+            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
+            return True
+            
+        except Exception as e:
+            print(f"Could not load AI model: {str(e)}")
             return False
     
-    def generate_response(self, query: str, context: str = "") -> str:
-        """Generate conversational AI response"""
+    def generate_response(self, query: str) -> str:
+        """Generate response using the AI model"""
         if not self.model_loaded:
-            if not self.load_model():
-                return self.generate_fallback_response(query, context)
+            return "Sorry, I'm having technical difficulties. Please try again later."
         
         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}
+            # Create prompt that encourages complete answers
+            prompt = f"""The following is a conversation with an AI assistant. The assistant is helpful, knowledgeable, and provides detailed answers.
 
-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:"
+User: {query}
+AI:"""
             
             response = self.generator(
-                full_prompt,
-                max_new_tokens=250,
+                prompt,
+                max_new_tokens=300,
                 do_sample=True,
-                temperature=0.7 + random.random() * 0.3,  # Vary temperature
+                temperature=0.7,
                 top_p=0.9,
                 pad_token_id=self.tokenizer.eos_token_id,
-                repetition_penalty=1.2,
+                repetition_penalty=1.1,
                 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
-                        
+                return response[0]["generated_text"].strip()
+                
         except Exception as e:
             print(f"AI generation error: {e}")
         
-        # Fallback response
-        return self.generate_fallback_response(query, context)
-    
-    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..."
-            ])
-    
-    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 "I couldn't generate a response. Please try asking differently."
+
+    def process_message(self, message: str) -> str:
+        """Process user message and generate response"""
+        # Check for name introduction
+        name_response = self.get_user_name(message)
+        if name_response:
+            return name_response
         
-        return random.choice(responses[category])
+        # Generate AI response
+        return self.generate_response(message)
 
     def get_user_name(self, message):
+        """Check if user is introducing themselves"""
         name_patterns = [
             r"my name is (\w+)",
             r"i'm (\w+)",
@@ -233,26 +104,9 @@ Please provide a helpful response that addresses the user's needs while followin
             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 f"Nice to meet you, {self.user_name}! How can I help you today?"
         return None
 
-    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()
 
@@ -261,561 +115,42 @@ def chat_response(message, history):
     if not message.strip():
         return "Please enter a message!"
     
-    response = assistant.process_message(message)
-    return response
+    return assistant.process_message(message)
 
 def greet():
-    greetings = [
+    return random.choice([
         "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)
+        "Hi there! What would you like to know?",
+        "Welcome! I'm ready to answer your questions."
+    ])
 
 # 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); }
-        }
-        """
+        title="AI Assistant",
+        theme=gr.themes.Soft()
     ) 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
+        chatbot = gr.Chatbot(
+            value=[(None, greet())],
+            height=500,
+            label="AI Assistant"
         )
         
-        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
-        )
+        with gr.Row():
+            msg = gr.Textbox(
+                label="Type your message",
+                placeholder="Ask me anything...",
+                lines=2
+            )
+            submit_btn = gr.Button("Send", variant="primary")
         
-        # 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>
-        """)
+        msg.submit(chat_response, [msg, chatbot], [chatbot])
+        submit_btn.click(chat_response, [msg, chatbot], [chatbot])
     
     return iface
 
-# Create and launch the interface
+# Launch the interface
 if __name__ == "__main__":
     interface = create_interface()
-    interface.launch(
-        share=True,
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_api=False
-    )
+    interface.launch()