Update app.py

app.py

@@ -1,304 +1,372 @@
-# EDUTUTOR AI - Complete app.py for Hugging Face Spaces
-# An intelligent AI tutor powered by IBM Granite that provides personalized educational explanations across multiple subjects and difficulty levels.
-
-import gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-import warnings
-warnings.filterwarnings("ignore")
-
-class EduTutorAI:
-    def __init__(self):
-        self.model_name = "ibm-granite/granite-3.3-2b-instruct"
-        self.tokenizer = None
-        self.model = None
-        self.pipe = None
-        self.conversation_history = []
-        
-    def load_model(self):
-        """Load the Granite model and tokenizer"""
-        try:
-            print("Loading EDUTUTOR AI model...")
-            
-            # Load tokenizer
-            self.tokenizer = AutoTokenizer.from_pretrained(
-                self.model_name,
-                trust_remote_code=True
-            )
-            
-            # Load model with optimization for deployment
-            self.model = AutoModelForCausalLM.from_pretrained(
-                self.model_name,
-                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-                device_map="auto" if torch.cuda.is_available() else None,
-                trust_remote_code=True,
-                low_cpu_mem_usage=True
-            )
-            
-            # Create pipeline
-            self.pipe = pipeline(
-                "text-generation",
-                model=self.model,
-                tokenizer=self.tokenizer,
-                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-                device_map="auto" if torch.cuda.is_available() else None
-            )
-            
-            print("✅ Model loaded successfully!")
-            return True
-            
-        except Exception as e:
-            print(f"❌ Error loading model: {str(e)}")
-            return False
-    
-    def create_educational_prompt(self, user_question, subject="General", difficulty="Intermediate"):
-        """Create an educational prompt template"""
-        system_prompt = f"""You are EDUTUTOR AI, an expert educational tutor specializing in {subject}. 
-Your role is to:
-1. Provide clear, accurate explanations at {difficulty} level
-2. Break down complex concepts into digestible parts
-3. Use examples and analogies when helpful
-4. Encourage learning through questions
-5. Be patient and supportive
-
-Student Question: {user_question}
-
-Please provide a comprehensive yet accessible explanation:"""
-        
-        return system_prompt
-    
-    def generate_response(self, question, subject, difficulty, max_length=512):
-        """Generate educational response"""
-        if not self.pipe:
-            return "❌ Model not loaded. Please wait for initialization."
-        
-        try:
-            # Create educational prompt
-            prompt = self.create_educational_prompt(question, subject, difficulty)
-            
-            # Generate response
-            response = self.pipe(
-                prompt,
-                max_length=max_length,
-                num_return_sequences=1,
-                temperature=0.7,
-                do_sample=True,
-                pad_token_id=self.tokenizer.eos_token_id,
-                truncation=True
-            )
-            
-            # Extract the generated text
-            full_response = response[0]['generated_text']
-            
-            # Remove the prompt to get only the AI response
-            ai_response = full_response.replace(prompt, "").strip()
-            
-            # Store in conversation history
-            self.conversation_history.append({
-                "question": question,
-                "subject": subject,
-                "difficulty": difficulty,
-                "response": ai_response
-            })
-            
-            return ai_response
-            
-        except Exception as e:
-            return f"❌ Error generating response: {str(e)}"
-    
-    def get_conversation_history(self):
-        """Get formatted conversation history"""
-        if not self.conversation_history:
-            return "No conversation history yet."
-        
-        history = "📚 **EDUTUTOR AI - Learning Session History**\n\n"
-        for i, conv in enumerate(self.conversation_history[-5:], 1):  # Show last 5 conversations
-            history += f"**Session {i}:**\n"
-            history += f"🎯 Subject: {conv['subject']} | Level: {conv['difficulty']}\n"
-            history += f"❓ Question: {conv['question']}\n"
-            history += f"💡 Response: {conv['response'][:200]}...\n\n"
-        
-        return history
-    
-    def clear_history(self):
-        """Clear conversation history"""
-        self.conversation_history = []
-        return "🗑️ Conversation history cleared!"
-
-# Initialize the EduTutor AI
-edututor = EduTutorAI()
-
-# Load model function for Gradio
-def initialize_model():
-    """Initialize the model and return status"""
-    success = edututor.load_model()
-    if success:
-        return "✅ EDUTUTOR AI is ready! You can now start asking questions."
-    else:
-        return "❌ Failed to load model. Please try again."
-
-# Main chat function
-def chat_with_edututor(question, subject, difficulty, max_length):
-    """Main chat interface function"""
-    if not question.strip():
-        return "Please enter a question to get started!"
-    
-    response = edututor.generate_response(question, subject, difficulty, max_length)
-    return response
-
-# Create Gradio interface
-def create_interface():
-    """Create the EDUTUTOR AI Gradio interface"""
-    
-    with gr.Blocks(
-        title="🎓 EDUTUTOR AI - Your Personal Learning Assistant",
-        theme=gr.themes.Soft(),
-        css="""
-        .gradio-container {
-            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-        }
-        .main-header {
-            text-align: center;
-            background: linear-gradient(45deg, #667eea 0%, #764ba2 100%);
-            color: white;
-            padding: 20px;
-            border-radius: 10px;
-            margin-bottom: 20px;
-        }
-        """
-    ) as interface:
-        
-        # Header
-        gr.HTML("""
-        <div class="main-header">
-            <h1>🎓 EDUTUTOR AI</h1>
-            <p>Your Intelligent Educational Tutor powered by IBM Granite 3.3-2B</p>
-            <p><em>Ask questions, learn concepts, and expand your knowledge!</em></p>
-        </div>
-        """)
-        
-        # Model initialization section
-        with gr.Row():
-            with gr.Column():
-                init_button = gr.Button("🚀 Initialize EDUTUTOR AI", variant="primary", size="lg")
-                init_status = gr.Textbox(
-                    label="Initialization Status",
-                    value="Click 'Initialize EDUTUTOR AI' to start",
-                    interactive=False
-                )
-        
-        # Main interface
-        with gr.Row():
-            with gr.Column(scale=2):
-                # Input section
-                with gr.Group():
-                    gr.Markdown("### 📝 Ask Your Question")
-                    question_input = gr.Textbox(
-                        label="Your Question",
-                        placeholder="e.g., Explain quantum physics, How does photosynthesis work?, What is machine learning?",
-                        lines=3
-                    )
-                    
-                    with gr.Row():
-                        subject_dropdown = gr.Dropdown(
-                            choices=[
-                                "General", "Mathematics", "Physics", "Chemistry", 
-                                "Biology", "Computer Science", "History", "Literature",
-                                "Geography", "Economics", "Philosophy"
-                            ],
-                            value="General",
-                            label="Subject Area"
-                        )
-                        
-                        difficulty_dropdown = gr.Dropdown(
-                            choices=["Beginner", "Intermediate", "Advanced"],
-                            value="Intermediate",
-                            label="Difficulty Level"
-                        )
-                    
-                    max_length_slider = gr.Slider(
-                        minimum=100,
-                        maximum=1000,
-                        value=512,
-                        step=50,
-                        label="Response Length (tokens)"
-                    )
-                    
-                    ask_button = gr.Button("🤔 Ask EDUTUTOR AI", variant="primary")
-            
-            with gr.Column(scale=1):
-                # Quick actions
-                with gr.Group():
-                    gr.Markdown("### ⚡ Quick Actions")
-                    history_button = gr.Button("📚 View Learning History")
-                    clear_button = gr.Button("🗑️ Clear History")
-                    
-                    gr.Markdown("### 💡 Tips")
-                    gr.Markdown("""
-                    - Be specific with your questions
-                    - Select appropriate subject and difficulty
-                    - Use follow-up questions for deeper understanding
-                    - Experiment with different difficulty levels
-                    """)
-        
-        # Response section
-        with gr.Row():
-            response_output = gr.Textbox(
-                label="🎓 EDUTUTOR AI Response",
-                lines=15,
-                max_lines=20,
-                interactive=False
-            )
-        
-        # History section
-        with gr.Row():
-            history_output = gr.Textbox(
-                label="📚 Learning Session History",
-                lines=10,
-                interactive=False,
-                visible=False
-            )
-        
-        # Event handlers
-        init_button.click(
-            fn=initialize_model,
-            outputs=init_status
-        )
-        
-        ask_button.click(
-            fn=chat_with_edututor,
-            inputs=[question_input, subject_dropdown, difficulty_dropdown, max_length_slider],
-            outputs=response_output
-        )
-        
-        question_input.submit(
-            fn=chat_with_edututor,
-            inputs=[question_input, subject_dropdown, difficulty_dropdown, max_length_slider],
-            outputs=response_output
-        )
-        
-        history_button.click(
-            fn=edututor.get_conversation_history,
-            outputs=history_output
-        ).then(
-            fn=lambda: gr.update(visible=True),
-            outputs=history_output
-        )
-        
-        clear_button.click(
-            fn=edututor.clear_history,
-            outputs=init_status
-        )
-    
-    return interface
-
-# Launch the application
-if __name__ == "__main__":
-    print("🎓 Starting EDUTUTOR AI...")
-    print("=" * 50)
-    
-    # Create and launch interface
-    demo = create_interface()
-    
-    # Launch for Hugging Face Spaces (simplified)
+# EDUTUTOR AI with Google Authentication
+# Note: This collects user data - ensure you have proper privacy policy and user consent
+
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import warnings
+import datetime
+import json
+warnings.filterwarnings("ignore")
+
+# User session storage (in-memory for demo - use database in production)
+user_sessions = {}
+usage_analytics = []
+
+class EduTutorAI:
+    def __init__(self):
+        self.model_name = "ibm-granite/granite-3.3-2b-instruct"
+        self.tokenizer = None
+        self.model = None
+        self.pipe = None
+        self.conversation_history = []
+        
+    def load_model(self):
+        """Load the Granite model and tokenizer"""
+        try:
+            print("Loading EDUTUTOR AI model...")
+            
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                self.model_name,
+                trust_remote_code=True
+            )
+            
+            self.model = AutoModelForCausalLM.from_pretrained(
+                self.model_name,
+                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+                device_map="auto" if torch.cuda.is_available() else None,
+                trust_remote_code=True,
+                low_cpu_mem_usage=True
+            )
+            
+            self.pipe = pipeline(
+                "text-generation",
+                model=self.model,
+                tokenizer=self.tokenizer,
+                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+                device_map="auto" if torch.cuda.is_available() else None
+            )
+            
+            print("✅ Model loaded successfully!")
+            return True
+            
+        except Exception as e:
+            print(f"❌ Error loading model: {str(e)}")
+            return False
+    
+    def create_educational_prompt(self, user_question, subject="General", difficulty="Intermediate"):
+        """Create an educational prompt template"""
+        system_prompt = f"""You are EDUTUTOR AI, an expert educational tutor specializing in {subject}. 
+Your role is to:
+1. Provide clear, accurate explanations at {difficulty} level
+2. Break down complex concepts into digestible parts
+3. Use examples and analogies when helpful
+4. Encourage learning through questions
+5. Be patient and supportive
+
+Student Question: {user_question}
+
+Please provide a comprehensive yet accessible explanation:"""
+        
+        return system_prompt
+    
+    def generate_response(self, question, subject, difficulty, max_length, user_info=None):
+        """Generate educational response with user tracking"""
+        if not self.pipe:
+            return "❌ Model not loaded. Please wait for initialization."
+        
+        try:
+            # Log user interaction
+            if user_info:
+                self.log_user_interaction(user_info, question, subject, difficulty)
+            
+            # Create educational prompt
+            prompt = self.create_educational_prompt(question, subject, difficulty)
+            
+            # Generate response
+            response = self.pipe(
+                prompt,
+                max_length=max_length,
+                num_return_sequences=1,
+                temperature=0.7,
+                do_sample=True,
+                pad_token_id=self.tokenizer.eos_token_id,
+                truncation=True
+            )
+            
+            # Extract the generated text
+            full_response = response[0]['generated_text']
+            ai_response = full_response.replace(prompt, "").strip()
+            
+            # Store in conversation history
+            self.conversation_history.append({
+                "user": user_info.get("name", "Anonymous") if user_info else "Anonymous",
+                "question": question,
+                "subject": subject,
+                "difficulty": difficulty,
+                "response": ai_response,
+                "timestamp": datetime.datetime.now().isoformat()
+            })
+            
+            return ai_response
+            
+        except Exception as e:
+            return f"❌ Error generating response: {str(e)}"
+    
+    def log_user_interaction(self, user_info, question, subject, difficulty):
+        """Log user interaction for analytics"""
+        interaction = {
+            "timestamp": datetime.datetime.now().isoformat(),
+            "user_name": user_info.get("name", "Unknown"),
+            "user_email": user_info.get("email", "Unknown"),
+            "question": question,
+            "subject": subject,
+            "difficulty": difficulty
+        }
+        usage_analytics.append(interaction)
+        print(f"📊 Logged interaction: {user_info.get('name', 'Anonymous')} asked about {subject}")
+
+# Initialize the EduTutor AI
+edututor = EduTutorAI()
+
+# Authentication functions
+def authenticate_user(username, password):
+    """Simple authentication (replace with Google OAuth in production)"""
+    # This is a demo - in production, use proper Google OAuth
+    if username and password:
+        user_info = {
+            "name": username,
+            "email": f"{username}@example.com",
+            "login_time": datetime.datetime.now().isoformat()
+        }
+        user_sessions[username] = user_info
+        return True, f"✅ Welcome {username}! You are now logged in.", user_info
+    return False, "❌ Please enter valid credentials.", None
+
+def initialize_model():
+    """Initialize the model and return status"""
+    success = edututor.load_model()
+    if success:
+        return "✅ EDUTUTOR AI is ready! You can now start asking questions."
+    else:
+        return "❌ Failed to load model. Please try again."
+
+def chat_with_edututor(question, subject, difficulty, max_length, user_info):
+    """Main chat interface function with user tracking"""
+    if not question.strip():
+        return "Please enter a question to get started!"
+    
+    # Parse user_info if it's a string
+    if isinstance(user_info, str) and user_info.startswith("{"):
+        try:
+            user_info = json.loads(user_info)
+        except:
+            user_info = None
+    
+    response = edututor.generate_response(question, subject, difficulty, max_length, user_info)
+    return response
+
+def get_user_analytics():
+    """Get analytics dashboard"""
+    if not usage_analytics:
+        return "No user interactions logged yet."
+    
+    total_users = len(set([interaction["user_email"] for interaction in usage_analytics]))
+    total_questions = len(usage_analytics)
+    
+    subjects = {}
+    for interaction in usage_analytics:
+        subject = interaction["subject"]
+        subjects[subject] = subjects.get(subject, 0) + 1
+    
+    analytics = f"""
+📊 **EDUTUTOR AI Analytics Dashboard**
+
+👥 **Total Unique Users**: {total_users}
+❓ **Total Questions Asked**: {total_questions}
+
+📚 **Popular Subjects**:
+"""
+    for subject, count in sorted(subjects.items(), key=lambda x: x[1], reverse=True):
+        analytics += f"• {subject}: {count} questions\n"
+    
+    analytics += f"\n🕐 **Recent Activity** (Last 5):\n"
+    for interaction in usage_analytics[-5:]:
+        analytics += f"• {interaction['user_name']} asked about {interaction['subject']} ({interaction['timestamp'][:19]})\n"
+    
+    return analytics
+
+# Create Gradio interface with authentication
+def create_interface():
+    """Create the EDUTUTOR AI Gradio interface with authentication"""
+    
+    with gr.Blocks(
+        title="🎓 EDUTUTOR AI - Your Personal Learning Assistant",
+        theme=gr.themes.Soft(),
+        css="""
+        .gradio-container {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+        }
+        .main-header {
+            text-align: center;
+            background: linear-gradient(45deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            padding: 20px;
+            border-radius: 10px;
+            margin-bottom: 20px;
+        }
+        .privacy-notice {
+            background: #f0f0f0;
+            padding: 15px;
+            border-radius: 5px;
+            margin: 10px 0;
+            border-left: 4px solid #ff6b6b;
+        }
+        """
+    ) as interface:
+        
+        # Header
+        gr.HTML("""
+        <div class="main-header">
+            <h1>🎓 EDUTUTOR AI</h1>
+            <p>Your Intelligent Educational Tutor powered by IBM Granite 3.3-2B</p>
+            <p><em>Ask questions, learn concepts, and expand your knowledge!</em></p>
+        </div>
+        """)
+        
+        # Privacy Notice
+        gr.HTML("""
+        <div class="privacy-notice">
+            <h3>🔒 Privacy Notice</h3>
+            <p><strong>Data Collection:</strong> This app collects user names, questions, and usage analytics to improve the learning experience.</p>
+            <p><strong>By using this app, you consent to this data collection.</strong></p>
+            <p>📧 Contact: For privacy concerns, contact the administrator.</p>
+        </div>
+        """)
+        
+        # Authentication Section
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown("### 🔐 Login to EDUTUTOR AI")
+                username_input = gr.Textbox(label="Username", placeholder="Enter your username")
+                password_input = gr.Textbox(label="Password", type="password", placeholder="Enter your password")
+                login_button = gr.Button("🔑 Login", variant="primary")
+                login_status = gr.Textbox(label="Login Status", value="Please login to continue", interactive=False)
+                user_info_state = gr.State(value=None)
+        
+        # Model initialization section
+        with gr.Row():
+            with gr.Column():
+                init_button = gr.Button("🚀 Initialize EDUTUTOR AI", variant="primary", size="lg")
+                init_status = gr.Textbox(
+                    label="Initialization Status",
+                    value="Click 'Initialize EDUTUTOR AI' to start",
+                    interactive=False
+                )
+        
+        # Main interface
+        with gr.Row():
+            with gr.Column(scale=2):
+                # Input section
+                with gr.Group():
+                    gr.Markdown("### 📝 Ask Your Question")
+                    question_input = gr.Textbox(
+                        label="Your Question",
+                        placeholder="e.g., Explain quantum physics, How does photosynthesis work?, What is machine learning?",
+                        lines=3
+                    )
+                    
+                    with gr.Row():
+                        subject_dropdown = gr.Dropdown(
+                            choices=[
+                                "General", "Mathematics", "Physics", "Chemistry", 
+                                "Biology", "Computer Science", "History", "Literature",
+                                "Geography", "Economics", "Philosophy"
+                            ],
+                            value="General",
+                            label="Subject Area"
+                        )
+                        
+                        difficulty_dropdown = gr.Dropdown(
+                            choices=["Beginner", "Intermediate", "Advanced"],
+                            value="Intermediate",
+                            label="Difficulty Level"
+                        )
+                    
+                    max_length_slider = gr.Slider(
+                        minimum=100,
+                        maximum=1000,
+                        value=512,
+                        step=50,
+                        label="Response Length (tokens)"
+                    )
+                    
+                    ask_button = gr.Button("🤔 Ask EDUTUTOR AI", variant="primary")
+            
+            with gr.Column(scale=1):
+                # Quick actions
+                with gr.Group():
+                    gr.Markdown("### ⚡ Quick Actions")
+                    analytics_button = gr.Button("📊 View Analytics")
+                    
+                    gr.Markdown("### 💡 Tips")
+                    gr.Markdown("""
+                    - Login to track your learning progress
+                    - Be specific with your questions
+                    - Select appropriate subject and difficulty
+                    - Use follow-up questions for deeper understanding
+                    """)
+        
+        # Response section
+        with gr.Row():
+            response_output = gr.Textbox(
+                label="🎓 EDUTUTOR AI Response",
+                lines=15,
+                max_lines=20,
+                interactive=False
+            )
+        
+        # Analytics section
+        with gr.Row():
+            analytics_output = gr.Textbox(
+                label="📊 Usage Analytics",
+                lines=10,
+                interactive=False,
+                visible=False
+            )
+        
+        # Event handlers
+        login_button.click(
+            fn=authenticate_user,
+            inputs=[username_input, password_input],
+            outputs=[gr.State(), login_status, user_info_state]
+        )
+        
+        init_button.click(
+            fn=initialize_model,
+            outputs=init_status
+        )
+        
+        ask_button.click(
+            fn=chat_with_edututor,
+            inputs=[question_input, subject_dropdown, difficulty_dropdown, max_length_slider, user_info_state],
+            outputs=response_output
+        )
+        
+        analytics_button.click(
+            fn=get_user_analytics,
+            outputs=analytics_output
+        ).then(
+            fn=lambda: gr.update(visible=True),
+            outputs=analytics_output
+        )
+    
+    return interface
+
+# Launch the application
+if __name__ == "__main__":
+    print("🎓 Starting EDUTUTOR AI with User Authentication...")
+    print("=" * 50)
+    
+    demo = create_interface()
     demo.launch()