Update app.py

app.py

@@ -1,18 +1,12 @@
-# EDUTUTOR AI with Google Authentication
-# Note: This collects user data - ensure you have proper privacy policy and user consent
+# 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
-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"
@@ -26,11 +20,13 @@ class EduTutorAI:
         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,
@@ -39,6 +35,7 @@ class EduTutorAI:
                 low_cpu_mem_usage=True
             )
             
+            # Create pipeline
             self.pipe = pipeline(
                 "text-generation",
                 model=self.model,
@@ -70,16 +67,12 @@ 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"""
+    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:
-            # 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)
             
@@ -96,16 +89,16 @@ Please provide a comprehensive yet accessible explanation:"""
             
             # 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({
-                "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()
+                "response": ai_response
             })
             
             return ai_response
@@ -113,36 +106,29 @@ Please provide a comprehensive yet accessible explanation:"""
         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}")
+    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()
 
-# 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
-
+# Load model function for Gradio
 def initialize_model():
     """Initialize the model and return status"""
     success = edututor.load_model()
@@ -151,54 +137,18 @@ def initialize_model():
     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"""
+# 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!"
     
-    # 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)
+    response = edututor.generate_response(question, subject, difficulty, max_length)
     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
+# Create Gradio interface
 def create_interface():
-    """Create the EDUTUTOR AI Gradio interface with authentication"""
+    """Create the EDUTUTOR AI Gradio interface"""
     
     with gr.Blocks(
         title="🎓 EDUTUTOR AI - Your Personal Learning Assistant",
@@ -215,13 +165,6 @@ def create_interface():
             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:
         
@@ -234,26 +177,6 @@ def create_interface():
         </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():
@@ -307,14 +230,15 @@ def create_interface():
                 # Quick actions
                 with gr.Group():
                     gr.Markdown("### ⚡ Quick Actions")
-                    analytics_button = gr.Button("📊 View Analytics")
+                    history_button = gr.Button("📚 View Learning History")
+                    clear_button = gr.Button("🗑️ Clear History")
                     
                     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
+                    - Experiment with different difficulty levels
                     """)
         
         # Response section
@@ -326,22 +250,16 @@ def create_interface():
                 interactive=False
             )
         
-        # Analytics section
+        # History section
         with gr.Row():
-            analytics_output = gr.Textbox(
-                label="📊 Usage Analytics",
+            history_output = gr.Textbox(
+                label="📚 Learning Session History",
                 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
@@ -349,24 +267,38 @@ def create_interface():
         
         ask_button.click(
             fn=chat_with_edututor,
-            inputs=[question_input, subject_dropdown, difficulty_dropdown, max_length_slider, user_info_state],
+            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
         )
         
-        analytics_button.click(
-            fn=get_user_analytics,
-            outputs=analytics_output
+        history_button.click(
+            fn=edututor.get_conversation_history,
+            outputs=history_output
         ).then(
             fn=lambda: gr.update(visible=True),
-            outputs=analytics_output
+            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 with User Authentication...")
+    print("🎓 Starting EDUTUTOR AI...")
     print("=" * 50)
     
+    # Create and launch interface
     demo = create_interface()
+    
+    # Launch for Hugging Face Spaces (simplified)
     demo.launch()