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| # 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) | |
| demo.launch() | |