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| from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM | |
| import gradio as gr | |
| import torch | |
| # Step 1: Set device | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Step 2: Load model & tokenizer | |
| try: | |
| model_name = "ibm-granite/granite-3.3-2b-instruct" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| generator = pipeline( | |
| "text-generation", | |
| model=model, | |
| tokenizer=tokenizer, | |
| device=0 if device == "cuda" else -1, | |
| max_new_tokens=700 | |
| ) | |
| print("✅ Model and tokenizer loaded successfully.") | |
| except Exception as e: | |
| print(f"❌ Error loading model/tokenizer: {e}") | |
| generator = None | |
| # Utility function to generate text | |
| def generate_response(prompt): | |
| if generator is None: | |
| return "❌ Error: Model not loaded." | |
| response = generator(prompt) | |
| return response[0]["generated_text"] | |
| # Functionality 1: Generate Quiz | |
| def generate_quiz(subject: str, score: int, num_questions: int): | |
| prompt = f""" | |
| You are an expert tutor. | |
| Topic: {subject} | |
| Student Score: {score}/10 | |
| Generate {num_questions} multiple-choice questions to help the student's understanding of the topic '{subject}'. | |
| Each question must: | |
| - Be relevant and based only on the topic: '{subject}' | |
| - Be logically sound and factually correct | |
| - Have 4 answer options labeled (A–D) | |
| - All options should be plausible and follow the same format or pattern | |
| - Avoid giving away the correct answer by formatting (e.g., using acronyms only in one option) | |
| - Clearly mark the correct answer at the end of each question like this: Correct Answer: B | |
| Use this exact format: | |
| Qn: <question> | |
| A. <option A> | |
| B. <option B> | |
| C. <option C> | |
| D. <option D> | |
| Correct Answer: <correct option letter> | |
| """ | |
| return generate_response(prompt) | |
| # Functionality 2: Feedback Generator | |
| def generate_feedback(score): | |
| prompt = f"A student scored {score}/10. Provide a friendly, personalized feedback message with suggestions to improve." | |
| return generate_response(prompt) | |
| # Functionality 3: Recommended Resources | |
| def generate_resources(subject): | |
| prompt = f"Provide 5 free, high-quality online learning resources (websites, YouTube, courses) to study the topic: {subject}." | |
| return generate_response(prompt) | |
| # Functionality 4: Summary Notes | |
| def generate_summary_notes(subject): | |
| prompt = f"Give a beginner-friendly summary of the topic '{subject}' with clear and simple explanation." | |
| return generate_response(prompt) | |
| # Functionality 5: Adaptive Question Suggestion | |
| def generate_adaptive_question(subject, score): | |
| difficulty = "easy" if score <= 4 else "medium" if score <= 7 else "hard" | |
| prompt = f"Generate one {difficulty}-level multiple choice question on the topic: {subject}." | |
| return generate_response(prompt) | |
| # Functionality 6: Concept-wise MCQ Generation | |
| def generate_concept_questions(subject, concept): | |
| prompt = f"Generate 3 multiple-choice questions focused on the sub-topic '{concept}' under '{subject}'." | |
| return generate_response(prompt) | |
| # Functionality 7: Fill in the Blanks | |
| def generate_fill_in_the_blanks(subject): | |
| prompt = f""" | |
| Generate 5 fill-in-the-blank questions with answers on the topic: '{subject}'. | |
| Format: | |
| Q1: <question with blank> | |
| Answer: <correct word or phrase> | |
| Ensure each blank tests an important concept from the topic. | |
| """ | |
| return generate_response(prompt) | |
| # Functionality 8: Important Points | |
| def generate_important_points(subject): | |
| prompt = f""" | |
| List the 7 most important points a beginner should remember when studying the topic: '{subject}'. Use short, clear bullet points. | |
| """ | |
| return generate_response(prompt) | |
| # Functionality 9: Flashcard Format Output | |
| def generate_flashcards(subject, num_flashcards): | |
| prompt = f"Generate {num_flashcards} flashcards for the topic '{subject}'. Format each as: Q: <question> A: <answer>" | |
| return generate_response(prompt) | |
| # Functionality 10: Misconception Correction | |
| def generate_misconceptions(subject): | |
| prompt = f""" | |
| List common misconceptions students have when learning the topic: '{subject}'. For each one, provide a correct explanation. | |
| Format: | |
| Misconception: <wrong idea> | |
| Correction: <correct understanding> | |
| """ | |
| return generate_response(prompt) | |
| # Functionality 11: Confidence Score Explanation | |
| def confidence_analysis(score): | |
| prompt = f"A student scored {score}/10. Analyze their confidence level and suggest how to build stronger understanding in weak areas." | |
| return generate_response(prompt) | |
| # Functionality 12: Weekly Learning Plan Generator | |
| def generate_study_plan(subject, score): | |
| prompt = f"A student scored {score}/10 on the topic '{subject}'. Create a personalized 5-day learning plan to improve their understanding." | |
| return generate_response(prompt) | |
| # Gradio App | |
| def run_all(subject, score, num_questions, concept, flashcard_count): | |
| quiz = generate_quiz(subject, score, num_questions) | |
| feedback = generate_feedback(score) | |
| resources = generate_resources(subject) | |
| notes = generate_summary_notes(subject) | |
| adaptive = generate_adaptive_question(subject, score) | |
| concept_questions = generate_concept_questions(subject, concept) | |
| fill_blanks = generate_fill_in_the_blanks(subject) | |
| important_points = generate_important_points(subject) | |
| flashcards = generate_flashcards(subject, flashcard_count) | |
| misconceptions = generate_misconceptions(subject) | |
| confidence = confidence_analysis(score) | |
| study_plan = generate_study_plan(subject, score) | |
| return quiz, feedback, resources, notes, adaptive, concept_questions, fill_blanks, important_points, misconceptions, confidence, flashcards, study_plan | |
| interface = gr.Interface( | |
| fn=run_all, | |
| inputs=[ | |
| gr.Textbox(label="Topic (e.g., Algebra)"), | |
| gr.Slider(0, 10, step=1, label="Score (out of 10)"), | |
| gr.Slider(1, 10, step=1, label="Number of Questions"), | |
| gr.Textbox(label="Concept Name (e.g., Linear Equations)"), | |
| gr.Slider(1, 10, step=1, label="Number of Flashcards") | |
| ], | |
| outputs=[ | |
| gr.Textbox(label="Generated Quiz", show_copy_button=True), | |
| gr.Textbox(label="Personalized Feedback", show_copy_button=True), | |
| gr.Textbox(label="Learning Resources", show_copy_button=True), | |
| gr.Textbox(label="Summary Notes", show_copy_button=True), | |
| gr.Textbox(label="Adaptive Question", show_copy_button=True), | |
| gr.Textbox(label="Concept-Based Questions", show_copy_button=True), | |
| gr.Textbox(label="Fill in the Blanks", show_copy_button=True), | |
| gr.Textbox(label="Important Points", show_copy_button=True), | |
| gr.Textbox(label="Flashcards", show_copy_button=True), | |
| gr.Textbox(label="Misconception Correction", show_copy_button=True), | |
| gr.Textbox(label="Confidence Analysis", show_copy_button=True), | |
| gr.Textbox(label="Weekly Study Plan", show_copy_button=True) | |
| ], | |
| title="EduTutor AI – Personalized Learning & Assessment System", | |
| description="📚 Generate quizzes, feedback, flashcards, study plans, and more using IBM Granite LLM" | |
| ) | |
| interface.launch(debug=True) |