Update app.py

app.py

@@ -5,37 +5,39 @@ import torch
 # Step 1: Set device
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Step 2: Load tokenizer and model explicitly
+# 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=500
+        max_new_tokens=700
     )
     print("✅ Model and tokenizer loaded successfully.")
 except Exception as e:
     print(f"❌ Error loading model/tokenizer: {e}")
     generator = None
 
-# Step 3: Define generation functions
-def generate_quiz(subject: str, score: int, num_questions: int):
+# 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 understaning of the topic '{subject}'.
+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}'
@@ -54,40 +56,122 @@ C. <option C>
 D. <option D>
 Correct Answer: <correct option letter>
 """
+    return generate_response(prompt)
 
-    output = generator(prompt)
-    return output[0]["generated_text"]
-
+# Functionality 2: Feedback Generator
 def generate_feedback(score):
-    if generator is None:
-        return "❌ Error: Model not loaded."
+    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"""
-    A student scored {score}/10 on a recent test.
-    Provide a friendly, personalized feedback message including suggestions to improve further.
-    """
-    output = generator(prompt)
-    return output[0]["generated_text"]
-
-# Step 4: Gradio Interface
-def run_all(subject, score, num_questions):
+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)
-    return quiz, feedback
+    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="Enter Topic (e.g., Algebra)"),
+        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.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="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="AI-powered quiz and feedback generator using IBM Granite LLM"
+    description="📚 Generate quizzes, feedback, flashcards, study plans, and more using IBM Granite LLM"
 )
 
 interface.launch(debug=True)