Update app.py

app.py

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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 tokenizer and model explicitly
+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
+    )
+    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):
+    if generator is None:
+        return "❌ Error: Model not loaded."
+
+    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}'.
+
+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>
+"""
+
+    output = generator(prompt)
+    return output[0]["generated_text"]
+
+def generate_feedback(score):
+    if generator is None:
+        return "❌ Error: Model not loaded."
+
+    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):
+    quiz = generate_quiz(subject, score, num_questions)
+    feedback = generate_feedback(score)
+    return quiz, feedback
+
+interface = gr.Interface(
+    fn=run_all,
+    inputs=[
+        gr.Textbox(label="Enter 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")
+    ],
+    outputs=[
+        gr.Textbox(label="Generated Quiz", show_copy_button=True),
+        gr.Textbox(label="Personalized Feedback", show_copy_button=True)
+    ],
+    title="EduTutor AI – Personalized Learning & Assessment",
+    description="AI-powered quiz and feedback generator using IBM Granite LLM"
+)
+
+interface.launch(debug=True)