Spaces:
Running
Running
File size: 2,877 Bytes
2fb15ce f852fbb 2fb15ce |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
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 System",
description="AI-powered quiz and feedback generator using IBM Granite LLM"
)
interface.launch(debug=True) |