app.py

import gradio as gr
import pytesseract
from PIL import Image
from transformers import pipeline

# Load the pre-trained model for question generation
generator = pipeline("text2text-generation", model="t5-small")

# Function to process image and generate questions
def generate_questions(image):
    # Step 1: Extract text from the image using pytesseract
    text = pytesseract.image_to_string(image)
    
    # Step 2: Use the T5 model to generate questions from the text
    prompt = f"Generate multiple-choice questions based on the following text:\n{text}"
    questions = generator(prompt, max_length=150, num_return_sequences=1)
    
    # Return the generated questions
    return questions[0]['generated_text']

# Create the Gradio interface
iface = gr.Interface(
    fn=generate_questions, 
    inputs=gr.Image(type="pil", label="Upload Image"), 
    outputs=gr.Textbox(label="Generated Question Paper"),
    title="Image to Question Paper Generator",
    description="Upload images containing text, and this tool will generate a question paper based on the text found in the images."
)

iface.launch()