Create app.py

app.py

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+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()