Delete app.py

app.py

@@ -1,30 +0,0 @@
-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()