Create main.py

main.py

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+import gradio as gr
+import PyPDF2
+from transformers import pipeline
+
+# Load a Hugging Face model for text-to-text generation
+mcq_generator = pipeline("text2text-generation", model="t5-small", tokenizer="t5-small")
+
+def extract_text_from_pdf(pdf_file):
+    """Extract text from an uploaded PDF file."""
+    pdf_reader = PyPDF2.PdfReader(pdf_file)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+def generate_mcqs(pdf_file):
+    """Generate MCQs from the extracted text of the PDF."""
+    # Extract text from the PDF
+    pdf_text = extract_text_from_pdf(pdf_file)
+    
+    # Generate MCQs (this is a basic prompt for the model; you can refine it)
+    prompt = f"Generate MCQs from the following text: {pdf_text[:2000]}"  # Limiting text to 2000 characters
+    results = mcq_generator(prompt, max_length=512, num_return_sequences=5)
+
+    # Extract questions from the model's output
+    mcqs = [result['generated_text'] for result in results]
+    return "\n\n".join(mcqs)
+
+# Gradio UI
+with gr.Blocks() as mcq_app:
+    gr.Markdown("# PDF-based MCQ Generator")
+    pdf_input = gr.File(label="Upload PDF", type="file")
+    generate_button = gr.Button("Generate MCQs")
+    mcq_output = gr.Textbox(label="Generated MCQs", lines=15)
+    
+    generate_button.click(fn=generate_mcqs, inputs=pdf_input, outputs=mcq_output)
+
+# Launch the app
+mcq_app.launch()