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| import gradio as gr | |
| from transformers import BartTokenizer, BartForConditionalGeneration | |
| model_name = "chikki2004/bart-mcq-generator" | |
| tokenizer = BartTokenizer.from_pretrained(model_name) | |
| model = BartForConditionalGeneration.from_pretrained(model_name) | |
| def generate_mcq(input_text, num_questions): | |
| prompt = f"Topic: Unknown. Content: {input_text} Generate {num_questions} MCQs." | |
| inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512) | |
| output = model.generate(**inputs, max_length=512, num_beams=4, early_stopping=True) | |
| return tokenizer.decode(output[0], skip_special_tokens=True) | |
| iface = gr.Interface( | |
| fn=generate_mcq, | |
| inputs=[ | |
| gr.Textbox(label="Topic Content / Paragraph", lines=10, placeholder="Paste educational content here..."), | |
| gr.Slider(minimum=1, maximum=5, step=1, value=2, label="Number of Questions") | |
| ], | |
| outputs="text", | |
| title="Quiz Generator", | |
| description="Paste a topic content (e.g., Photosynthesis) and select how many MCQs you want." | |
| ) | |
| iface.launch() | |