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app.py

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+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import gradio as gr
+
+# Load model and tokenizer
+model_name = "valhalla/t5-small-qg-hl"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Function to generate question
+def generate_question(context, answer):
+    if not context.strip() or not answer.strip():
+        return "Please enter both context and answer."
+    input_text = f"generate question: {context.replace(answer, '<hl> ' + answer + ' <hl>')}"
+    inputs = tokenizer.encode(input_text, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=64, num_beams=4, early_stopping=True)
+    question = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return question
+
+# Gradio interface
+iface = gr.Interface(
+    fn=generate_question,
+    inputs=[
+        gr.Textbox(lines=5, label="Context or Paragraph"),
+        gr.Textbox(lines=1, label="Answer (highlighted text)")
+    ],
+    outputs="text",
+    title="🧠 AI Question Generator",
+    description="Enter a paragraph and the answer you want highlighted. The app generates a relevant question."
+)
+
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

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+transformers==4.40.1
+torch
+gradio==5.34.1