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app.py

@@ -1,25 +1,26 @@
-# app.py
-
 import gradio as gr
 from transformers import pipeline
 
-# Load question generation pipeline (use any other T5-based if you want)
-question_generator = pipeline("e2e-qg", model="valhalla/t5-base-e2e-qg")
+# ✅ Use the correct pipeline task
+question_generator = pipeline("text2text-generation", model="valhalla/t5-base-e2e-qg")
 
 def generate_questions(text):
-    try:
-        results = question_generator(text)
-        questions = [q['question'] for q in results]
-        return "\n".join(questions)
-    except Exception as e:
-        return f"Error: {str(e)}"
+    # Split text into chunks (optional)
+    chunks = text.split(". ")
+    questions = []
+    for chunk in chunks:
+        if len(chunk.strip()) > 0:
+            result = question_generator(chunk, max_length=64)[0]['generated_text']
+            questions.append(f"❓ {result}")
+    return "\n".join(questions)
 
-iface = gr.Interface(
+# Gradio interface
+interface = gr.Interface(
     fn=generate_questions,
-    inputs=gr.Textbox(lines=10, label="Paste your text here"),
-    outputs=gr.Textbox(lines=10, label="Generated Questions"),
-    title="T5-based Question Generator",
-    description="Paste a long paragraph, and this tool will generate multiple questions using a T5 model."
+    inputs=gr.Textbox(lines=15, placeholder="Paste your long text here..."),
+    outputs="text",
+    title="🔍 Generate Questions from Text",
+    description="Uses a T5 model to generate questions from a multi-sentence paragraph."
 )
 
-iface.launch()
+interface.launch()