Create app.py

app.py

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+import torch
+import gradio as gr
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Set device
+device = "mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load model and tokenizer
+model_path = "thenHung/question_decomposer_t5"
+tokenizer = T5Tokenizer.from_pretrained(model_path)
+model = T5ForConditionalGeneration.from_pretrained(model_path)
+model.to(device)
+model.eval()
+
+def decompose_question(question):
+    """
+    Decompose a complex question into sub-questions
+
+    Args:
+        question (str): Input complex question
+
+    Returns:
+        list: List of decomposed sub-questions
+    """
+    try:
+        # Prepare input
+        input_text = f"decompose question: {question}"
+        input_ids = tokenizer(
+            input_text,
+            max_length=128,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt"
+        ).input_ids.to(device)
+
+        # Generate sub-questions
+        with torch.no_grad():
+            outputs = model.generate(
+                input_ids,
+                max_length=128,
+                num_beams=4,
+                early_stopping=True
+            )
+
+        # Decode and split output
+        decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        sub_questions = decoded_output.split(" [SEP] ")
+
+        return sub_questions
+    except Exception as e:
+        return [f"Error: {str(e)}"]
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=decompose_question,
+    inputs=gr.Textbox(label="Enter your complex question"),
+    outputs=gr.JSON(label="Decomposed Sub-Questions"),
+    title="Question Decomposer",
+    description="Breaks down complex questions into simpler sub-questions using a T5 model",
+    examples=[
+        "Who is taller between John and Mary?",
+        "What is the capital of Vietnam and the largest city in Vietnam?",
+    ]
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()