Update app.py

app.py

@@ -1,32 +1,25 @@
 import gradio as gr
 from span_marker import SpanMarkerModel
 
-# Define the function
-def function(text):
-    # Load the pre-trained model from Hugging Face
-    model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-acronyms")
-    
-    # Ensure the input is valid
+# Download the model from the Hugging Face Hub
+model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-acronyms")
+
+# Define the function for prediction
+def predict_acronyms(text):
     if text:
-        # Run inference on the input text
-        out = model.predict([text])  # Wrap the text in a list as the model expects a list of strings
-        return out
-    else:
-        return "Please provide valid text input."
+        output = model.predict(text)
+        return output
+    return {"error": "Please provide valid text"}
 
-# Set up the Gradio interface
-demo = gr.Interface(
-    fn=function,
-    inputs=gr.Textbox(lines=5, placeholder="Enter your text here...", label="Input Text"),
-    outputs="json",
-    title="Abbreviation Detector",
-    examples=[
-        "NASA is an abbreviation for National Aeronautics and Space Administration.",
-        "AI stands for Artificial Intelligence.",
-        "What does HTTP mean?"
-    ]
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict_acronyms,
+    inputs=gr.Textbox(label="Enter some text:", lines=5, placeholder="Type here..."),
+    outputs=gr.JSON(label="Predicted Output"),
+    title="Acronym Detection with Span Marker",
+    description="This application detects acronyms in the given text using the SpanMarker model."
 )
 
-# Launch the app
+# Launch the Gradio app
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()