Upload app.py

app.py

@@ -68,12 +68,18 @@ def predict_outcome(*args):
     inputs_scaled = scaler.transform(input_data)
     prediction = model.predict(inputs_scaled)
     outcome_probability = prediction.flatten()[0]
-    return f'Risk of Post Hepatectomy Liver Failure: {outcome_probability:.2%}'
+    prediction_text = f'Probability of Post Hepatectomy Liver Failure: {outcome_probability:.2%}'
+    markdown_text = "More details on the variables used in NSQIP can be found in the [NSQIP PUF User Guide 2021]	(https://www.facs.org/media/wd2hlqzv/nsqip_puf_userguide_2021.pdf)."
+    return prediction_text, markdown_text
 
 inputs = [gr.Radio(label=feature_descriptions.get(feature), choices=['Yes', 'No']) if feature in categorical_features else gr.Number(label=feature_descriptions.get(feature)) for feature in features]
 
 output_text = gr.Textbox(label="Probability of Liver Failure")
-link_info = gr.Markdown("More details on the variables used in NSQIP can be found in the [NSQIP PUF User Guide 2021](https://www.facs.org/media/wd2hlqzv/nsqip_puf_userguide_2021.pdf).")
+link_info = gr.Markdown()
 
-iface = gr.Interface(fn=predict_outcome, inputs=inputs, outputs=[output_text, link_info], title="NSQIP Post Hepatectomy Liver Failure Calculator")
-iface.launch()
+iface = gr.Interface(
+    fn=predict_outcome,
+    inputs=[gr.Radio(label=feature_descriptions.get(feature), choices=['Yes', 'No']) if feature in categorical_features else gr.Number(label=feature_descriptions.get(feature)) for feature in features],
+    outputs=[output_text, link_info],
+    title="NSQIP Post Hepatectomy Liver Failure Calculator"
+)