Update app.py

app.py

@@ -3,6 +3,7 @@ with open("app.py", "w") as f:
 import os
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 import gradio as gr
 import pickle
 import pandas as pd
@@ -69,16 +70,16 @@ def predict(*inputs):
         with torch.no_grad():
             tensor_data = torch.FloatTensor(scaled_data)
             output = model(tensor_data)
-            probabilities = torch.softmax(output, dim=1)
+            probabilities = F.softmax(output, dim=1)
             print("Prediction completed")
             
-        return {
-            "Response Probability": float(probabilities[0][0]),
-            "Non-Response Probability": float(probabilities[0][1])
-        }
+            response_prob = float(probabilities[0][0])
+            non_response_prob = float(probabilities[0][1])
+            
+            return f"Response Probability: {response_prob:.2%}\nNon-Response Probability: {non_response_prob:.2%}"
     except Exception as e:
         print(f"Error in prediction: {str(e)}")
-        return {"error": str(e)}
+        return f"Error: {str(e)}"
 
 # Default values
 numeric_defaults = {
@@ -90,35 +91,31 @@ numeric_defaults = {
 }
 
 # Create interface
-demo = gr.Interface(
+title = "PVC Response Predictor"
+description = "Enter patient features to predict response probability"
+
+inputs = [
+    gr.inputs.Dropdown(choices=["Yes", "No"], label="Multifocal_PVC"),
+    gr.inputs.Dropdown(choices=["Yes", "No"], label="Nonsustained_VT"),
+    gr.inputs.Dropdown(choices=["Yes", "No"], label="gender"),
+    gr.inputs.Dropdown(choices=["Yes", "No"], label="HTN"),
+    gr.inputs.Dropdown(choices=["Yes", "No"], label="DM"),
+    gr.inputs.Dropdown(choices=["Yes", "No"], label="Fullcompansasion")
+]
+
+for feature in numeric_features:
+    inputs.append(gr.inputs.Number(default=numeric_defaults[feature], label=feature))
+
+outputs = gr.outputs.Textbox(label="Prediction Result")
+
+interface = gr.Interface(
     fn=predict,
-    inputs=[
-        gr.Dropdown(choices=["Yes", "No"], label="Multifocal_PVC"),
-        gr.Dropdown(choices=["Yes", "No"], label="Nonsustained_VT"),
-        gr.Dropdown(choices=["Yes", "No"], label="gender"),
-        gr.Dropdown(choices=["Yes", "No"], label="HTN"),
-        gr.Dropdown(choices=["Yes", "No"], label="DM"),
-        gr.Dropdown(choices=["Yes", "No"], label="Fullcompansasion"),
-        gr.Number(value=numeric_defaults['pvc_percent'], label="pvc_percent"),
-        gr.Number(value=numeric_defaults['PVCQRS'], label="PVCQRS"),
-        gr.Number(value=numeric_defaults['EF'], label="EF"),
-        gr.Number(value=numeric_defaults['Age'], label="Age"),
-        gr.Number(value=numeric_defaults['PVC_Prematurity_index'], label="PVC_Prematurity_index"),
-        gr.Number(value=numeric_defaults['QRS_ratio'], label="QRS_ratio"),
-        gr.Number(value=numeric_defaults['mean_HR'], label="mean_HR"),
-        gr.Number(value=numeric_defaults['symptom_duration'], label="symptom_duration"),
-        gr.Number(value=numeric_defaults['QTc_sinus'], label="QTc_sinus"),
-        gr.Number(value=numeric_defaults['PVCCI_dispersion'], label="PVCCI_dispersion"),
-        gr.Number(value=numeric_defaults['CI_variability'], label="CI_variability"),
-        gr.Number(value=numeric_defaults['PVC_Peak_QRS_duration'], label="PVC_Peak_QRS_duration"),
-        gr.Number(value=numeric_defaults['PVCCI'], label="PVCCI"),
-        gr.Number(value=numeric_defaults['PVC_Compansatory_interval'], label="PVC_Compansatory_interval")
-    ],
-    outputs=gr.Label(label="Prediction"),
-    title="PVC Response Predictor",
-    description="Enter patient features to predict response probability"
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    description=description
 )
 
 if __name__ == "__main__":
     print("Launching application...")
-    demo.launch()""")
+    interface.launch()""")