Update app.py

app.py

@@ -1,16 +1,20 @@
 with open("app.py", "w") as f:
     f.write("""
 import os
+import sys
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import gradio as gr
 import pickle
 import pandas as pd
+import warnings
+warnings.filterwarnings('ignore')
 
-print("Starting application...")
+print("Python version:", sys.version)
+print("Torch version:", torch.__version__)
 print("Current directory:", os.getcwd())
-print("Files in directory:", os.listdir())
+print("Directory contents:", os.listdir())
 
 class TabTransformer(nn.Module):
     def __init__(self, input_dim, num_classes=2, d_model=64, nhead=4, num_layers=3, dropout=0.1):
@@ -34,15 +38,42 @@ class TabTransformer(nn.Module):
         x = x.squeeze(0)
         return self.fc(x)
 
+# Özellik listeleri
+categorical_features = ['Multifocal_PVC', 'Nonsustained_VT', 'gender', 'HTN', 'DM', 'Fullcompansasion']
+numeric_features = ['pvc_percent', 'PVCQRS', 'EF', 'Age', 'PVC_Prematurity_index', 'QRS_ratio',
+                   'mean_HR', 'symptom_duration', 'QTc_sinus', 'PVCCI_dispersion',
+                   'CI_variability', 'PVC_Peak_QRS_duration', 'PVCCI', 'PVC_Compansatory_interval']
+
+# Varsayılan değerler
+numeric_defaults = {
+    'pvc_percent': 11.96, 'PVCQRS': 155.1, 'EF': 59.93, 'Age': 52.19,
+    'PVC_Prematurity_index': 0.6158, 'QRS_ratio': 1.933, 'mean_HR': 71.28,
+    'symptom_duration': 14.91, 'QTc_sinus': 425.0, 'PVCCI_dispersion': 57.1,
+    'CI_variability': 22.98, 'PVC_Peak_QRS_duration': 76.13, 'PVCCI': 513.4,
+    'PVC_Compansatory_interval': 1044
+}
+
+try:
+    print("Loading model...")
+    # Model tanımı
+    input_dim = len(categorical_features) + len(numeric_features)
+    model = TabTransformer(input_dim=input_dim)
+    model.load_state_dict(torch.load("tabtransformer_model.pth", map_location=torch.device('cpu')))
+    model.eval()
+    print("Model loaded successfully")
+
+    print("Loading scaler...")
+    # Scaler yükleme
+    with open("trans_scaler.pkl", "rb") as f:
+        scaler = pickle.load(f)
+    print("Scaler loaded successfully")
+
+except Exception as e:
+    print(f"Error during initialization: {str(e)}")
+    sys.exit(1)
+
 def predict(*inputs):
     try:
-        print("Prediction started...")
-        # Feature lists
-        categorical_features = ['Multifocal_PVC', 'Nonsustained_VT', 'gender', 'HTN', 'DM', 'Fullcompansasion']
-        numeric_features = ['pvc_percent', 'PVCQRS', 'EF', 'Age', 'PVC_Prematurity_index', 'QRS_ratio',
-                          'mean_HR', 'symptom_duration', 'QTc_sinus', 'PVCCI_dispersion',
-                          'CI_variability', 'PVC_Peak_QRS_duration', 'PVCCI', 'PVC_Compansatory_interval']
-
         # Split inputs
         cat_inputs = inputs[:len(categorical_features)]
         num_inputs = inputs[len(categorical_features):]
@@ -53,69 +84,63 @@ def predict(*inputs):
         
         # Create DataFrame
         data = pd.DataFrame([cat_data + num_data], columns=categorical_features + numeric_features)
-        print("Data prepared:", data.shape)
-
-        # Load scaler and transform data
-        with open("trans_scaler.pkl", 'rb') as f:
-            scaler = pickle.load(f)
+        
+        # Scale data
         scaled_data = scaler.transform(data)
-        print("Data scaled")
-
-        # Load model and predict
-        input_dim = len(categorical_features) + len(numeric_features)
-        model = TabTransformer(input_dim=input_dim)
-        model.load_state_dict(torch.load("tabtransformer_model.pth", map_location='cpu'))
-        model.eval()
         
+        # Predict
         with torch.no_grad():
             tensor_data = torch.FloatTensor(scaled_data)
-            output = model(tensor_data)
-            probabilities = F.softmax(output, dim=1)
-            print("Prediction completed")
-            
-            response_prob = float(probabilities[0][0])
-            non_response_prob = float(probabilities[0][1])
+            outputs = model(tensor_data)
+            probs = F.softmax(outputs, dim=1)
             
-            return f"Response Probability: {response_prob:.2%}\nNon-Response Probability: {non_response_prob:.2%}"
+        response_prob = float(probs[0][0])
+        non_response_prob = float(probs[0][1])
+        
+        return f"Response: {response_prob:.1%}\nNon-Response: {non_response_prob:.1%}"
+        
     except Exception as e:
         print(f"Error in prediction: {str(e)}")
         return f"Error: {str(e)}"
 
-# Default values
-numeric_defaults = {
-    'pvc_percent': 11.96, 'PVCQRS': 155.1, 'EF': 59.93, 'Age': 52.19,
-    'PVC_Prematurity_index': 0.6158, 'QRS_ratio': 1.933, 'mean_HR': 71.28,
-    'symptom_duration': 14.91, 'QTc_sinus': 425.0, 'PVCCI_dispersion': 57.1,
-    'CI_variability': 22.98, 'PVC_Peak_QRS_duration': 76.13, 'PVCCI': 513.4,
-    'PVC_Compansatory_interval': 1044
-}
-
 # Create 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=inputs,
-    outputs=outputs,
-    title=title,
-    description=description
-)
+with gr.Blocks() as demo:
+    gr.Markdown("# PVC Response Predictor")
+    gr.Markdown("Enter patient features to predict response probability")
+    
+    with gr.Row():
+        with gr.Column():
+            inputs = []
+            # Categorical inputs
+            for feat in categorical_features:
+                inputs.append(gr.Dropdown(
+                    choices=["Yes", "No"],
+                    value="No",
+                    label=feat
+                ))
+            
+            # Numeric inputs
+            for feat in numeric_features:
+                inputs.append(gr.Number(
+                    value=numeric_defaults[feat],
+                    label=feat
+                ))
+        
+        with gr.Column():
+            output = gr.Textbox(label="Prediction Results")
+    
+    submit_btn = gr.Button("Predict")
+    submit_btn.click(
+        fn=predict,
+        inputs=inputs,
+        outputs=output
+    )
 
 if __name__ == "__main__":
-    print("Launching application...")
-    interface.launch()""")
+    print("Starting server...")
+    demo.launch(
+        server_name="0.0.0.0",
+        show_error=True,
+        share=False,
+        debug=True
+    )""")