Update app.py

app.py

@@ -1,20 +1,16 @@
 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
 
-# Debug için print fonksiyonları
-print("Python version:", sys.version)
-print("Current working directory:", os.getcwd())
-print("Directory contents:", os.listdir())
+print("Starting application...")
+print("Current directory:", os.getcwd())
+print("Files in directory:", os.listdir())
 
-# TabTransformer Model Tanımı
 class TabTransformer(nn.Module):
     def __init__(self, input_dim, num_classes=2, d_model=64, nhead=4, num_layers=3, dropout=0.1):
         super().__init__()
@@ -37,117 +33,92 @@ class TabTransformer(nn.Module):
         x = x.squeeze(0)
         return self.fc(x)
 
-# Özellikler
-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']
-
-numeric_means = {
-    '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
-}
-
-# Global değişkenler
-model = None
-scaler = None
-
-def load_model_and_scaler():
-    global model, scaler
-    try:
-        print("Model ve scaler yükleniyor...")
-        
-        # Model dosyası kontrolü
-        model_path = "tabtransformer_model.pth"
-        if not os.path.exists(model_path):
-            raise FileNotFoundError(f"Model dosyası bulunamadı: {model_path}")
-        
-        # Scaler dosyası kontrolü
-        scaler_path = "trans_scaler.pkl"
-        if not os.path.exists(scaler_path):
-            raise FileNotFoundError(f"Scaler dosyası bulunamadı: {scaler_path}")
-        
-        # Model yükleme
-        input_dim = len(categorical_features) + len(numeric_features)
-        model = TabTransformer(input_dim=input_dim)
-        model.load_state_dict(torch.load(model_path, map_location='cpu'))
-        model.eval()
-        
-        # Scaler yükleme
-        with open(scaler_path, 'rb') as f:
-            scaler = pickle.load(f)
-        
-        print("Model ve scaler başarıyla yüklendi!")
-        return True
-    except Exception as e:
-        print(f"Model yükleme hatası: {str(e)}")
-        return False
-
 def predict(*inputs):
-    if model is None or scaler is None:
-        return {"Error": "Model henüz yüklenmedi"}
-    
     try:
-        # Girdileri ayır
+        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):]
         
-        # Kategorik verileri dönüştür
+        # Convert inputs
         cat_data = [1 if val == "Yes" else 0 for val in cat_inputs]
-        
-        # Sayısal verileri dönüştür
         num_data = [float(val) for val in num_inputs]
         
-        # DataFrame oluştur
+        # Create DataFrame
         data = pd.DataFrame([cat_data + num_data], columns=categorical_features + numeric_features)
-        
-        # Veriyi ölçeklendir
+        print("Data prepared:", data.shape)
+
+        # Load scaler and transform data
+        with open("trans_scaler.pkl", 'rb') as f:
+            scaler = pickle.load(f)
         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()
         
-        # Tahmin
         with torch.no_grad():
             tensor_data = torch.FloatTensor(scaled_data)
-            logits = model(tensor_data)
-            probabilities = F.softmax(logits, dim=1).numpy()
-        
+            output = model(tensor_data)
+            probabilities = torch.softmax(output, dim=1)
+            print("Prediction completed")
+            
         return {
-            "Probability of Response": float(probabilities[0][0]),
-            "Probability of Non-Response": float(probabilities[0][1])
+            "Response Probability": float(probabilities[0][0]),
+            "Non-Response Probability": float(probabilities[0][1])
         }
     except Exception as e:
-        print(f"Tahmin hatası: {str(e)}")
-        return {"Error": str(e)}
+        print(f"Error in prediction: {str(e)}")
+        return {"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
+}
 
-# Gradio arayüzü
-def create_interface():
-    inputs = [gr.Dropdown(choices=['Yes', 'No'], label=feat) for feat in categorical_features]
-    inputs.extend([gr.Number(label=feat, value=numeric_means[feat]) for feat in numeric_features])
-    
-    outputs = gr.Label(label="Prediction")
-    
-    return gr.Interface(
-        fn=predict,
-        inputs=inputs,
-        outputs=outputs,
-        title="TabTransformer Prediction",
-        description="Enter the features to predict the response probability"
-    )
+# Create interface
+demo = 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"
+)
 
 if __name__ == "__main__":
-    print("Uygulama başlatılıyor...")
-    
-    # Model ve scaler'ı yükle
-    if not load_model_and_scaler():
-        print("Model yüklenemedi. Uygulama sonlandırılıyor.")
-        sys.exit(1)
-    
-    # Arayüzü oluştur ve başlat
-    try:
-        demo = create_interface()
-        demo.launch(server_name="0.0.0.0", server_port=7860)
-    except Exception as e:
-        print(f"Arayüz başlatma hatası: {str(e)}")
-        sys.exit(1)""")
+    print("Launching application...")
+    demo.launch()""")