Update app.py

app.py

@@ -1,12 +1,3 @@
-# -*- coding: utf-8 -*-
-"""Untitled14.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1iYWQPQr4OVakQQHRAlYVFLDwdJ-933Tv
-"""
-
 with open("app.py", "w") as f:
     f.write("""
 import torch
@@ -45,9 +36,27 @@ numeric_features = ['pvc_percent', 'PVCQRS', 'EF', 'Age', 'PVC_Prematurity_index
                     'mean_HR', 'symptom_duration', 'QTc_sinus', 'PVCCI_dispersion',
                     'CI_variability', 'PVC_Peak_QRS_duration', 'PVCCI', 'PVC_Compansatory_interval']
 
+# Mean değerleri ile varsayılanlar
+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
+}
+
 # Model ve scaler'ı yükleme
-model_path = "/content/tabtransformer_model.pth"
-scaler_path = "/content/trans_scaler.pkl"
+model_path = "tabtransformer_model.pth"
+scaler_path = "trans_scaler.pkl"
 
 # Model tanımı
 input_dim = len(categorical_features) + len(numeric_features)  # Toplam giriş boyutu
@@ -81,17 +90,19 @@ def predict(*inputs):
         logits = model(tensor_data)
         probabilities = F.softmax(logits, dim=1).numpy()
 
-    return {"Response Probability": probabilities[0][0], "Non-response Probability": probabilities[0][1]}
+    return {
+        "Probability of Response": probabilities[0][0],
+        "Probability of Non-Response": probabilities[0][1]
+    }
 
 # Gradio arayüzü
 inputs = (
     [gr.Dropdown(choices=['Yes', 'No'], label=feature) for feature in categorical_features] +
-    [gr.Number(label=feature) for feature in numeric_features]
+    [gr.Number(label=feature, value=numeric_means[feature]) for feature in numeric_features]
 )
 outputs = gr.Label(label="Prediction")
 interface = gr.Interface(fn=predict, inputs=inputs, outputs=outputs, title="TabTransformer Prediction")
 
-# Public URL ile başlat
-interface.launch(share=True)
-
-""")
+# Spaces için başlatma
+if __name__ == "__main__":
+    interface.launch(server_name="0.0.0.0", server_port=8080)""")