Create app.py

app.py

@@ -0,0 +1,88 @@
+with open("app.py", "w") as f:
+    f.write("""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import gradio as gr
+import pickle
+import pandas as pd
+
+# 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__()
+        self.embedding = nn.Linear(input_dim, d_model)
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=d_model, nhead=nhead, dim_feedforward=d_model * 4, dropout=dropout, activation='gelu'
+        )
+        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+        self.fc = nn.Sequential(
+            nn.Linear(d_model, d_model // 2),
+            nn.ReLU(),
+            nn.Dropout(dropout),
+            nn.Linear(d_model // 2, num_classes)
+        )
+
+    def forward(self, x):
+        x = self.embedding(x)
+        x = x.unsqueeze(0)  # Add sequence length dimension
+        x = self.transformer_encoder(x)
+        x = x.squeeze(0)  # Remove sequence length dimension
+        return self.fc(x)
+
+# Kategorik ve sayısal 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']
+
+# Model ve scaler'ı yükleme
+model_path = "/content/tabtransformer_model.pth"
+scaler_path = "/content/trans_scaler.pkl"
+
+# Model tanımı
+input_dim = len(categorical_features) + len(numeric_features)  # Toplam giriş boyutu
+model = TabTransformer(input_dim=input_dim)
+model.load_state_dict(torch.load(model_path, weights_only=True))  # Model ağırlıklarını yükle
+model.eval()  # Değerlendirme moduna al
+
+# Scaler yükleme
+with open(scaler_path, "rb") as f:
+    scaler = pickle.load(f)
+
+# Prediction fonksiyonu
+def predict(*inputs):
+    # Girdileri kategorik ve sayısal olarak ayır
+    cat_inputs = inputs[:len(categorical_features)]
+    num_inputs = inputs[len(categorical_features):]
+
+    # Kategorik girdiler (binary olarak 0/1 kodlama: "Yes" -> 1, "No" -> 0)
+    cat_data = [1 if val == "Yes" else 0 for val in cat_inputs]
+
+    # Sayısal girdiler
+    num_data = [float(val) for val in num_inputs]
+
+    # Veriyi birleştir ve ölçeklendir
+    data = pd.DataFrame([cat_data + num_data])
+    scaled_data = scaler.transform(data)
+
+    # Modelden tahmin al
+    tensor_data = torch.FloatTensor(scaled_data)
+    with torch.no_grad():
+        logits = model(tensor_data)
+        probabilities = F.softmax(logits, dim=1).numpy()
+
+    return {"Response Probability": probabilities[0][0], "Non-response Probability": 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]
+)
+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)
+
+""")