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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)
""")
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