Upload app.py

app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+import xgboost as xgb
+
+# XGBoost JSON modelini yükle
+model = xgb.Booster()
+model.load_model("xgb_model.json")
+
+# Scaler'ı joblib ile yükle
+scaler = joblib.load("scaler.pkl")
+
+def predict_quality(pressure, temp_x_pressure, fusion_metric):
+    # Girdi verisini DataFrame olarak hazırla
+    input_df = pd.DataFrame([[pressure, temp_x_pressure, fusion_metric]],
+                            columns=["Pressure (kPa)", "Temperature x Pressure", "Material Fusion Metric"])
+    # Ölçeklendirme uygula
+    scaled = scaler.transform(input_df)
+    # Tahmin için DMatrix oluştur
+    dmatrix = xgb.DMatrix(scaled)
+    prediction = model.predict(dmatrix)[0]  # Liste döner, ilk elemanı al
+    return float(prediction)
+
+iface = gr.Interface(
+    fn=predict_quality,
+    inputs=[
+        gr.Number(label="Pressure (kPa)"),
+        gr.Number(label="Temperature x Pressure"),
+        gr.Number(label="Material Fusion Metric")
+    ],
+    outputs=gr.Number(label="Kalite Skoru"),
+    title="Kalite Skoru Tahmin Modeli",
+    description="Pressure, Temperature x Pressure ve Material Fusion Metric değerlerini giriniz, kalite skorunu tahmin eder."
+)
+
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860)
+
+