Update app.py

app.py

@@ -3,38 +3,40 @@ import pickle
 import numpy as np
 from sklearn.preprocessing import StandardScaler
 
-# Load the model
+# 1) Load the model from the file you uploaded
 def load_model():
     with open("rf_model.pkl", "rb") as f:
-        model = pickle.load(f)
-    return model
+        return pickle.load(f)
 
-# Make predictions with the model
-def predict(model, feature1, feature2, feature3, feature4, feature5):
-    new_data = np.array([[feature1, feature2, feature3, feature4, feature5]])
-    scaler = StandardScaler()
-    new_data_scaled = scaler.fit_transform(new_data)  # Standardize the data
-    return model.predict(new_data_scaled)[0]
+# 2) Prediction logic
+def predict(model, f1, f2, f3, f4, f5):
+    X = np.array([[f1, f2, f3, f4, f5]])
+    X_scaled = StandardScaler().fit_transform(X)
+    return model.predict(X_scaled)[0]
 
-# Gradio Interface
+# 3) Build & launch the Gradio interface
 def main():
     model = load_model()
-    
     inputs = [
-        gr.Slider(minimum=0, maximum=10, value=5, label="Feature 1"),
-        gr.Slider(minimum=0, maximum=10, value=3, label="Feature 2"),
-        gr.Slider(minimum=0, maximum=10, value=7, label="Feature 3"),
-        gr.Slider(minimum=0, maximum=10, value=6, label="Feature 4"),
-        gr.Slider(minimum=0, maximum=10, value=4, label="Feature 5")
+        gr.Slider(0, 10, value=5, label="Feature 1"),
+        gr.Slider(0, 10, value=3, label="Feature 2"),
+        gr.Slider(0, 10, value=7, label="Feature 3"),
+        gr.Slider(0, 10, value=6, label="Feature 4"),
+        gr.Slider(0, 10, value=4, label="Feature 5")
     ]
-    
-    output = gr.Textbox()
+    output = gr.Textbox(label="Prediction")
 
-    # Launch the Gradio interface without the gpu argument
-    gr.Interface(
-        fn=lambda feature1, feature2, feature3, feature4, feature5: predict(model, feature1, feature2, feature3, feature4, feature5),
-        inputs=inputs, outputs=output, live=True
-    ).launch()
+    demo = gr.Interface(
+        fn=lambda f1, f2, f3, f4, f5: predict(model, f1, f2, f3, f4, f5),
+        inputs=inputs,
+        outputs=output,
+        live=True,
+        title="TaskMaster Job Scheduler",
+        description="Random Forest inference on 5 features"
+    )
 
-if __name__ == '__main__':
+    # Disable SSR so it stays up on Spaces
+    demo.launch(ssr=False)
+
+if __name__ == "__main__":
     main()