Update app.py

app.py

@@ -4,38 +4,33 @@ import pickle
 import numpy as np
 from sklearn.preprocessing import StandardScaler
 
-# 1) Load your trained model (make sure rf_model.pkl is in the repo root)
+MODEL_PATH = "rf_model.pkl"
+
 def load_model():
-    with open("rf_model.pkl", "rb") as f:
+    with open(MODEL_PATH, "rb") as f:
         return pickle.load(f)
 
-# 2) Prediction function: takes five numeric inputs, scales them, returns class
 def predict(feature1, feature2, feature3, feature4, feature5):
     model = load_model()
-    x = np.array([[feature1, feature2, feature3, feature4, feature5]])
-    # NOTE: We fit_transform here for demo; in prod you'd persist the scaler too.
-    x_scaled = StandardScaler().fit_transform(x)
-    return str(model.predict(x_scaled)[0])
+    raw = np.array([[feature1, feature2, feature3, feature4, feature5]])
+    # Standardize features (must match training preprocessing)
+    scaler = StandardScaler()
+    scaled = scaler.fit_transform(raw)
+    return int(model.predict(scaled)[0])
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🐨 Random Forest Prediction App\nMove the sliders and click **Predict**")
+    with gr.Row():
+        f1 = gr.Slider(0, 10, value=5, label="Feature 1")
+        f2 = gr.Slider(0, 10, value=3, label="Feature 2")
+    with gr.Row():
+        f3 = gr.Slider(0, 10, value=7, label="Feature 3")
+        f4 = gr.Slider(0, 10, value=6, label="Feature 4")
+    f5 = gr.Slider(0, 10, value=4, label="Feature 5")
+    btn = gr.Button("Predict")
+    out = gr.Textbox(label="Prediction")
 
-# 3) Build the Gradio interface
-demo = gr.Interface(
-    fn=predict,
-    inputs=[
-        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"),
-    ],
-    outputs=gr.Textbox(label="Predicted Class"),
-    title="TaskMaster Job Scheduler",
-    description="Enter five feature values to get a RandomForest prediction.",
-)
+    btn.click(fn=predict, inputs=[f1, f2, f3, f4, f5], outputs=out)
 
-# 4) Launch with SSR turned off for Spaces
 if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        ssr_mode=False  # disable server-side rendering on the Space
-    )
+    demo.launch()