Update app.py

app.py

@@ -1,34 +1,29 @@
-# app.py
 import gradio as gr
 import pickle
 import numpy as np
 from sklearn.preprocessing import StandardScaler
 
-# Load model at startup
+# Load the pre-trained model at startup
 with open("rf_model.pkl", "rb") as f:
     MODEL = pickle.load(f)
 
 def predict(f1, f2, f3, f4, f5):
-    x = np.array([[f1, f2, f3, f4, f5]])
-    x_scaled = StandardScaler().fit_transform(x)
-    return str(MODEL.predict(x_scaled)[0])
-
-with gr.Blocks() as demo:
-    gr.Markdown("## 🐨 TaskMaster Job Scheduler")
-    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")
-    btn.click(fn=predict, inputs=[f1, f2, f3, f4, f5], outputs=out)
+    data = np.array([[f1, f2, f3, f4, f5]])
+    scaled = StandardScaler().fit_transform(data)
+    return int(MODEL.predict(scaled)[0])
 
 if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        prevent_thread_lock=False    # keeps the Space container running
+    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="Prediction"),
+        title="🐨 TaskMaster Job Scheduler",
+        description="Enter five features and get a RandomForest prediction.",
     )
+    demo.launch()  # on Spaces, this binds to 0.0.0.0:7860 and blocks the process