Update app.py

app.py

@@ -1,28 +1,35 @@
-
-import gradio as gr
-import joblib
-import numpy as np
-
-model = joblib.load("game_model.joblib")
-scaler = joblib.load("scaler.joblib")
-
-def predict(NA, EU, JP, OTHER, YEAR):
-    X = np.array([[NA, EU, JP, OTHER, YEAR]])
-    X_scaled = scaler.transform(X)
-    pred = model.predict(X_scaled)[0]
-    return pred
-
-app = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Number(label="NA_Sales"),
-        gr.Number(label="EU_Sales"),
-        gr.Number(label="JP_Sales"),
-        gr.Number(label="Other_Sales"),
-        gr.Number(label="Year")
-    ],
-    outputs="number",
-    title="Game Sales Predictor"
-)
-
-app.launch()
+import gradio as gr
+import joblib
+import numpy as np
+
+# Load model and scaler
+model = joblib.load("game_model.joblib")
+scaler = joblib.load("scaler.joblib")
+
+def predict_sales(na, eu, jp, other, year):
+    # Convert inputs to numpy array
+    X = np.array([[na, eu, jp, other, year]])
+
+    # Scale using the fitted scaler
+    X_scaled = scaler.transform(X)
+
+    # Predict
+    prediction = model.predict(X_scaled)[0]
+    return float(prediction)
+
+# Build UI
+interface = gr.Interface(
+    fn=predict_sales,
+    inputs=[
+        gr.Number(label="NA_Sales"),
+        gr.Number(label="EU_Sales"),
+        gr.Number(label="JP_Sales"),
+        gr.Number(label="Other_Sales"),
+        gr.Number(label="Year")
+    ],
+    outputs=gr.Number(label="Predicted Global Sales"),
+    title="Game Sales Predictor",
+    description="Enter game details to predict total worldwide sales."
+)
+
+interface.launch()