| | import gradio as gr |
| | import joblib |
| | import numpy as np |
| |
|
| | |
| | model = joblib.load("game_model.joblib") |
| | scaler = joblib.load("scaler.joblib") |
| |
|
| | def predict_sales(na, eu, jp, other, year): |
| | |
| | X = np.array([[na, eu, jp, other, year]]) |
| |
|
| | |
| | X_scaled = scaler.transform(X) |
| |
|
| | |
| | prediction = model.predict(X_scaled)[0] |
| | return float(prediction) |
| |
|
| | |
| | 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() |
| |
|