Upload 3 files

app.py

@@ -0,0 +1,60 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import pickle
+
+# Load the model
+with open("model.pkl", "rb") as file:
+    model = pickle.load(file)
+
+# OPTIONS: These must match exactly what you used in training.
+# You can add more options to these lists if needed.
+PLATFORM_OPTIONS = ['Wii', 'NES', 'GB', 'DS', 'X360', 'PS3', 'PS2', 'SNES', 'GBA', '3DS', 'PS4', 'N64', 'PS5', 'XB', 'PC', '2600', 'PSP', 'XOne', 'GC', 'WiiU', 'GEN', 'DC', 'PSV', 'SAT', 'SCD', 'WS', 'NG', 'TG16', '3DO', 'GG', 'PCFX']
+GENRE_OPTIONS = ['Sports', 'Platform', 'Racing', 'Role-Playing', 'Puzzle', 'Misc', 'Shooter', 'Simulation', 'Action', 'Fighting', 'Adventure', 'Strategy']
+PUBLISHER_OPTIONS = ['Nintendo', 'Microsoft Game Studios', 'Take-Two Interactive', 'Sony Computer Entertainment', 'Activision', 'Ubisoft', 'Bethesda Softworks', 'Electronic Arts', 'Sega', 'SquareSoft', 'Atari']
+
+def predict_sales(
+    Platform, Year, Genre, Publisher,
+    NA_Sales, EU_Sales, JP_Sales, Other_Sales
+):
+    total_sales = NA_Sales + EU_Sales + JP_Sales + Other_Sales
+
+    # Create the DataFrame with the exact column names the model expects
+    input_data = pd.DataFrame([{
+        "Platform": Platform,
+        "Year": Year,
+        "Genre": Genre,
+        "Publisher": Publisher,
+        "NA_Sales": NA_Sales,
+        "EU_Sales": EU_Sales,
+        "JP_Sales": JP_Sales,
+        "Other_Sales": Other_Sales,
+        "Total_Regional_Sales": total_sales,
+        "NA_ratio": NA_Sales / (total_sales + 1e-6),
+        "EU_ratio": EU_Sales / (total_sales + 1e-6),
+        "JP_ratio": JP_Sales / (total_sales + 1e-6),
+        "Other_ratio": Other_Sales / (total_sales + 1e-6)
+    }])
+
+    # Predict
+    prediction = model.predict(input_data)[0]
+    return float(np.clip(prediction, 0, 4))
+
+app = gr.Interface(
+    fn=predict_sales,
+    inputs=[
+        gr.Dropdown(choices=PLATFORM_OPTIONS, label="Platform"), # Safer than Textbox
+        gr.Number(label="Year", value=2010),
+        gr.Dropdown(choices=GENRE_OPTIONS, label="Genre"),       # Safer than Textbox
+        gr.Dropdown(choices=PUBLISHER_OPTIONS, label="Publisher"), # Safer than Textbox
+        gr.Number(label="NA_Sales"),
+        gr.Number(label="EU_Sales"),
+        gr.Number(label="JP_Sales"),
+        gr.Number(label="Other_Sales"),
+    ],
+    outputs=gr.Number(label="Predicted Global Sales (millions)"),
+    title="Video Game Global Sales Prediction",
+    description="Enter game details to predict Global Sales (millions)."
+)
+
+app.launch()

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e8f7e5400e5eda44b42cc03c9cd9a0235db18d5e07d45d34f7c93e395db7c65d
+size 10168224

requirements.txt

@@ -0,0 +1,4 @@
+scikit-learn
+pandas
+numpy
+gradio