Create app.py

app.py

@@ -0,0 +1,49 @@
+import gradio as gr
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import r2_score
+from xgboost import XGBRegressor
+
+# Load dataset
+data = pd.read_csv('uber.csv')
+data = data.drop('cars_available', axis=1)
+
+# Features and target
+X = data.drop('price_usd', axis=1)
+Y = data['price_usd']
+
+# Train-test split
+Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.2)
+
+# Train model
+xgb = XGBRegressor()
+xgb.fit(Xtrain, Ytrain)
+
+# Evaluate model (just printing in logs, not in interface)
+output = xgb.predict(Xtest)
+score = r2_score(Ytest, output)
+print("R² Score:", score)
+
+# Gradio prediction function
+def predict_price(rain, distance, time, traffic):
+    user_data = [[rain, distance, time, traffic]]
+    predicted_price = xgb.predict(user_data)[0]
+    return f"Predicted Uber Price: ${predicted_price:,.2f}"
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_price,
+    inputs=[
+        gr.Slider(1, 10, step=1, label="Rain (1-10)", value=5),
+        gr.Number(label="Distance (km)", value=10),
+        gr.Slider(1, 24, step=1, label="Time (Hour of Day)", value=12),
+        gr.Slider(1, 10, step=1, label="Traffic (1-10)", value=5),
+    ],
+    outputs=gr.Textbox(label="Prediction"),
+    title="Uber Price Prediction",
+    description="Enter ride details to predict Uber price using XGBoost."
+)
+
+# Launch app
+if __name__ == "__main__":
+    interface.launch(share=True)