Create app.py

app.py

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+import joblib
+import pandas as pd
+import gradio as gr
+
+# --- Given weights ---
+weights = {
+    "GradientBoosting": 0.239,
+    "RandomForest": 0.573,
+    "XGBoost": 0.188
+}
+
+# --- Given model paths (place models in the same directory as app.py for HF Spaces) ---
+model_paths = {
+    "GradientBoosting": "GradientBoosting_model.pkl",
+    "RandomForest": "RandomForest_model.pkl",
+    "XGBoost": "xgb_model.pkl"
+}
+
+# --- Load models ---
+models = {name: joblib.load(path) for name, path in model_paths.items()}
+
+# --- Prediction function ---
+def predict(
+    dep_delay_x_congestion, dep_congestion, block_time_diff, enroute_delay,
+    dep_delay_rolling_mean, dep_congestion_roll3h, route_hour_delay_mean,
+    taxi_in_ratio, taxi_out_ratio, arr_congestion_roll3h, month_cos,
+    pres_delta, wspd_delta, arr_congestion, month_sin, season_wind,
+    season, distance_partofday, part_of_day_ord, segment_peak_hours,
+    wind_speed_cat_ord, humidity_cat_ord, prcp_delta, pressure_cat_ord,
+    distance_bin_ord
+):
+    # Prepare input DataFrame
+    X_input = pd.DataFrame([{
+        'dep_delay_x_congestion': dep_delay_x_congestion,
+        'dep_congestion': dep_congestion,
+        'block_time_diff': block_time_diff,
+        'enroute_delay': enroute_delay,
+        'dep_delay_rolling_mean': dep_delay_rolling_mean,
+        'dep_congestion_roll3h': dep_congestion_roll3h,
+        'route_hour_delay_mean': route_hour_delay_mean,
+        'taxi_in_ratio': taxi_in_ratio,
+        'taxi_out_ratio': taxi_out_ratio,
+        'arr_congestion_roll3h': arr_congestion_roll3h,
+        'month_cos': month_cos,
+        'pres_delta': pres_delta,
+        'wspd_delta': wspd_delta,
+        'arr_congestion': arr_congestion,
+        'month_sin': month_sin,
+        'season_wind': season_wind,
+        'season': season,
+        'distance_partofday': distance_partofday,
+        'part_of_day_ord': part_of_day_ord,
+        'segment_peak_hours': segment_peak_hours,
+        'wind_speed_cat_ord': wind_speed_cat_ord,
+        'humidity_cat_ord': humidity_cat_ord,
+        'prcp_delta': prcp_delta,
+        'pressure_cat_ord': pressure_cat_ord,
+        'distance_bin_ord': distance_bin_ord
+    }])
+
+    # Get predictions from each model
+    preds = {name: models[name].predict(X_input)[0] for name in models}
+
+    # Weighted ensemble prediction
+    final_pred = sum(preds[name] * weights[name] for name in preds)
+
+    # Categorize delay
+    if 20 <= final_pred < 30:
+        delay_category = "Minimal Delay"
+    elif 30 <= final_pred <= 60:
+        delay_category = "Moderate Delay"
+    elif final_pred > 60:
+        delay_category = "Excessive Delay"
+    else:
+        delay_category = "No Significant Delay"
+
+    return preds, final_pred, delay_category
+
+# --- Gradio interface ---
+inputs = [
+    gr.Number(label="Departure Delay × Congestion Index"),
+    gr.Number(label="Departure Congestion"),
+    gr.Number(label="Block Time Difference (min)"),
+    gr.Number(label="En‑Route Delay (min)"),
+    gr.Number(label="Avg Departure Delay (last 3 flights)"),
+    gr.Number(label="Departure Congestion (3‑hour rolling)"),
+    gr.Number(label="Avg Delay for Route & Hour"),
+    gr.Number(label="Taxi‑In Time ÷ Block Time"),
+    gr.Number(label="Taxi‑Out Time ÷ Block Time"),
+    gr.Number(label="Arrival Congestion (3‑hour rolling)"),
+    gr.Number(label="Month (cosine encoding)"),
+    gr.Number(label="Pressure Change (Dest − Origin)"),
+    gr.Number(label="Wind Speed Change (Dest − Origin)"),
+    gr.Number(label="Arrival Congestion"),
+    gr.Number(label="Month (sine encoding)"),
+    gr.Number(label="Seasonal Wind Category"),
+    gr.Number(label="Season (ordinal)"),
+    gr.Number(label="Distance × Part of Day"),
+    gr.Number(label="Part of Day (ordinal)"),
+    gr.Number(label="Peak Hour Segment Flag"),
+    gr.Number(label="Wind Speed Category (ordinal)"),
+    gr.Number(label="Humidity Category (ordinal)"),
+    gr.Number(label="Precipitation Change (Dest − Origin)"),
+    gr.Number(label="Pressure Category (ordinal)"),
+    gr.Number(label="Distance Bin (ordinal)")
+]
+
+outputs = [
+    gr.JSON(label="Model Predictions"),
+    gr.Number(label="Weighted Ensemble Prediction (minutes)"),
+    gr.Textbox(label="Delay Category")
+]
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=inputs,
+    outputs=outputs,
+    title="Flight Delay Prediction (Weighted Ensemble)",
+    description="Enter flight features to get predictions from GradientBoosting, RandomForest, and XGBoost, plus a weighted ensemble result and delay category."
+)
+
+if __name__ == "__main__":
+    demo.launch()