Update app.py

app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+import numpy as np
+
+# -----------------------------
+# Load models and training columns
+# -----------------------------
+# Classification models
+rf_model = joblib.load("main/random_forest_model.pkl")
+xgb_clf_model = joblib.load("main/xgboost_model.pkl")
+gbr_clf_model = joblib.load("main/gradient_boosting_model.pkl")
+training_columns_clf = joblib.load("main/training_clm.pkl")
+
+# Regression models
+ridge_model = joblib.load("main/ridge_model.pkl")
+xgb_reg_model = joblib.load("main/xgb_model.pkl")
+gbr_reg_model = joblib.load("main/gbr_model.pkl")
+training_columns_reg = joblib.load("main/training_columns.pkl")
+
+# -----------------------------
+# Preprocessing functions
+# -----------------------------
+def preprocess_classification(df):
+    categorical_cols = ['ORIGIN', 'DEST', 'CARRIER', 'TAIL_NUM',
+                        'DEP_TIME_BLK', 'DEST_STATE_ABR', 'ORIGIN_CITY_NAME',
+                        'DEST_CITY_NAME', 'route']
+    df_encoded = pd.get_dummies(df, columns=categorical_cols)
+    df_encoded = df_encoded.reindex(columns=training_columns_clf, fill_value=0)
+    return df_encoded
+
+def preprocess_regression(df):
+    df_encoded = pd.get_dummies(df, columns=['time_of_day', 'wind_dir_bucket'])
+    df_encoded = df_encoded.reindex(columns=training_columns_reg, fill_value=0)
+    return df_encoded
+
+# -----------------------------
+# Delay category helper
+# -----------------------------
+def categorize_delay(minutes):
+    if minutes < 15:
+        return "Delay not considered less then 15mins"
+    elif 15 <= minutes < 20:
+        return "Delay is Minimum"
+    elif 20 <= minutes < 30:
+        return "Flight is moderately delayed"
+    elif 30 <= minutes < 60:
+        return "Flight is highly delayed"
+    else:
+        return "Flight is delayed too much"
+
+# -----------------------------
+# Prediction functions
+# -----------------------------
+def predict_classification(YEAR, MONTH, DAY_OF_MONTH, DAY_OF_WEEK,
+                           ORIGIN, DEST, CARRIER, TAIL_NUM, DEP_TIME_BLK,
+                           DEST_STATE_ABR, ORIGIN_CITY_NAME, DEST_CITY_NAME):
+    # Auto-generate route
+    route = f"{ORIGIN}_{DEST}"
+    data = {
+        'YEAR': int(YEAR),
+        'MONTH': int(MONTH),
+        'DAY_OF_MONTH': int(DAY_OF_MONTH),
+        'DAY_OF_WEEK': int(DAY_OF_WEEK),
+        'ORIGIN': ORIGIN,
+        'DEST': DEST,
+        'CARRIER': CARRIER,
+        'TAIL_NUM': TAIL_NUM,
+        'DEP_TIME_BLK': DEP_TIME_BLK,
+        'DEST_STATE_ABR': DEST_STATE_ABR,
+        'ORIGIN_CITY_NAME': ORIGIN_CITY_NAME,
+        'DEST_CITY_NAME': DEST_CITY_NAME,
+        'route': route
+    }
+    df_input = pd.DataFrame([data])
+    X = preprocess_classification(df_input)
+
+    pred_rf = rf_model.predict(X)[0]
+    pred_xgb = xgb_clf_model.predict(X)[0]
+    pred_gbr = gbr_clf_model.predict(X)[0]
+
+    prob_rf = rf_model.predict_proba(X)[0][1] if hasattr(rf_model, "predict_proba") else None
+    prob_xgb = xgb_clf_model.predict_proba(X)[0][1] if hasattr(xgb_clf_model, "predict_proba") else None
+    prob_gbr = gbr_clf_model.predict_proba(X)[0][1] if hasattr(gbr_clf_model, "predict_proba") else None
+
+    majority_vote = int(np.round(np.mean([pred_rf, pred_xgb, pred_gbr])))
+
+    return {
+        "Random Forest Prediction": int(pred_rf),
+        "Random Forest Prob": round(prob_rf, 3) if prob_rf is not None else None,
+        "XGBoost Prediction": int(pred_xgb),
+        "XGBoost Prob": round(prob_xgb, 3) if prob_xgb is not None else None,
+        "Gradient Boosting Prediction": int(pred_gbr),
+        "Gradient Boosting Prob": round(prob_gbr, 3) if prob_gbr is not None else None,
+        "Majority Vote": majority_vote
+    }
+
+def predict_regression_with_check(DEP_DELAY, DEP_DELAY_NEW, DEP_DEL15, DEP_DELAY_GROUP,
+                                  temp, prcp, wspd, wdir, bad_weather, wind_dir_bucket,
+                                  time_of_day, is_weekend):
+    # If not delayed, skip regression
+    if int(DEP_DEL15) == 0:
+        return {
+            "Status": "No delay predicted",
+            "Delay Category": None
+        }
+
+    data = {
+        'DEP_DELAY': float(DEP_DELAY),
+        'DEP_DELAY_NEW': float(DEP_DELAY_NEW),
+        'DEP_DEL15': int(DEP_DEL15),
+        'DEP_DELAY_GROUP': int(DEP_DELAY_GROUP),
+        'temp': float(temp),
+        'prcp': float(prcp),
+        'wspd': float(wspd),
+        'wdir': float(wdir),
+        'bad_weather': int(bad_weather),
+        'wind_dir_bucket': wind_dir_bucket,
+        'time_of_day': time_of_day,
+        'is_weekend': int(is_weekend)
+    }
+    df_input = pd.DataFrame([data])
+    X = preprocess_regression(df_input)
+
+    pred_ridge = ridge_model.predict(X)[0]
+    pred_xgb = xgb_reg_model.predict(X)[0]
+    pred_gbr = gbr_reg_model.predict(X)[0]
+
+    max_pred = max(pred_ridge, pred_xgb, pred_gbr)
+    category = categorize_delay(max_pred)
+
+    return {
+        "Ridge Prediction": round(pred_ridge, 2),
+        "XGBoost Prediction": round(pred_xgb, 2),
+        "Gradient Boosting Prediction": round(pred_gbr, 2),
+        "Max Prediction": round(max_pred, 2),
+        "Delay Category": category
+    }
+
+# -----------------------------
+# Gradio Interface
+# -----------------------------
+classification_inputs = [
+    gr.Number(label="YEAR"),
+    gr.Number(label="MONTH"),
+    gr.Number(label="DAY_OF_MONTH"),
+    gr.Number(label="DAY_OF_WEEK (1=Mon ... 7=Sun)"),
+    gr.Textbox(label="Origin Airport Code"),
+    gr.Textbox(label="Destination Airport Code"),
+    gr.Textbox(label="Carrier Code"),
+    gr.Textbox(label="Tail Number"),
+    gr.Textbox(label="Departure Time Block (e.g., 0600-0659)"),
+    gr.Textbox(label="Destination State Abbreviation"),
+    gr.Textbox(label="Origin City Name"),
+    gr.Textbox(label="Destination City Name")
+]
+
+regression_inputs = [
+    gr.Number(label="DEP_DELAY"),
+    gr.Number(label="DEP_DELAY_NEW"),
+    gr.Number(label="DEP_DEL15 (0 or 1)"),
+    gr.Number(label="DEP_DELAY_GROUP"),
+    gr.Number(label="Temperature"),
+    gr.Number(label="Precipitation"),
+    gr.Number(label="Wind Speed"),
+    gr.Number(label="Wind Direction"),
+    gr.Number(label="Bad Weather (0 or 1)"),
+    gr.Textbox(label="Wind Dir Bucket (North/South/East/West/etc.)"),
+    gr.Textbox(label="Time of Day (Morning/Afternoon/Evening/Night)"),
+    gr.Number(label="Is Weekend (0 or 1)")
+]
+
+classification_tab = gr.Interface(
+    fn=predict_classification,
+    inputs=classification_inputs,
+    outputs="json",
+    title="Flight Delay Classification",
+    description="Predict delay classification using Random Forest, XGBoost, and Gradient Boosting."
+)
+
+regression_tab = gr.Interface(
+    fn=predict_regression_with_check,
+    inputs=regression_inputs,
+    outputs="json",
+    title="Flight Delay Regression (Conditional)",
+    description="Predict arrival delay in minutes only if DEP_DEL15=1, with categorized output."
+)
+
+demo = gr.TabbedInterface([classification_tab, regression_tab],
+                          ["Classification", "Regression"])
+
+if __name__ == "__main__":
+    demo.launch()