app.py

def predict_fall(movement_activity, location, day_of_week, hour_of_day, minute_of_day, time_since_last_event):
    try:
        data = {f: 0 for f in feature_names}  # fallback, keep for all known features

        # Set your categorical one-hot
        data[f'Movement Activity_{movement_activity}'] = 1
        data[f'Location_{location}'] = 1
        data[f'day_of_week_{day_of_week}'] = 1

        # Set numeric features
        data['hour_of_day'] = hour_of_day
        data['minute_of_day'] = minute_of_day
        data['time_since_last_event'] = time_since_last_event

        # Build DataFrame with **all keys of data** (guarantees no missing columns)
        input_df = pd.DataFrame([data], columns=list(data.keys()))

        scaler_feature_names = scaler.feature_names_in_

        scaled_array = scaler.transform(input_df[scaler_feature_names])
        input_df.loc[:, scaler_feature_names] = scaled_array

        pred_proba = model.predict_proba(input_df)[0, 1]
        threshold = 0.4
        pred_label = "Fall Detected" if pred_proba >= threshold else "No Fall"

        return f"Prediction: {pred_label}\nFall Probability: {pred_proba:.2f}"

    except Exception as e:
        import traceback
        tb = traceback.format_exc()
        print("Error in prediction:\n", tb)
        return f"Error: {str(e)}\nCheck server logs for details."