import joblib
import pandas as pd
import gradio as gr

# Load model, scaler, feature names etc.
model = joblib.load('fall_detection_model.joblib')  # updated here
scaler = joblib.load('scaler.joblib')
feature_names = joblib.load('feature_names.joblib')  # list of all features in correct order

movement_activities = ['Lying', 'No Movement', 'Sitting', 'Walking']
locations = ['Bathroom', 'Bedroom', 'Kitchen', 'Living Room']
days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']

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}

        data[f'Movement Activity_{movement_activity}'] = 1
        data[f'Location_{location}'] = 1
        data[f'day_of_week_{day_of_week}'] = 1

        data['hour_of_day'] = hour_of_day
        data['minute_of_day'] = minute_of_day
        data['time_since_last_event'] = time_since_last_event

        input_df = pd.DataFrame([data], columns=feature_names, dtype=float)

        scaler_cols = scaler.feature_names_in_
        scaled_features = scaler.transform(input_df[scaler_cols])
        input_df.loc[:, scaler_cols] = scaled_features

        input_df = input_df[model.feature_names_in_]

        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
        traceback.print_exc()
        return f"Error: {str(e)}. Check server logs."
    print("User inputs:", movement_activity, location, day_of_week, hour_of_day, minute_of_day, time_since_last_event)
    print("Data dict:", data)
    print("Input dataframe:\n", input_df)


with gr.Blocks() as demo:
    gr.Markdown("## Fall Prediction")

    with gr.Row():
        movement_input = gr.Dropdown(choices=movement_activities, label="Movement Activity")
        location_input = gr.Dropdown(choices=locations, label="Location")
        day_input = gr.Dropdown(choices=days_of_week, label="Day of Week")

    with gr.Row():
        hour_input = gr.Slider(minimum=0, maximum=23, step=1, label="Hour of Day")
        minute_input = gr.Slider(minimum=0, maximum=59, step=1, label="Minute of Day")

    time_since_input = gr.Number(label="Time Since Last Event (minutes)")

    predict_button = gr.Button("Predict")
    output = gr.Textbox(label="Prediction Result")

    predict_button.click(
        predict_fall,
        inputs=[movement_input, location_input, day_input, hour_input, minute_input, time_since_input],
        outputs=output
    )

if __name__ == "__main__":
    demo.launch()