import gradio as gr
import pandas as pd
import joblib
from collections import OrderedDict

# Load models and encoders
food_model = joblib.load("goal_classifier.pkl")
exercise_model = joblib.load("exercise_classifier.pkl")
encoders = joblib.load("encoder.pkl")
df = pd.read_csv("monthly_fitness_dataset_user200.csv")

le_gender = encoders['le_gender']
le_workout = encoders['le_workout']
le_goal = encoders['le_goal']
le_exercise = encoders['le_exercise']
preprocessor = encoders['preprocessor']


def calculate_bmi(weight_kg, height_cm):
    return weight_kg / ((height_cm / 100) ** 2)

def get_meal_plan(week, day):
    filtered = df[(df['Week'] == week) & (df['Day'] == day)]
    if not filtered.empty:
        row = filtered.iloc[0]
        return OrderedDict([
            ("Breakfast", {"Meal": row["Breakfast"], "Calories": int(row["Calories_Breakfast"])}),
            ("Snack_1", {"Meal": row["Snack_1"], "Calories": int(row["Calories_Snack_1"])}),
            ("Lunch", {"Meal": row["Lunch"], "Calories": int(row["Calories_Lunch"])}),
            ("Snack_2", {"Meal": row["Snack_2"], "Calories": int(row["Calories_Snack_2"])}),
            ("Dinner", {"Meal": row["Dinner"], "Calories": int(row["Calories_Dinner"])}),
        ])
    return OrderedDict()

def get_exercise(week, day):
    filtered = df[(df['Week'] == week) & (df['Day'] == day)]
    exercises = []
    seen = set()

    if not filtered.empty:
        for _, row in filtered.iterrows():
            try:
                exercise_name = le_exercise.inverse_transform([row['Exercise_Name']])[0]
            except:
                exercise_name = row['Exercise_Name']

            exercise_key = (
                exercise_name,
                row["Exercise_Description"],
                row["Exercise_Duration"]
            )

            if exercise_key not in seen:
                seen.add(exercise_key)
                exercises.append({
                    "Exercise_Name": exercise_name,
                    "Exercise_Description": row["Exercise_Description"],
                    "Exercise_Duration": row["Exercise_Duration"]
                })

    return exercises

def recommend(choice, gender, age, height_cm, weight_kg, workout_history, goal, week, day):
    try:
        user_input = {
            'Gender': le_gender.transform([gender])[0],
            'Age': age,
            'Height_cm': height_cm,
            'Weight_kg': weight_kg,
            'Workout_History': le_workout.transform([workout_history])[0],
            'Goal': le_goal.transform([goal])[0],
            'Week': week,
            'Day': day
        }

        user_input['BMI'] = calculate_bmi(weight_kg, height_cm)

        user_df = pd.DataFrame([user_input])
        user_X = preprocessor.transform(user_df)

        food_model.predict(user_X)
        exercise_model.predict(user_X)

        if choice == "meal":
            meal_plan = get_meal_plan(week, day)
            return {
                "Meal_Plan": meal_plan,
                "Total_Calories": sum(meal["Calories"] for meal in meal_plan.values())
            }
        elif choice == "exercise":
            return {"Exercises": get_exercise(week, day)}
        else:
            return {"error": "Invalid choice. Must be 'meal' or 'exercise'"}
    except Exception as e:
        return {"error": str(e)}

# Gradio interface
demo = gr.Interface(
    fn=recommend,
    inputs=[
        gr.Radio(choices=["meal", "exercise"], label="Choice"),
        gr.Radio(choices=["Male", "Female"], label="Gender"),
        gr.Slider(10, 80, step=1, label="Age"),
        gr.Slider(100, 220, step=1, label="Height (cm)"),
        gr.Slider(30, 200, step=1, label="Weight (kg)"),
        gr.Dropdown(choices=le_workout.classes_.tolist(), label="Workout History"),
        gr.Dropdown(choices=le_goal.classes_.tolist(), label="Goal"),
        gr.Slider(1, 4, step=1, label="Week"),
        gr.Slider(1, 7, step=1, label="Day")
    ],
    outputs=gr.JSON(label="Recommendation"),
    title="Fitness Meal & Exercise Recommendation System",
    description="Select your info and receive a personalized meal plan or exercise for the chosen week & day."
)

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