Spaces:
Sleeping
Sleeping
| 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("fitness_meal_plan_with_exercises.csv") | |
| # Load individual encoders | |
| le_gender = encoders['gender'] | |
| le_workout = encoders['workout'] | |
| le_goal = encoders['goal'] | |
| le_exercise = encoders['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)] | |
| if not filtered.empty: | |
| row = filtered.iloc[0] | |
| try: | |
| row['Exercise_Name'] = le_exercise.inverse_transform([row['Exercise_Name']])[0] | |
| except: | |
| pass | |
| return row[['Exercise_Name', 'Exercise_Description', 'Exercise_Duration']].to_dict() | |
| return {} | |
| def recommend(choice, gender, age, height_cm, weight_kg, workout_history, goal, week, day): | |
| try: | |
| # Encode inputs | |
| 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) | |
| # Prepare for prediction | |
| user_df = pd.DataFrame([user_input]) | |
| user_X = preprocessor.transform(user_df) | |
| # Optionally use models (if needed for future logic) | |
| food_model.predict(user_X) | |
| exercise_model.predict(user_X) | |
| # Output | |
| 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 {"Exercise": 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() | |
| # import gradio as gr | |
| # import pandas as pd | |
| # import joblib | |
| # from collections import OrderedDict | |
| # # Load models | |
| # food_model = joblib.load("goal_classifier.pkl") | |
| # exercise_model = joblib.load("exercise_classifier.pkl") | |
| # # Load individual encoders & preprocessor | |
| # le_gender = joblib.load("le_gender.pkl") | |
| # le_workout = joblib.load("le_workout.pkl") | |
| # le_goal = joblib.load("le_goal.pkl") | |
| # le_exercise = joblib.load("exercise.pkl") | |
| # preprocessor = joblib.load("preprocessor.pkl") | |
| # # Load dataset | |
| # df = pd.read_csv("fitness_meal_plan_with_exercises.csv") | |
| # # Reverse map exercise ID to name | |
| # exercise_reverse_mapping = {v: k for k, v in le_exercise.items()} | |
| # 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)] | |
| # if not filtered.empty: | |
| # row = filtered.iloc[0] | |
| # exercise_id = row['Exercise_ID'] | |
| # exercise_name = exercise_reverse_mapping.get(exercise_id, "Unknown Exercise") | |
| # return { | |
| # "Exercise_Name": exercise_name, | |
| # "Exercise_Description": row["Exercise_Description"], | |
| # "Exercise_Duration": row["Exercise_Duration"] | |
| # } | |
| # return {} | |
| # 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) | |
| # # Prepare for prediction | |
| # user_df = pd.DataFrame([user_input]) | |
| # user_X = preprocessor.transform(user_df) | |
| # # Predict (not used yet, can be incorporated) | |
| # _ = 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 {"Exercise": 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 details to receive a personalized meal or exercise plan for the selected week and day." | |
| # ) | |
| # if __name__ == "__main__": | |
| # demo.launch() | |