Update app.py

app.py

@@ -1,3 +1,110 @@
+# 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("encoders.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
@@ -6,15 +113,21 @@ 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("encoders.pkl")
+# encoders = joblib.load("encoders.pkl")
+le_gender = joblib.load['gender']
+le_workout = joblib.load['workout']
+le_goal = joblib.load['goal']
+le_exercise = joblib.load['exercise']
+preprocessor = joblib.load['preprocessor']
+
 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']
+# # 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)
@@ -101,5 +214,8 @@ demo = gr.Interface(
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()    
+    
+    
+