Update app.py

app.py

@@ -1,131 +1,20 @@
-# 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
 from collections import OrderedDict
 
-# Load models
+# Load models and encoders
 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
+encoders = joblib.load("encoder.pkl")
 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()}
+# 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)
@@ -147,17 +36,16 @@ 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"]
-        }
+        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,
@@ -175,10 +63,11 @@ def recommend(choice, gender, age, height_cm, weight_kg, workout_history, goal,
         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)
+        # 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 {
@@ -208,11 +97,122 @@ demo = gr.Interface(
     ],
     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."
+    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()