Update app.py

app.py

@@ -7,15 +7,15 @@ from collections import OrderedDict
 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")
+df = pd.read_csv("/content/monthly_fitness_dataset_user200.csv")
 
-# Load individual encoders
-le_gender = encoders['gender']
-le_workout = encoders['workout']
-le_goal = encoders['goal']
-le_exercise = encoders['exercise']
+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)
 
@@ -34,18 +34,34 @@ def get_meal_plan(week, day):
 
 def get_exercise(week, day):
     filtered = df[(df['Week'] == week) & (df['Day'] == day)]
+    exercises = []
+    seen = set()
+
     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 {}
+        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:
-        # Encode inputs
         user_input = {
             'Gender': le_gender.transform([gender])[0],
             'Age': age,
@@ -59,15 +75,12 @@ def recommend(choice, gender, age, height_cm, weight_kg, workout_history, goal,
 
         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 {
@@ -75,7 +88,7 @@ def recommend(choice, gender, age, height_cm, weight_kg, workout_history, goal,
                 "Total_Calories": sum(meal["Calories"] for meal in meal_plan.values())
             }
         elif choice == "exercise":
-            return {"Exercise": get_exercise(week, day)}
+            return {"Exercises": get_exercise(week, day)}
         else:
             return {"error": "Invalid choice. Must be 'meal' or 'exercise'"}
     except Exception as e:
@@ -103,116 +116,6 @@ demo = gr.Interface(
 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()