Update app.py

app.py

@@ -110,18 +110,22 @@ import pandas as pd
 import joblib
 from collections import OrderedDict
 
-# Load models 
+# 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")
 
-le_gender = joblib.load['gender.pkl']
-le_workout = joblib.load['workout.pkl']
-le_goal = joblib.load['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")
 
-df = pd.read_csv("monthly_fitness_dataset_user200.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)
@@ -143,16 +147,17 @@ 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()
+        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:
-        # Encode inputs
         user_input = {
             'Gender': le_gender.transform([gender])[0],
             'Age': age,
@@ -170,11 +175,10 @@ def recommend(choice, gender, age, height_cm, weight_kg, workout_history, goal,
         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)
+        # Predict (not used yet, can be incorporated)
+        _ = food_model.predict(user_X)
+        _ = exercise_model.predict(user_X)
 
-        # Output
         if choice == "meal":
             meal_plan = get_meal_plan(week, day)
             return {
@@ -204,11 +208,12 @@ demo = gr.Interface(
     ],
     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."
+    description="Select your details to receive a personalized meal or exercise plan for the selected week and day."
 )
 
 if __name__ == "__main__":
-    demo.launch()    
+    demo.launch()
+