Upload app.py

app.py

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+from flask import Flask, request, jsonify, make_response
+import pandas as pd
+import joblib
+from flask_cors import CORS
+from collections import OrderedDict
+import json
+
+app = Flask(__name__)
+CORS(app)  # Enables cross-origin requests (e.g., from Flutter)
+
+# 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']
+
+# --- Utilities ---
+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]
+        # Decode label if encoded
+        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 {}
+
+# --- Main API Endpoint ---
+@app.route("/recommend", methods=["POST"])
+def recommend():
+    data = request.get_json()
+
+    try:
+        # Encode inputs
+        user_input = {
+            'Gender': le_gender.transform([data['Gender']])[0],
+            'Age': data['Age'],
+            'Height_cm': data['Height_cm'],
+            'Weight_kg': data['Weight_kg'],
+            'Workout_History': le_workout.transform([data['Workout_History']])[0],
+            'Goal': le_goal.transform([data['Goal']])[0],
+            'Week': data['Week'],
+            'Day': data['Day']
+        }
+
+        # Add BMI for potential future use
+        user_input['BMI'] = calculate_bmi(user_input['Weight_kg'], user_input['Height_cm'])
+
+        # Prepare input for prediction (not used in current response)
+        user_df = pd.DataFrame([user_input])
+        user_X = preprocessor.transform(user_df)
+
+        # Perform predictions (optional if not needed in response)
+        food_model.predict(user_X)
+        exercise_model.predict(user_X)
+
+        # Return either meal or exercise plan
+        if data['choice'] == 'meal':
+            meal_plan = get_meal_plan(user_input['Week'], user_input['Day'])
+            response_data = {
+                "Meal_Plan": meal_plan,
+                "Total_Calories": sum(meal["Calories"] for meal in meal_plan.values())
+            }
+
+        elif data['choice'] == 'exercise':
+            exercise = get_exercise(user_input['Week'], user_input['Day'])
+            response_data = {"Exercise": exercise}
+
+        else:
+            return jsonify({"error": "Invalid choice. Must be 'meal' or 'exercise'"}), 400
+
+        return make_response(json.dumps(response_data, ensure_ascii=False), 200, {'Content-Type': 'application/json'})
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+# --- Run the Flask app ---
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000, debug=True)