Update app.py

app.py

@@ -1,104 +1,105 @@
-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)
+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()
+