update

models/nutrition_model.py

@@ -6,7 +6,7 @@ import os
 
 SERVER_FILE_DIR = os.path.dirname(os.path.abspath(__file__))
 NUTRITION_MODEL_PATH = os.path.join(SERVER_FILE_DIR, "../resources/models/nutrition_model.pkl")
-MEALS_JSON_PATH = os.path.join(SERVER_FILE_DIR, "../resources/datasets/meals.json")
+MEALS_JSON_PATH = os.path.join(SERVER_FILE_DIR, "../../src/resources/meals.json")
 
 # Ensure the file exists
 if not os.path.exists(MEALS_JSON_PATH):
@@ -14,64 +14,67 @@ if not os.path.exists(MEALS_JSON_PATH):
 
 df = pd.read_json(MEALS_JSON_PATH)
 
-
 class NutritionModel:
     def __init__(self):
         self.load()
 
     def generate_plan(self, calories):
         lunch_attr = {
-            "Calories": calories * 0.5,
-            "FatContent": random.uniform(19, 97),
-            "SaturatedFatContent": random.uniform(6, 12),
-            "CholesterolContent": random.uniform(77, 299),
-            "SodiumContent": random.uniform(565, 2299),
-            "CarbohydrateContent": random.uniform(28, 317),
-            "FiberContent": random.uniform(2, 38),
+            "Calories":calories*0.4, 
+            "FatContent":random.uniform(19, 97), 
+            "SaturatedFatContent":random.uniform(6, 12), 
+            "CholesterolContent": random.uniform(77, 299), 
+            "SodiumContent":random.uniform(565, 2299), 
+            "CarbohydrateContent":random.uniform(28, 317), 
+            "FiberContent": random.uniform(2, 38), 
             "SugarContent": random.uniform(0, 38),
-            "ProteinContent": random.uniform(20, 123)
+            "ProteinContent":random.uniform(20, 123),
+            'weight' : 30
         }
         
         lunch_df = pd.DataFrame(lunch_attr, index=[0])
         
         breakfast_attr = {
-            "Calories": calories * 0.30,
-            "FatContent": random.uniform(8.7, 20),
-            "SaturatedFatContent": random.uniform(1.7, 3.7),
-            "CholesterolContent": random.uniform(0, 63),
-            "SodiumContent": random.uniform(163, 650),
-            "CarbohydrateContent": random.uniform(23, 56),
-            "FiberContent": random.uniform(2.6, 8),
+            "Calories":calories*0.30, 
+            "FatContent":random.uniform(8.7, 20), 
+            "SaturatedFatContent":random.uniform(1.7, 3.7), 
+            "CholesterolContent": random.uniform(0, 63), 
+            "SodiumContent":random.uniform(163, 650), 
+            "CarbohydrateContent":random.uniform(23, 56), 
+            "FiberContent": random.uniform(2.6, 8), 
             "SugarContent": random.uniform(3.5, 13),
-            "ProteinContent": random.uniform(6, 25)
+            "ProteinContent":random.uniform(6, 25),
+            'weight' : 0
         }
         
         breakfast_df = pd.DataFrame(breakfast_attr, index=[0])
         
         dinner_attr = {
-            "Calories": calories * 0.30,
-            "FatContent": random.uniform(8.7, 20),
-            "SaturatedFatContent": random.uniform(1.7, 3.7),
-            "CholesterolContent": random.uniform(0, 63),
-            "SodiumContent": random.uniform(163, 650),
-            "CarbohydrateContent": random.uniform(23, 56),
-            "FiberContent": random.uniform(2.6, 8),
-            "SugarContent": random.uniform(3.5, 13),
-            "ProteinContent": random.uniform(6, 25)
+            "Calories":calories*0.30, 
+            "FatContent":random.uniform(15, 33), 
+            "SaturatedFatContent":random.uniform(6, 8), 
+            "CholesterolContent": random.uniform(22, 86), 
+            "SodiumContent":random.uniform(265, 775), 
+            "CarbohydrateContent":random.uniform(14, 44), 
+            "FiberContent": random.uniform(201, 210), 
+            "SugarContent": random.uniform(3, 13),
+            "ProteinContent":random.uniform(11, 25), 
+            'weight' :10
         }
         
         dinner_df = pd.DataFrame(dinner_attr, index=[0])
         
         snack_attr = {
-            "Calories": random.uniform(90, 190),
-            "FatContent": random.uniform(1.7, 10),
-            "SaturatedFatContent": random.uniform(0.7, 3),
-            "CholesterolContent": random.uniform(2, 16),
-            "SodiumContent": random.uniform(47, 200),
-            "CarbohydrateContent": random.uniform(10, 31),
-            "FiberContent": random.uniform(0.4, 2.5),
+            "Calories":random.uniform(90, 190), 
+            "FatContent":random.uniform(1.7, 10), 
+            "SaturatedFatContent":random.uniform(0.7, 3), 
+            "CholesterolContent": random.uniform(2, 16), 
+            "SodiumContent":random.uniform(47, 200), 
+            "CarbohydrateContent":random.uniform(10, 31), 
+            "FiberContent": random.uniform(0.4, 2.5), 
             "SugarContent": random.uniform(5.7, 21),
-            "ProteinContent": random.uniform(3, 20)
+            "ProteinContent":random.uniform(3, 20),
+            'weight' :40
         }
         
         snack_df = pd.DataFrame(snack_attr, index=[0])
@@ -83,12 +86,10 @@ class NutritionModel:
 
         meals = np.concatenate((breakfast, lunch, dinner, snack), axis=0)
         meals = np.transpose(meals)
-
         days = []
         for i in range(7):
             day_meals = df.iloc[meals[i]].to_dict(orient="records")
             days.append(day_meals)
-
         return days
 
     def load(self):

resources/datasets/meals.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0149719d8294fc792dfe053133b3c9edce076b5d69978b26185e96cfec9a5140
-size 4038577
+oid sha256:51acbeea46fac5d8ccbc8d3a4640c06e7be141e740a2079fda91c0ab9aae8cac
+size 3369182

resources/models/nutrition_model.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9cd0dc84cc9dcc0c985725e8d5cda8d9f9b0571c6c3219bdef2309f177b46ae1
-size 258480
+oid sha256:b69a06d48ce8d41617e979f87bdc82bbe5c4b2cff80234f688a642509ee55673
+size 278272