feat: Add nutrition prediction endpoint

models-server/models/nutrition_model.py

@@ -0,0 +1,94 @@
+import random 
+import pandas as pd
+import numpy as np 
+import pickle
+import sys
+import os
+import pickle
+
+SERVER_FILE_DIR = os.path.dirname(os.path.abspath(__file__))
+NUTRITION_MODEL_PATH = os.path.join(
+    SERVER_FILE_DIR, *"../resources/models/nutrition_model.pkl".split("/")
+)
+
+
+class NutritionModel:
+    def generate_plan(self,calories):
+        the_model = self.nutrition_model
+        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), 
+        "SugarContent": random.uniform(0, 38),
+        "ProteinContent":random.uniform(20, 123)}
+        
+        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), 
+        "SugarContent": random.uniform(3.5, 13),
+        "ProteinContent":random.uniform(6, 25)}
+        
+        breakfast_df = pd.DataFrame(breakfast_attr, index=[0])
+        
+        dinner_attr = {"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(101, 110), 
+        "SugarContent": random.uniform(3, 13),
+        "ProteinContent":random.uniform(11, 25)}
+        
+        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), 
+        "SugarContent": random.uniform(5.7, 21),
+        "ProteinContent":random.uniform(3, 20)}
+        
+        snack_df = pd.DataFrame(snack_attr, index=[0])
+        
+        drinks_attr = {"Calories":random.uniform(60, 125), 
+        "FatContent":random.uniform(0.2, 0.6), 
+        "SaturatedFatContent":random.uniform(0, 0.1), 
+        "CholesterolContent": random.uniform(0, 0.1), 
+        "SodiumContent":random.uniform(3.5, 51), 
+        "CarbohydrateContent":random.uniform(14, 30), 
+        "FiberContent": random.uniform(0.2, 3.6), 
+        "SugarContent": random.uniform(109, 122),
+        "ProteinContent":random.uniform(0.4, 6)}
+        
+        drink_df = pd.DataFrame(drinks_attr, index=[0])
+        
+        lunch = the_model.transform(lunch_df)
+        breakfast = the_model.transform(breakfast_df)
+        dinner = the_model.transform(dinner_df)
+        snack = the_model.transform(snack_df)
+        drink = the_model.transform(drink_df)
+
+        meals = np.concatenate((breakfast, lunch, dinner, snack, drink), axis=0)
+        meals = np.transpose(meals)
+        
+        return meals
+    
+
+    def load(self):
+
+        with open(NUTRITION_MODEL_PATH, "rb") as f:
+            self.nutrition_model = pickle.load(f)
+

models-server/resources/models/nutrition_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9cd0dc84cc9dcc0c985725e8d5cda8d9f9b0571c6c3219bdef2309f177b46ae1
+size 258480

models-server/server.py

@@ -2,6 +2,7 @@ from flask import Flask, request, jsonify
 from dotenv import load_dotenv
 import os
 from models.fitness_model import FitnessModel
+from models.nutrition_model import NutritionModel
 
 load_dotenv()
 
@@ -11,6 +12,8 @@ PORT = os.getenv("MODELS_PORT") or "3030"
 
 
 fitness_model = FitnessModel.load()
+nutrition_model = NutritionModel()
+nutrition_model.load()
 app = Flask("model-server")
 
 
@@ -36,11 +39,25 @@ def fitness_predict():
     for paramName in paramNames:
         value = request.json.get(paramName)
         if value is None:
-            return jsonify({"error": f"{paramName} is missing"}), 399
+            return jsonify({"error": f"{paramName} is missing"}), 400
         params[paramName] = value
 
     return jsonify({"result": fitness_model.predict(**params)})
 
 
+@app.post("/nutrition")
+def nutrition_predict():
+    paramNames = ["calories"]
+
+    params = {}
+    for paramName in paramNames:
+        value = request.json.get(paramName)
+        if value is None:
+            return jsonify({"error": f"{paramName} is missing"}), 400
+        params[paramName] = value
+    print("nutrition_model", nutrition_model.generate_plan(**params), type(nutrition_model.generate_plan(**params)))
+    return jsonify({"result": list(nutrition_model.generate_plan(**params))})
+
+
 if __name__ == "__main__":
-    app.run(host=HOST, port=PORT)
+    app.run(host=HOST, port=PORT, debug=True)