import random
import pandas as pd
import numpy as np
import pickle
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")
# Ensure the file exists
if not os.path.exists(MEALS_JSON_PATH):
    raise FileNotFoundError(f"File {MEALS_JSON_PATH} does not exist")

df = pd.read_json(MEALS_JSON_PATH)

class NutritionModel:
    def __init__(self):
        self.load()

    def generate_plan(self, calories):
        lunch_attr = {
            "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),
            '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), 
            "SugarContent": random.uniform(3.5, 13),
            "ProteinContent":random.uniform(6, 25),
            'weight' : 0
        }
        
        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(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), 
            "SugarContent": random.uniform(5.7, 21),
            "ProteinContent":random.uniform(3, 20),
            'weight' :40
        }
        
        snack_df = pd.DataFrame(snack_attr, index=[0])
        
        lunch = self.nutrition_model.transform(lunch_df)
        breakfast = self.nutrition_model.transform(breakfast_df)
        dinner = self.nutrition_model.transform(dinner_df)
        snack = self.nutrition_model.transform(snack_df)

        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):
        with open(NUTRITION_MODEL_PATH, "rb") as f:
            self.nutrition_model = pickle.load(f)