Kids education

  # app.py import tensorflow as tf import numpy as np import requests import os from flask import Flask, request, render_template from PIL import Image from io import BytesIO app = Flask(__name__) # Load Food Recognition Model from TF Hub model = tf.keras.Sequential([ tf.keras.layers.Input(shape=(224, 224, 3)), hub.KerasLayer("https://tfhub.dev/google/aiy/vision/classifier/food_V1/1") ]) # Edamam API Credentials NUTRITION_API_ID = os.getenv('EDAMAM_APP_ID') NUTRITION_API_KEY = os.getenv('EDAMAM_API_KEY') def preprocess_image(image): img = Image.open(image).convert('RGB') img = img.resize((224, 224)) img_array = np.array(img) / 255.0 return np.expand_dims(img_array, axis=0) def get_nutrition_info(food_name): url = f'https://api.edamam.com/api/nutrition-data' params = { 'app_id': NUTRITION_API_ID, 'app_key': NUTRITION_API_KEY, 'ingr': food_name } response...