main.py

from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from tensorflow.keras.models import load_model
from tensorflow.keras.utils import get_file 
from tensorflow.keras.utils import load_img 
from tensorflow.keras.utils import img_to_array
from tensorflow import expand_dims
from tensorflow.nn import softmax
from numpy import argmax
from numpy import max
from numpy import array
from json import dumps
from uvicorn import run
import os

app = FastAPI()

origins = ["*"]
methods = ["*"]
headers = ["*"]

app.add_middleware(
    CORSMiddleware, 
    allow_origins = origins,
    allow_credentials = True,
    allow_methods = methods,
    allow_headers = headers    
)

model_dir = "food-vision-model.h5"
model = load_model(model_dir)

class_predictions = array([
    'apple pie',
    'baby back ribs',
    'baklava',
    'beef carpaccio',
    'beef tartare',
    'beet salad',
    'beignets',
    'bibimbap',
    'bread pudding',
    'breakfast burrito',
    'bruschetta',
    'caesar salad',
    'cannoli',
    'caprese salad',
    'carrot cake',
    'ceviche',
    'cheesecake',
    'cheese plate',
    'chicken curry',
    'chicken quesadilla',
    'chicken wings',
    'chocolate cake',
    'chocolate mousse',
    'churros',
    'clam chowder',
    'club sandwich',
    'crab cakes',
    'creme brulee',
    'croque madame',
    'cup cakes',
    'deviled eggs',
    'donuts',
    'dumplings',
    'edamame',
    'eggs benedict',
    'escargots',
    'falafel',
    'filet mignon',
    'fish and chips',
    'foie gras',
    'french fries',
    'french onion soup',
    'french toast',
    'fried calamari',
    'fried rice',
    'frozen yogurt',
    'garlic bread',
    'gnocchi',
    'greek salad',
    'grilled cheese sandwich',
    'grilled salmon',
    'guacamole',
    'gyoza',
    'hamburger',
    'hot and sour soup',
    'hot dog',
    'huevos rancheros',
    'hummus',
    'ice cream',
    'lasagna',
    'lobster bisque',
    'lobster roll sandwich',
    'macaroni and cheese',
    'macarons',
    'miso soup',
    'mussels',
    'nachos',
    'omelette',
    'onion rings',
    'oysters',
    'pad thai',
    'paella',
    'pancakes',
    'panna cotta',
    'peking duck',
    'pho',
    'pizza',
    'pork chop',
    'poutine',
    'prime rib',
    'pulled pork sandwich',
    'ramen',
    'ravioli',
    'red velvet cake',
    'risotto',
    'samosa',
    'sashimi',
    'scallops',
    'seaweed salad',
    'shrimp and grits',
    'spaghetti bolognese',
    'spaghetti carbonara',
    'spring rolls',
    'steak',
    'strawberry shortcake',
    'sushi',
    'tacos',
    'takoyaki',
    'tiramisu',
    'tuna tartare',
    'waffles'
])

@app.get("/")
async def root():
    return {"message": "Welcome to the Food Vision API!"}

@app.post("/net/image/prediction/")
async def get_net_image_prediction(image_link: str = ""):
    if image_link == "":
        return {"message": "No image link provided"}
    
    img_path = get_file(
        origin = image_link
    )
    img = load_img(
        img_path, 
        target_size = (224, 224)
    )

    img_array = img_to_array(img)
    img_array = expand_dims(img_array, 0)

    pred = model.predict(img_array)
    score = softmax(pred[0])

    class_prediction = class_predictions[argmax(score)]
    model_score = round(max(score) * 100, 2)

    return {
        "model-prediction": class_prediction,
        "model-prediction-confidence-score": model_score
    }

if __name__ == "__main__":
    port = int(os.environ.get('PORT', 5000))
    run(app, host="0.0.0.0", port=port)