from PIL import Image
from sanic import Sanic
from sanic.response import json as json_answer
import numpy as np
from tensorflow import keras
from tensorflow.keras.utils import img_to_array
import io
import os
import json
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'


app = Sanic("Ai")
model_name = "../beerd_imagenet_25_04_2023.keras"
test_model_imagenet = keras.models.load_model(model_name)

model_name = "../beerd_25_04_2023.keras"
test_model = keras.models.load_model(model_name)

dict_names = {}
with open("beerds.json", "r") as f:
    dict_names = json.loads(f.read())
app.static("/", "index.html", name="main")
app.static("/static/", "static/", name="static")


@app.post("/beeds")
async def beeds(request):
    body = request.files.get("f").body

    img = Image.open(io.BytesIO(body))
    img = img.convert('RGB')

    img_net = img.resize((180, 180, ), Image.BILINEAR)
    img_array = img_to_array(img_net)
    test_loss_image_net = test_model_imagenet.predict(
        np.expand_dims(img_array, 0))

    img = img.resize((200, 200, ), Image.BILINEAR)
    img_array = img_to_array(img)
    test_loss = test_model.predict(np.expand_dims(img_array, 0))

    result = {}
    for i, val in enumerate(test_loss[0]):
        if val <= 0.09:
            continue
        result[val] = dict_names[str(i)]

    result_net = {}
    for i, val in enumerate(test_loss_image_net[0]):
        if val <= 0.09:
            continue
        result_net[val] = dict_names[str(i)]

    return json_answer({
        "results": dict(sorted(result.items(), reverse=True)),
        "results_net": dict(sorted(result_net.items(), reverse=True)),
    })

if __name__ == "__main__":
    app.run(auto_reload=True, port=4003, host="0.0.0.0")