from eden.block import Block
from eden.datatypes import Image
from eden.hosting import host_block

## eden <3 pytorch
from torchvision import models, transforms
import torch

model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
model = model.eval()  ## no dont move it to the gpu just yet :)

my_transforms = transforms.Compose(
    [
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),  # this normalizes the image to the same format as the pretrained model
    ]
)

eden_block = Block()

my_args = {
    "width": 224,  ## width
    "height": 224,  ## height
    "input_image": Image(),  ## images require eden.datatypes.Image()
}

import requests
labels = requests.get(
    "https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt"
).text.split("\n")


@eden_block.run(args=my_args, progress=False)
def do_something(config):
    global model, labels

    pil_image = config["input_image"]
    pil_image = pil_image.resize((config["width"], config["height"]))

    device = config.gpu
    input_tensor = my_transforms(pil_image).to(device).unsqueeze(0)

    model = model.to(device)

    with torch.no_grad():
        pred = model(input_tensor)[0].cpu()
        index = torch.argmax(pred).item()
        value = pred[index].item()
    # the index is the classification label for the pretrained resnet18 model.
    # the human-readable labels associated with this index are pulled and returned as "label"
    # we need to get them from imagenet labels, which we need to get online.

    label = labels[index]
    # serialize the image
    pil_image = Image(pil_image)
    return {"value": value, "index": index, "label": label, 'image': pil_image}

if __name__ == "__main__":
    host_block(
        block=eden_block,
        port=5656,
        logfile="logs.log",
        log_level="debug",
        max_num_workers=1,
        requires_gpu=True,
    )