import os

import torch
import torch.nn.functional as F
import torchvision.transforms as T
from uniformer import uniformer_small
from imagenet_class_index import imagenet_classnames

import gradio as gr
from huggingface_hub import hf_hub_download

# Device on which to run the model
# Set to cuda to load on GPU
device = "cpu"
# os.system("wget https://cdn-lfs.huggingface.co/Andy1621/uniformer/fd192c31f8bd77670de8f171111bd51f56fd87e6aea45043ab2edc181e1fa775")
model_path = hf_hub_download(repo_id="Andy1621/uniformer", filename="uniformer_small_in1k.pth")
# Pick a pretrained model 
model = uniformer_small()
# state_dict = torch.load('fd192c31f8bd77670de8f171111bd51f56fd87e6aea45043ab2edc181e1fa775', map_location='cpu')
state_dict = torch.load(model_path, map_location='cpu')
model.load_state_dict(state_dict['model'])

# Set to eval mode and move to desired device
model = model.to(device)
model = model.eval()

# Create an id to label name mapping
imagenet_id_to_classname = {}
for k, v in imagenet_classnames.items():
    imagenet_id_to_classname[k] = v[1] 


def inference(img):
    image = img
    image_transform = T.Compose(
    [
        T.Resize(224),
        T.CenterCrop(224),
        T.ToTensor(),
        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ]
    )
    image = image_transform(image)
    
    # The model expects inputs of shape: B x C x H x W
    image = image.unsqueeze(0)
    
    prediction = model(image)
    prediction = F.softmax(prediction, dim=1).flatten()

    # return {imagenet_id_to_classname[str(i)]: float(prediction[i]) for i in range(1000)}

    pred_classes = prediction.topk(k=5).indices
    pred_class_names = [imagenet_id_to_classname[str(i.item())] for i in pred_classes[0]]
    pred_class_probs = [prediction[0][i.item()].item() * 100 for i in pred_classes[0]]
    res = "Top 5 predicted labels:\n"
    for name, prob in zip(pred_class_names, pred_class_probs):
        res += f"[{prob:2.2f}%]\t{name}\n"

    return res


def set_example_image(example: list) -> dict:
    return gr.Image.update(value=example[0])


demo = gr.Blocks()
with demo:
    gr.Markdown(
        """
        # UniFormer-S
        Gradio demo for UniFormer: To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.
        """
    )

    with gr.Box():
        with gr.Row():
                with gr.Column():
                    with gr.Row():
                        input_image = gr.Image(label='Input Image', type='file')
                    with gr.Row():
                        submit_button = gr.Button('Submit')
                with gr.Column():
                    label = gr.Label(num_top_classes=5)
        with gr.Row():
            example_images = gr.Dataset(components=[input_image], samples=[['library.jpeg'], ['cat.png'], ['dog.png'], ['panda.png']])

    gr.Markdown(
        """
        <p style='text-align: center'><a href='https://arxiv.org/abs/2201.09450' target='_blank'>UniFormer: Unifying Convolution and Self-attention for Visual Recognition</a> | <a href='https://github.com/Sense-X/UniFormer' target='_blank'>Github Repo</a></p>
        """
    )

    submit_button.click(fn=inference, inputs=input_image, outputs=label)
    example_images.click(fn=set_example_image, inputs=example_images, outputs=example_images.components)

demo.launch(enable_queue=True, cache_examples=True)



# inputs = gr.inputs.Image(type='pil')
# label = gr.outputs.Label(num_top_classes=5)

# title = "UniFormer-S"
# description = "Gradio demo for UniFormer: To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
# article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2201.09450' target='_blank'>UniFormer: Unifying Convolution and Self-attention for Visual Recognition</a> | <a href='https://github.com/Sense-X/UniFormer' target='_blank'>Github Repo</a></p>"

# gr.Interface(
#     inference, inputs, outputs=label, 
#     title=title, description=description, article=article, 
#     examples=[['library.jpeg'], ['cat.png'], ['dog.png'], ['panda.png']]
#     ).launch(enable_queue=True, cache_examples=True)