import gradio as gr
import os
from PIL import Image
import torch
from torchvision import transforms

import torch
model = torch.hub.load('pytorch/vision:v0.10.0', 'wide_resnet50_2', pretrained=True)
model = torch.hub.load('pytorch/vision:v0.10.0', 'wide_resnet101_2', pretrained=True)
model.eval()
os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")
torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")

def inference(input_image):
    preprocess = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])
    input_tensor = preprocess(input_image)
    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
    if torch.cuda.is_available():
        input_batch = input_batch.to('cuda')
        model.to('cuda')
    with torch.no_grad():
        output = model(input_batch)
    probabilities = torch.nn.functional.softmax(output[0], dim=0)
    with open("imagenet_classes.txt", "r") as f:
        categories = [s.strip() for s in f.readlines()]
    top5_prob, top5_catid = torch.topk(probabilities, 5)
    result = {}
    for i in range(top5_prob.size(0)):
        result[categories[top5_catid[i]]] = top5_prob[i].item()
    return result

inputs = gr.inputs.Image(type='pil')
outputs = gr.outputs.Label(type="confidences",num_top_classes=5)

title = "WRN - Wide Residual Networks"
description = "ResNet blocks based architecture where depth is decreased and width of residual networks is increased."
article = "<p style='text-align: center'><a href='https://paperswithcode.com/paper/wide-residual-networks'>Wide Residual Networks on Papers With Code</a></p>"
examples = [
            ['1.jpg'],
            ['2.jpg'],
            ['3.jpg'],
            ['4.jpg'],
            ['5.jpg'],
            ['20190210_171436.jpg'],
            ['20190211_215501.jpg'],
            ['20190220_220143.jpg'],
            ['20190223_181415.jpg'],
            ['20190404_193912.jpg'],
            ['20190413_021309.jpg'],
            ['20190413_115659.jpg']
]
gr.Interface(
    inference, 
    inputs, 
    outputs, 
    title=title, 
    description=description, 
    article=article, 
    examples=examples, 
    analytics_enabled=True
).launch()