import requests
import gradio as gr
import torch
from timm import create_model
from timm import resolve_data_config
from timm.data.transforms_factory import create_transform 

IMAGENET_1k_URL = "https://storage.googleapis.com/bit_models/ilsvrc2012_wordnet_lemmas.txt"
LABELS = requests.get()

model = create_model('resnet50',pretrained=True)
transform = create_transform(**resolve_data_config({},model=model))

model.eval()

def predict(img):
    img = img.convert('RGB')
    img = transform(img).unsqueeze(0)
    with torch.no_grad():
        out=  model(img)
    probability = torch.nn.functional.softmax(out[0],dim=0)

    values, indices = torch.topk(probability,k=5)
    return {LABELS[i]: v.items() for i,v in zip(indices,values)}


iface = gr.Interface(fn=predict, inputs=gr.inputs.Image(type='pil'), outputs="label").launch()
iface.launch()