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

LABELS = {0:'Cat', 1:'Dog'}
model = torch.load('CatVsDogsModel.pth',map_location='cpu')

transform = create_transform(**resolve_data_config({},model=model))


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=2)
    return {LABELS[i]: v.item() for i,v in zip(indices,values)}


transform = create_transform(**resolve_data_config({},model=model))
# we do not need to train model , hence using model.eval() to use it only for inference 
model.eval()

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