app.py

import gradio as gr
import torch
from torch import nn
from torch.nn import functional as F
import torchvision
from torchvision import transforms

model= torch.jit.load('best.pt')
data_transform1=transforms.Compose([
    transforms.Resize((640,640)),
    transforms.ToTensor(),
    transforms.Normalize((0.485,0.456,0.406),(0.229,0.224,0.225))
])

title = " Fashion Items Classification"

examples=[['https://github.com/Kr1n3/MPC_2022/blob/main/dataset/pants_33.jpg?raw=true'],['https://github.com/Kr1n3/MPC_2022/blob/main/dataset/pants_30.jpeg?raw=true'],['https://github.com/Kr1n3/MPC_2022/blob/main/dataset/bag_01.jpg?raw=true']]

classes=['Bags','Dress','Pants','Shoes','Skirt']
def predict(img):
  imag=data_transform1(img)
  inp =imag.unsqueeze(0)
  outputs=model(inp)
  pred=F.softmax(outputs[0], dim=0).cpu().data.numpy()
  confidences = {classes[i]:(float(pred[i])) for i in range(5)}    
  return confidences

gr.Interface(predict,gr.inputs.Image(type='pil'),title=title,examples=examples,outputs='label').launch(debug=True)