app.py

import onnx
import numpy as np
import onnxruntime as ort
from PIL import Image
import cv2
import os
import gradio as gr

import mxnet
from torchvision import transforms

os.system("wget https://s3.amazonaws.com/onnx-model-zoo/synset.txt")


with open('synset.txt', 'r') as f:
    labels = [l.rstrip() for l in f]
    
os.system("wget https://github.com/AK391/models/raw/main/vision/classification/shufflenet/model/shufflenet-v2-10.onnx")

os.system("wget https://s3.amazonaws.com/model-server/inputs/kitten.jpg")



model_path = 'shufflenet-v2-10.onnx'
model = onnx.load(model_path)
session = ort.InferenceSession(model.SerializeToString())


    
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]),
])
        

    

def predict(img):
    input_tensor = preprocess(img)
    img = input_tensor.unsqueeze(0)
    ort_inputs = {session.get_inputs()[0].name: img.cpu().detach().numpy()}
    preds = session.run(None, ort_inputs)[0]
    preds = np.squeeze(preds)
    a = np.argsort(preds)
    results = {}
    for i in a[0:5]:    
        results[labels[a[i]]] = float(preds[a[i]])
    return results
       

title="ShuffleNet-v2"
description="ShuffleNet is a deep convolutional network for image classification. ShuffleNetV2 is an improved architecture that is the state-of-the-art in terms of speed and accuracy tradeoff used for image classification."

examples=[['kitten.jpg']]
gr.Interface(predict,gr.inputs.Image(type='pil'),"label",title=title,description=description,examples=examples).launch(enable_queue=True,debug=True)