app.py

import mxnet as mx
import matplotlib.pyplot as plt
import numpy as np
from collections import namedtuple
from mxnet.gluon.data.vision import transforms
import os
import gradio as gr

from PIL import Image
import imageio
import onnxruntime as ort
from torchvision import transforms


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]),
])

mx.test_utils.download('https://s3.amazonaws.com/model-server/inputs/kitten.jpg')

mx.test_utils.download('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/densenet-121/model/densenet-9.onnx")

ort_session = ort.InferenceSession("densenet-9.onnx")


def predict(pil):
    input_tensor = preprocess(pil)
    img_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model

    outputs = ort_session.run(
        None,
        {"data_0": img_batch.astype(np.float32)},
    )

    a = np.argsort(-outputs[0].flatten())
    results = {}
    for i in a[0:5]:
        results[labels[i]]=float(outputs[0][0][i])
    return results
       

title="DenseNet-121"
description="DenseNet-121 is a convolutional neural network for classification."

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