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

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/shufflenet/model/shufflenet-v2-10.onnx")

ort_session = ort.InferenceSession("shufflenet-v2-10.onnx")

    
def predict(path):
    input_image = Image.open(path)
    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]),
    ])
    input_tensor = preprocess(input_image)
    input_batch = input_tensor.unsqueeze(0)
    outputs = ort_session.run(
        None,
        {"input": input_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="GoogleNet"
description="GoogLeNet is the name of a convolutional neural network for classification, which competed in the ImageNet Large Scale Visual Recognition Challenge in 2014."

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