import mxnet as mx
import matplotlib.pyplot as plt
import numpy as np
from collections import namedtuple
from mxnet.gluon.data.vision import transforms
from mxnet.contrib.onnx.onnx2mx.import_model import import_model
import os
import gradio as gr

from PIL import Image
import imageio
import onnxruntime as ort

def get_image(path):
    '''
        Using path to image, return the RGB load image
    '''
    img = imageio.imread(path, pilmode='RGB')
    return img

# Pre-processing function for ImageNet models using numpy
def preprocess(img):
    '''
    Preprocessing required on the images for inference with mxnet gluon
    The function takes loaded image and returns processed tensor
    '''
    img = np.array(Image.fromarray(img).resize((224, 224))).astype(np.float32)
    img[:, :, 0] -= 123.68
    img[:, :, 1] -= 116.779
    img[:, :, 2] -= 103.939
    img[:,:,[0,1,2]] = img[:,:,[2,1,0]]
    img = img.transpose((2, 0, 1))
    img = np.expand_dims(img, axis=0)

    return img

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/alexnet/model/bvlcalexnet-7.onnx")

ort_session = ort.InferenceSession("bvlcalexnet-7.onnx")

    
def predict(path):
    img_batch = preprocess(get_image(path))

    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[label[i]]=outputs[0][0][i]
    return results
       

title="AlexNet"
description="AlexNet is the name of a convolutional neural network for classification, which competed in the ImageNet Large Scale Visual Recognition Challenge in 2012."

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