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

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/onnx/models/raw/main/vision/classification/inception_and_googlenet/googlenet/model/googlenet-9.onnx")

# Enter path to the ONNX model file

sym, arg_params, aux_params = import_model('googlenet-9.onnx')

Batch = namedtuple('Batch', ['data'])

    
def predict(path):
    img = get_image(path)
    img = preprocess(img)
    mod.forward(Batch([img]))
    # Take softmax to generate probabilities
    scores = mx.ndarray.softmax(mod.get_outputs()[0]).asnumpy()
    # print the top-5 inferences class
    scores = np.squeeze(scores)
    a = np.argsort(scores)[::-1]
    results = {}
    for i in a[0:5]:
        results[labels[i]] = float(scores[i])
    return results
        
 # Determine and set context
if len(mx.test_utils.list_gpus())==0:
    ctx = mx.cpu()
else:
    ctx = mx.gpu(0)
# Load module
mod = mx.mod.Module(symbol=sym, context=ctx, data_names=['data_0'], label_names=None)
mod.bind(for_training=False, data_shapes=[('data_0', (1,3,224,224))],label_shapes=mod._label_shapes)
mod.set_params(arg_params, aux_params, allow_missing=True, allow_extra=True)

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)