Create app.py

app.py

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+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
+
+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/caffenet/model/caffenet-12.onnx")
+
+ort_session = ort.InferenceSession("caffenet-12.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[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)