Create app.py

app.py

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+import onnx
+import numpy as np
+import onnxruntime as ort
+from PIL import Image
+import cv2
+import os
+import gradio as gr
+
+import mxnet
+from mxnet.gluon.data.vision import transforms
+
+os.system("wget 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-9.onnx")
+
+os.system("wget https://s3.amazonaws.com/model-server/inputs/kitten.jpg")
+
+
+
+model_path = 'shufflenet-9.onnx'
+model = onnx.load(model_path)
+session = ort.InferenceSession(model.SerializeToString())
+
+def get_image(path):
+    with Image.open(path) as img:
+        img = np.array(img.convert('RGB'))
+    return img
+    
+def preprocess(img):
+    '''
+    Preprocessing required on the images for inference with mxnet gluon
+    The function takes path to an image and returns processed tensor
+    '''
+    transform_fn = transforms.Compose([
+    transforms.Resize(224),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    img = mxnet.ndarray.array(img)
+    img = transform_fn(img)
+    img = img.expand_dims(axis=0) # batchify
+
+    return img.asnumpy()
+    
+
+def predict(path):
+    img = get_image(path)
+    img = preprocess(img)
+    ort_inputs = {session.get_inputs()[0].name: img}
+    preds = session.run(None, ort_inputs)[0]
+    preds = np.squeeze(preds)
+    a = np.argsort(preds)
+    results = {}
+    for i in a[0:5]:    
+        results[labels[a[i]]] = float(preds[a[i]])
+    return results
+       
+
+title="ShuffleNet-v1"
+description="ShuffleNet is a deep convolutional network for image classification. ShuffleNetV2 is an improved architecture that is the state-of-the-art in terms of speed and accuracy tradeoff used for image classification."
+
+examples=[['kitten.jpg']]
+gr.Interface(predict,gr.inputs.Image(type='filepath'),"label",title=title,description=description,examples=examples).launch(enable_queue=True,debug=True)