Create app.py

app.py

@@ -0,0 +1,72 @@
+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
+
+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/alexnet/model/bvlcalexnet-12.onnx")
+
+# Enter path to the ONNX model file
+
+sym, arg_params, aux_params = import_model('bvlcalexnet-12.onnx')
+
+Batch = namedtuple('Batch', ['data'])
+def get_image(path, show=False):
+    img = mx.image.imread(path)
+    if img is None:
+        return None
+    if show:
+        plt.imshow(img.asnumpy())
+        plt.axis('off')
+    return img
+    
+def preprocess(img):   
+    transform_fn = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    img = transform_fn(img)
+    img = img.expand_dims(axis=0)
+    return img
+    
+def predict(path):
+    img = get_image(path, show=True)
+    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, label_names=None)
+mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))], 
+         label_shapes=mod._label_shapes)
+mod.set_params(arg_params, aux_params, allow_missing=True, allow_extra=True)
+
+title="SqueezeNet"
+description="SqueezeNet is a small CNN which achieves AlexNet level accuracy on ImageNet with 50x fewer parameters. SqueezeNet requires less communication across servers during distributed training, less bandwidth to export a new model from the cloud to an autonomous car and more feasible to deploy on FPGAs and other hardware with limited memory."
+
+examples=[['catonnx.jpg']]
+gr.Interface(predict,gr.inputs.Image(type='filepath'),"label",title=title,description=description,examples=examples).launch(enable_queue=True)