Update app.py

app.py

@@ -3,12 +3,12 @@ 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):
     '''
@@ -39,41 +39,28 @@ 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")
+os.system("wget https://github.com/AK391/models/raw/main/vision/classification/alexnet/model/bvlcalexnet-7.onnx")
 
-# Enter path to the ONNX model file
-
-sym, arg_params, aux_params = import_model('googlenet-9.onnx')
-
-Batch = namedtuple('Batch', ['data'])
+ort_session = ort.InferenceSession("bvlcalexnet-7.onnx")
 
     
 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]
+    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(scores[i])
+        results[labels[i]]=float(outputs[0][0][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."
+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)
+gr.Interface(predict,gr.inputs.Image(type='filepath'),"label",title=title,description=description,examples=examples).launch(enable_queue=True,debug=True)