Create app.py

app.py

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+import numpy as np
+import math
+import matplotlib.pyplot as plt
+import onnxruntime as rt
+import cv2
+import json
+import gradio as gr
+from huggingface_hub import hf_hub_download
+import onnxruntime as rt
+
+modele = hf_hub_download(repo_id="onnx/EfficientNet-Lite4", filename="efficientnet-lite4-11.onnx")
+# load the labels text file
+labels = json.load(open("labels_map.txt", "r"))
+
+# set image file dimensions to 224x224 by resizing and cropping image from center
+def pre_process_edgetpu(img, dims):
+    output_height, output_width, _ = dims
+    img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2.INTER_LINEAR)
+    img = center_crop(img, output_height, output_width)
+    img = np.asarray(img, dtype='float32')
+    # converts jpg pixel value from [0 - 255] to float array [-1.0 - 1.0]
+    img -= [127.0, 127.0, 127.0]
+    img /= [128.0, 128.0, 128.0]
+    return img
+
+# resize the image with a proportional scale
+def resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR):
+    height, width, _ = img.shape
+    new_height = int(100. * out_height / scale)
+    new_width = int(100. * out_width / scale)
+    if height > width:
+        w = new_width
+        h = int(new_height * height / width)
+    else:
+        h = new_height
+        w = int(new_width * width / height)
+    img = cv2.resize(img, (w, h), interpolation=inter_pol)
+    return img
+
+# crop the image around the center based on given height and width
+def center_crop(img, out_height, out_width):
+    height, width, _ = img.shape
+    left = int((width - out_width) / 2)
+    right = int((width + out_width) / 2)
+    top = int((height - out_height) / 2)
+    bottom = int((height + out_height) / 2)
+    img = img[top:bottom, left:right]
+    return img
+
+
+sess = rt.InferenceSession(modele)
+
+def inference(img):
+  img = cv2.imread(img)
+  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+  
+  img = pre_process_edgetpu(img, (224, 224, 3))
+  
+  img_batch = np.expand_dims(img, axis=0)
+
+  results = sess.run(["Softmax:0"], {"images:0": img_batch})[0]
+  result = reversed(results[0].argsort()[-5:])
+  resultdic = {}
+  for r in result:
+      resultdic[labels[str(r)]] = float(results[0][r])
+  return resultdic
+  
+title="EfficientNet-Lite4"
+description="EfficientNet-Lite 4 is the largest variant and most accurate of the set of EfficientNet-Lite model. It is an integer-only quantized model that produces the highest accuracy of all of the EfficientNet models. It achieves 80.4% ImageNet top-1 accuracy, while still running in real-time (e.g. 30ms/image) on a Pixel 4 CPU."
+examples=[['catonnx.jpg']]
+gr.Interface(inference,gr.inputs.Image(type="filepath"),"label",title=title,description=description,examples=examples).launch()