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app.py

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+from keras.models import load_model
+from PIL import Image, ImageOps
+import numpy as np
+import gradio as gr
+import pandas as pd
+  
+def format_label(label):
+  """
+  From '0 rùa khác\n' to 'rùa khác'
+  """
+  return label[label.find(" ")+1:-1]
+
+def predict(image):
+
+  # Load the model
+  model = load_model('keras_model.h5')
+
+  # Create the array of the right shape to feed into the keras model
+  # The 'length' or number of images you can put into the array is
+  # determined by the first position in the shape tuple, in this case 1.
+  data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+  
+  #resize the image to a 224x224 with the same strategy as in TM2:
+  #resizing the image to be at least 224x224 and then cropping from the center
+  size = (224, 224)
+  image = ImageOps.fit(image, size, Image.ANTIALIAS)
+
+  #turn the image into a numpy array
+  image_array = np.asarray(image)
+  # Normalize the image
+  normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+  # Load the image into the array
+  data[0] = normalized_image_array
+
+  # run the inference
+  pred = model.predict(data)
+  pred = pred.tolist()
+
+  with open('labels.txt','r') as f:
+    labels = f.readlines()
+  
+
+  result = {format_label(labels[i]): round(pred[0][i],2) for i in range(len(pred[0]))}
+  
+  return result
+  
+  
+description="""
+
+Description
+
+"""
+
+title = """
+
+Title
+
+"""
+
+examples = [['example1.jpg'], ['example2.jpg'], ['example3.jpg']]
+
+gr.Interface(fn=predict, 
+             inputs=gr.Image(type="pil", label="Input Image"),
+             outputs=[gr.Label(), gr.Markdown()],
+             live=True,
+             title=title,
+             description=description,
+             examples=examples).launch()

requirements.txt

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+tensorflow
+Pillow
+numpy
+pandas