Create app.py

app.py

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+
+import numpy as np
+import pandas as pd
+import matplotlib.pylab as plt
+import gradio as gr
+import PIL.Image as Image
+import tensorflow as tf
+import tensorflow_hub as hub
+
+TF_MODEL_URL = 'https://tfhub.dev/google/on_device_vision/classifier/landmarks_classifier_asia_V1/1'
+LABEL_MAP_URL = 'https://www.gstatic.com/aihub/tfhub/labelmaps/landmarks_classifier_asia_V1_label_map.csv'
+IMAGE_SHAPE = (321, 321)
+
+classifier = tf.keras.Sequential([hub.KerasLayer(TF_MODEL_URL,
+                                                 input_shape=IMAGE_SHAPE+(3,),
+                                                 output_key="predictions:logits")])
+
+df = pd.read_csv(LABEL_MAP_URL)
+
+label_map = dict(zip(df.id, df.name))
+
+label_map
+
+img_loc = "image.jpeg"
+
+img = Image.open(img_loc).resize(IMAGE_SHAPE)
+
+img
+
+img = np.array(img)/255.0
+img.shape
+
+img = img[np.newaxis, ...]
+
+img.shape
+
+result = classifier.predict(img)
+
+result
+
+label_map[np.argmax(result)]
+
+class_names=list(label_map.values())
+
+def classify_image(image):
+    img = np.array(image)/255.0
+    img = img[np.newaxis, ...]
+    prediction = classifier.predict(img)
+    return label_map[np.argmax(prediction)]
+
+image = gr.inputs.Image(shape=(321, 321))
+label = gr.outputs.Label(num_top_classes=1)
+
+gr.Interface(    
+    classify_image, 
+    image, 
+    label,
+    capture_session=True).launch(debug=True);