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app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+
+def softmax(vector):
+    e = np.exp(vector)
+    return e / e.sum()
+
+def image_to_output (input_img):
+    gr_img = []
+    gr_img.append(input_img)
+    img2 = tf.image.resize(tf.cast(gr_img, tf.float32)/255. , [224, 224])
+
+    # print(img2)
+
+    x_test = np.asarray(img2)
+
+    prediction = model2.predict(x_test,batch_size=1).flatten()
+    prediction = softmax(prediction)
+
+    confidences = {labels[i]: float(prediction[i]) for i in range(102)}
+    # confidences = {labels[i]:float(top[i]) for i in range(num_predictions)}
+
+    return confidences
+
+# Download the model checkpoint
+import os
+import requests
+pretrained_repo = 'pretrained_model'
+model_repo_link = 'https://huggingface.co/qmjnh/flowerClassification_2/resolve/main'
+for item in [
+        'variables.data-00000-of-00001',
+        'variables.index',
+        'keras_metadata.pb',
+        'saved_model.pb',
+    ]:
+    params = requests.get(model_repo_link+item)
+    if item.startswith('variables'):
+        output_file = os.path.join(pretrained_repo, 'variables', item)
+    else:
+        output_file = os.path.join(pretrained_repo, item)
+    if not os.path.exists(os.path.dirname(output_file)):
+        os.makedirs(os.path.dirname(output_file))
+    with open(output_file, 'wb') as f: 
+        print(f'Downloading from {model_repo_link+item} to {output_file}')
+        f.write(params.content)
+    
+
+# Load the model
+model2=tf.keras.models.load_model(pretrained_repo)
+
+# Read the labels
+with open('flower_names.txt') as f:
+    labels = f.readlines()
+
+# Run gradio
+from gradio.components import Image as gradio_image
+from gradio.components import Label as gradio_label
+UI=gr.Interface(fn=image_to_output, 
+             inputs=gradio_image(shape=(224,224)),
+             outputs=gradio_label(num_top_classes=5),
+             interpretation="default"
+             )
+UI.launch()