Create app.py

app.py

@@ -0,0 +1,57 @@
+import numpy as np
+import tensorflow as tf
+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+
+teacher_model = from_pretrained_keras("Blazer007/consistency_training_with_supervision_teacher_model")
+
+student_model = from_pretrained_keras("Blazer007/consistency_training_with_supervision_student_model")
+
+class_names = [
+    "Airplane",
+    "Automobile",
+    "Bird",
+    "Cat",
+    "Deer",
+    "Dog",
+    "Frog",
+    "Horse",
+    "Ship",
+    "Truck",
+]
+
+IMG_SIZE = 72
+
+def infer(input_image):
+    print('#$$$$$$$$$$$$$$$$$$$$$$$$$ IN INFER $$$$$$$$$$$$$$$$$$$$$$$')
+    # image_tensor = read_image(input_image)
+    image_tensor = tf.convert_to_tensor(input_image)
+    image_tensor.set_shape([None, None, 3])
+    image_tensor = tf.image.resize(image_tensor, (IMG_SIZE, IMG_SIZE))
+    print(image_tensor.shape)
+    predictions = teacher_model.predict(np.expand_dims((image_tensor), axis=0))
+    print(predictions)
+    predictions = np.squeeze(predictions)
+    print(predictions)
+    predictions = np.argmax(predictions) # , axis=2
+    print(predictions)
+    predicted_label = class_names[predictions.item()]
+    print(predictions.item())
+    print(predicted_label)
+    return str(predicted_label)
+    
+input = gr.inputs.Image(shape=(IMG_SIZE, IMG_SIZE))
+output = [gr.outputs.Label()]
+examples = [[], []] 
+title = "Image Classification using "
+description = "Upload an image or select from examples to classify it.<br>The allowed classes are - Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck.<br><p><b>Space author: Vivek Rai</b> <br><b> Keras example author: Sayak Paul </b></p>"
+
+gr_interface = gr.Interface(
+    infer, 
+    input, 
+    output, 
+    examples=examples, 
+    allow_flagging=False, 
+    analytics_enabled=False, 
+    title=title, 
+    description=description).launch(enable_queue=True, debug=True)