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98b1303 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | import gradio as gr
import tensorflow as tf
import cv2
model = tf.keras.models.load_model(r"./vgg16_model.keras")
class_names = [
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck",
]
def predict(image):
# Resize image to (32, 32)
image = cv2.resize(image, (32, 32))
print("Resized image shape:", image.shape) # Print the shape of the resized image
# Convert image to float32 and normalize
image = image.astype("float32") / 255.0
# Add batch dimension
image = tf.expand_dims(image, 0)
# Predict using the model
prediction = model.predict(image)
class_index = tf.argmax(prediction, axis=1)[0].numpy()
class_label = class_names[class_index] # Get the class label
return class_label
gr.Interface(fn=predict, inputs="image", outputs="text").launch(share=True)
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