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from huggingface_hub import from_pretrained_keras
import gradio as gr
import tensorflow as tf
model = from_pretrained_keras("araeynn/validation_model")
def image_classifier(inp):
class_names = ["0", "1"]
inp.save("why.png")
sunflower_path = "why.png"
img = tf.keras.utils.load_img(sunflower_path, target_size=(256, 256))
img_array = tf.keras.utils.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create a batch
predictions = model.predict(img_array)
score = tf.nn.softmax(predictions)
r = {}
print(score)
for s in score:
print(s)
for class_name in class_names:
r[class_name] = score[0][class_names.index(class_name)]
return r
demo = gr.Interface(fn=image_classifier, inputs=gr.Image(type="pil"), outputs="label")
demo.launch(debug=True)
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