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import gradio as gr | |
import os | |
from huggingface_hub import from_pretrained_keras | |
import tensorflow as tf | |
import numpy as np | |
model = from_pretrained_keras("keras-io/bit") | |
allImages = [] | |
directory = 'images' | |
CLASSES = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips'] | |
# iterate over files in images directory | |
for filename in os.listdir(directory): | |
f = os.path.join(directory, filename) | |
if os.path.isfile(f): | |
allImages.append(f) | |
def flower_classifier(image): | |
print(image.shape) | |
image = tf.image.resize(image, (224, 224)) | |
image = image / 255.0 | |
image = tf.expand_dims(image, 0) | |
pred = np.argmax(model(image)) | |
label = CLASSES[pred] | |
return label | |
title = "Image Classification using BigTransfer (BiT)" | |
description = "This space finetunes BigTransfer (BiT) to classify images from Flower dataset" | |
article = """<p style='text-align: center'> | |
<a href='https://keras.io/examples/vision/bit/' target='_blank'>Keras Example given by Sayan Nath</a> | |
<br> | |
Space by @rushic24 | |
</p> | |
""" | |
iface = gr.Interface(flower_classifier, | |
title = "Image Classification using BigTransfer (BiT)", | |
inputs = gr.inputs.Image(), | |
outputs = gr.outputs.Label(num_top_classes=5), | |
capture_session=True, | |
examples = allImages, | |
) | |
iface.launch(debug=True) |