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import gradio as gr
# %% drive/MyDrive/Colab Notebooks/fastbook/fastcourse/ch2minimal/tmabrahamgradio/gradiotest/gradiotest.ipynb 1
from fastai.vision.all import *
import skimage
# %% drive/MyDrive/Colab Notebooks/fastbook/fastcourse/ch2minimal/tmabrahamgradio/gradiotest/gradiotest.ipynb 4
learn = load_learner('export.pkl')
labels = learn.dls.vocab
def predict(img):
img = PILImage.create(img)
pred, pred_idx, probs = learn.predict(img)
return {labels[i]: float(probs[i]) for i in range(len(labels))}
# %% drive/MyDrive/Colab Notebooks/fastbook/fastcourse/ch2minimal/tmabrahamgradio/gradiotest/gradiotest.ipynb 7
#gr.Interface(fn=predict, inputs=gr.Image(shape=(512,512)), outputs=gr.outputs.Label(num_top_classes=3), title="Pet Breed Classifier", description="A pet breed classifier trained on the Oxford Pets dataset using the fastai library (5 epochs) as a proof of concept for Gradio.", article="<p style='text-align: center'><a href='https://gameologist.com/portfolio' target='_blank'>see more of my things here</a></p>", examples=['abys.jpg', 'download (3).jpg'], enable_queue=True).launch()
#iface = gr.Interface(fn=predict, inputs=gr.Image(shape=(512,512)), outputs=gr.outputs.Label(num_top_classes=3))
#iface.launch();
#def greet(name):
# return "Hellooo " + name + "!!"
#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface = gr.Interface(fn=predict, inputs=gr.Image(shape=(512,512)), outputs=gr.outputs.Label(num_top_classes=3), title="Pet Breed Classifier", description="A pet breed classifier trained on the Oxford Pets dataset using the fastai library (5 epochs) as a proof of concept for Gradio.", article="<p style='text-align: center'><a href='https://gameologist.com/portfolio' target='_blank'>see more of my things here</a></p>", examples=['abys.jpg', 'download (3).jpg'], enable_queue=True)
iface.launch() |