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import numpy as np | |
import gradio as gr | |
from model import api | |
from PIL import Image | |
sign_api = api() | |
def sign(input_img): | |
input_img = Image.fromarray(input_img) | |
prediction = sign_api.predict(input_img) | |
print('prediction',prediction) | |
return prediction['class'] | |
css = '' | |
# with gr.Blocks(css=css) as demo: | |
# gr.HTML("<h1><center>Signsapp: Classify the signs based on the hands sign images<center><h1>") | |
# gr.Interface(sign,inputs=gr.Image(shape=(200, 200)), outputs=gr.Label()) | |
title = r"Signsapp" | |
description = r""" | |
<center> | |
Classify the signs based on the hands sign images | |
<img src="file/SIGNS.png" width=200px> | |
</center> | |
""" | |
article = r""" | |
- [Coursera](https://arxiv.org/pdf/1512.03385) | |
""" | |
demo = gr.Interface( | |
title = title, | |
description = description, | |
article = article, | |
fn=sign, | |
inputs = gr.Image(shape=(200, 200)), | |
outputs = gr.Label(), | |
# allow_flagging = "manual", | |
# flagging_options = ['recule', 'tournedroite', 'arretetoi', 'tournegauche', 'gauche', 'avance', 'droite'], | |
# flagging_dir = "./flag/men" | |
) | |
# demo.queue() | |
demo.launch(debug=True) | |