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import gradio as gr
import utils
# Araclip demo
with gr.Blocks() as demo_araclip:
gr.Markdown("## Choose the dataset")
dadtaset_select = gr.Radio(["XTD dataset", "Flicker 8k dataset"], value="XTD dataset", label="Dataset", info="Which dataset you would like to search in?")
gr.Markdown("## Input parameters")
txt = gr.Textbox(label="Text Query")
num = gr.Slider(label="Number of retrieved image", value=1, minimum=1, step=1)
with gr.Row():
btn = gr.Button("Retrieve images", scale=1)
gr.Markdown("## Retrieved Images")
gallery = gr.Gallery(
show_label=False, elem_id="gallery"
, columns=[5], rows=[1], object_fit="contain", height="auto")
with gr.Row():
lables = gr.Label(label="Text-image similarity")
btn.click(utils.predict, inputs=[txt, num, dadtaset_select], outputs=[gallery,lables])
gr.Examples(
examples=[["تخطي لاعب فريق بيتسبرج بايرتس منطقة اللوحة الرئيسية في مباراة بدوري البيسبول", 5],
["وقوف قطة بمخالبها على فأرة حاسوب على المكتب", 10],
["صحن به شوربة صينية بالخضار، وإلى جانبه بطاطس مقلية وزجاجة ماء", 7]],
inputs=[txt, num, dadtaset_select],
outputs=[gallery,lables],
fn=utils.predict,
cache_examples=False,
)
# mclip demo
with gr.Blocks() as demo_mclip:
gr.Markdown("## Choose the dataset")
dadtaset_select = gr.Radio(["XTD dataset", "Flicker 8k dataset"], value="XTD dataset", label="Dataset", info="Which dataset you would like to search in?")
gr.Markdown("## Input parameters")
txt = gr.Textbox(label="Text Query")
num = gr.Slider(label="Number of retrieved image", value=1, minimum=1, step=1)
with gr.Row():
btn = gr.Button("Retrieve images", scale=1)
gr.Markdown("## Retrieved Images")
gallery = gr.Gallery(
label="Generated images", show_label=True, elem_id="gallery_mclip"
, columns=[5], rows=[1], object_fit="contain", height="auto")
lables = gr.Label()
btn.click(utils.predict_mclip, inputs=[txt, num, dadtaset_select], outputs=[gallery,lables])
gr.Examples(
examples=[["تخطي لاعب فريق بيتسبرج بايرتس منطقة اللوحة الرئيسية في مباراة بدوري البيسبول", 5],
["وقوف قطة بمخالبها على فأرة حاسوب على المكتب", 10],
["صحن به شوربة صينية بالخضار، وإلى جانبه بطاطس مقلية وزجاجة ماء", 7]],
inputs=[txt, num, dadtaset_select],
outputs=[gallery,lables],
fn=utils.predict_mclip,
cache_examples=False,
)
# Define custom CSS to increase the size of the tabs
custom_css = """
.gr-tabbed-interface .gr-tab {
font-size: 50px; /* Increase the font size */
padding: 10px; /* Increase the padding */
}
"""
# Group the demos in a TabbedInterface
with gr.Blocks() as demo:
# gr.Image("statics/logo_araclip.png")
gr.Markdown("""
<center> <img src="/file=statics/logo_araclip.png" alt="Imgur" style="width:200px"></center>
""")
gr.Markdown("<center> <font color=red size=10>AraClip: Arabic Image Retrieval Application</font></center>")
gr.Markdown("""
<font size=4> To run the demo 🤗, please select the model, then the dataset you would like to search in, enter a text query, and specify the number of retrieved images.</font>
""")
gr.TabbedInterface([demo_araclip, demo_mclip], ["Our Model", "Mclip model"], css=custom_css)
gr.Markdown(
"""
If you find this work helpful, please help us to ⭐ the repositories in <a href='https://github.com/Arabic-Clip' target='_blank'>Github Organization</a>. Thank you!
---
📝 **Citation**
To be shared soon.
📋 **License**
"""
)
if __name__ == "__main__":
demo.launch() |