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import gradio as gr
from transformers import pipeline
model_pipeline = pipeline("text2text-generation", model="tribler/dsi-search-on-toy-dataset")
def process_query(query):
results = model_pipeline(query, max_length=60)
result_text = results[0]['generated_text'].strip()
if result_text.startswith("http"):
youtube_id = result_text.split('watch?v=')[-1]
iframe = f'<iframe width="560" height="315" src="https://www.youtube.com/embed/{youtube_id}" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>'
return gr.HTML(iframe)
elif result_text.startswith("magnet"):
return gr.HTML(f'<a href="{result_text}" target="_blank">{result_text}</a>')
else:
bitcoin_logo_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/4/46/Bitcoin.svg/800px-Bitcoin.svg.png"
return gr.Textbox(f'<div style="display:flex;align-items:center;"><img src="{bitcoin_logo_url}" alt="Bitcoin Logo" style="width:20px;height:20px;margin-right:5px;"><span>{result_text}</span></div>')
interface = gr.Interface(fn=process_query,
inputs=gr.Textbox(label="Query"),
outputs="html",
title="Search Interface",
submit_btn="Find",
description="""
### Search for movie trailers, music torrents, and bitcoin wallet addresses!
This toy example knows about 500 URLs exactly after merely a few hours of training on a single GPU ([view dataset](https://huggingface.co/tribler/dsi-search-on-toy-dataset/blob/main/dataset.csv)).
""",
article="""
This project represents both a groundbreaking advance and a preliminary exploration into decentralized systems.
As a preliminary model, the project showcases a toy example rather than the full potential of its ultimate capabilities.
It serves as a proof of concept that invites further development and imagination.
""",
examples=[["spider man"], ["oceans 13"], ["sister starlight"], ["bitcoin address of xileno"]],
concurrency_limit=50)
if __name__ == "__main__":
interface.launch()
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