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import argparse | |
import gradio as gr | |
from ui import chat | |
def main(args): | |
demo = gr.ChatInterface( | |
fn=chat, | |
examples=["hello", "how are you?", "What is Large Language Model?"], | |
title="Gradio 🤝 TGI", | |
description="This space is a template that you can fork/duplicate for your own usage. " | |
"This space let you build LLM powered idea on top of [Gradio](https://www.gradio.app/) " | |
"and open LLM served locally by [TGI(Text Generation Inference)](https://huggingface.co/docs/text-generation-inference/en/index). " | |
"Below is a placeholder Gradio ChatInterface for you to try out Mistral-7B backed by the power of TGI's efficiency. \n\n" | |
"To use this space for your own usecase, follow the simple steps below:\n" | |
"1. [Duplicate](https://huggingface.co/spaces/chansung/gradio_together_tgi/blob/main/app/main.py?duplicate=true) this space. \n" | |
"2. Set which LLM you wish to use (i.e. mistralai/Mistral-7B-Instruct-v0.2). \n" | |
"3. Inside [app/main.py](https://huggingface.co/spaces/chansung/gradio_together_tgi/blob/main/app/main.py), write Gradio application. \n" | |
"4. (Bonus➕) [app/gen](https://huggingface.co/spaces/chansung/gradio_together_tgi/tree/main/app/gen) provides handy utility functions " | |
"to aynchronously generate text by interacting with the locally served LLM.", | |
multimodal=False | |
) | |
demo.queue( | |
default_concurrency_limit=20, | |
max_size=256 | |
).launch(server_name="0.0.0.0", server_port=args.port) | |
if __name__ == "__main__": | |
parser = argparse.ArgumentParser(description="This is my Gradio app's description") | |
parser.add_argument("--port", type=int, default=7860, help="Port to expose Gradio app") | |
args = parser.parse_args() | |
main(args) | |