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NexusRaven2
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import spaces
import gradio as gr
from transformers import pipeline
import os
import torch

# Set max_split_size_mb
# os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:50'
title = """# 🙋🏻‍♂️Welcome to🌟Tonic's Nexus🐦‍⬛Raven"""
description = """You can build with this endpoint using Nexus Raven. The demo is still a work in progress but we hope to add some endpoints for commonly used functions such as intention mappers and audiobook processing.
        You can also use Nexus🐦‍⬛Raven on your laptop & by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic1/NexusRaven2?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> 
        Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)
"""

raven_pipeline = pipeline(
    "text-generation",
    model="Nexusflow/NexusRaven-V2-13B",
    torch_dtype="auto",
    device_map="auto",
)

@spaces.GPU
def process_text(input_text: str) -> str:
    prompt = f"User Query: {input_text}<human_end>"
    result = raven_pipeline(prompt, max_new_tokens=2048, return_full_text=False, do_sample=False, temperature=0.001)[0]["generated_text"]
    torch.cuda.empty_cache()
    return result

def create_interface():
    with gr.Blocks() as app:
        gr.Markdown(title)
        gr.Markdown(description)
        with gr.Row():
            input_text = gr.Textbox(label="Input Text")
            submit_button = gr.Button("Submit")
            output_text = gr.Textbox(label="Nexus🐦‍⬛Raven")
        submit_button.click(converter.process_text, inputs=input_text, outputs=output_text)
    return app


def main():
    with gr.Blocks() as demo:
        gr.Markdown(title)
        gr.Markdown(description)
        with gr.Row():
            input_text = gr.Textbox(label="Input Text", placeholder="Enter your text here...")
            submit_button = gr.Button("Submit")
        output_text = gr.Textbox(label="Nexus🐦‍⬛Raven", placeholder="Generated text will appear here...")
        submit_button.click(process_text, inputs=input_text, outputs=output_text)

    demo.launch()

if __name__ == "__main__":
    main()