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Running
on
Zero
Running
on
Zero
import gradio as gr | |
from transformers import pipeline | |
raven_pipeline = pipeline( | |
"text-generation", | |
model="Nexusflow/NexusRaven-V2-13B", | |
torch_dtype="auto", | |
device_map="auto", | |
) | |
class DialogueToSpeechConverter: | |
def __init__(self): | |
self.raven_pipeline = raven_pipeline | |
def process_text(self, input_text: str) -> str: | |
prompt = f"User Query: {input_text}<human_end>" | |
result = self.raven_pipeline(prompt, max_new_tokens=2048, return_full_text=False, do_sample=False, temperature=0.001)[0]["generated_text"] | |
return result | |
# Gradio interface | |
def create_interface(): | |
converter = DialogueToSpeechConverter() | |
with gr.Blocks() as app: | |
gr.Markdown("""# 🙋🏻♂️Welcome to🌟Tonic's Nexus🐦⬛Raven""") | |
gr.Markdown("""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.""") | |
with gr.Row(): | |
input_text = gr.Textbox(label="Input Text") | |
output_text = gr.Textbox(label="Nexus🐦⬛Raven", readonly=True) | |
input_text.change(converter.process_text, inputs=input_text, outputs=output_text) | |
return app | |
if __name__ == "__main__": | |
app = create_interface() | |
app.launch() |