DorjTranslator / app.py
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import os
import re
import gradio as gr
import edge_tts
import asyncio
import time
import tempfile
from huggingface_hub import InferenceClient
Fast = """## Fastest Model"""
Complex = """## Best in Complex Question"""
Detail = """## Best for Detailed Generation or Long Answers"""
client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
system_instructions1 = "<s>[SYSTEM] You are AI assistant named DorjGPT, developed by Dorjzodovsuren. Keep conversation very short, clear, friendly and concise. The text provided is a request for a specific type of response from you, the virtual assistant. The request asks you to provide friendly responses as if super interlligent AI assistant. The expectation is that I will avoid introductions and start answering the query directly, elaborating on all aspects of the request. As an AI-powered assistant, your task is to generate responses that appear as if they are created by the user and not an AI-powered assistant. [USER]"
async def generate1(prompt, a=None, b=None):
generate_kwargs = dict(
temperature=0.6,
max_new_tokens=256,
top_p=0.95,
repetition_penalty=1,
do_sample=True,
seed=42,
)
formatted_prompt = system_instructions1 + prompt + "[JARVIS]"
stream = client1.text_generation(
formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
output = ""
for response in stream:
output += response.token.text
output = output.replace("</s>","")
communicate = edge_tts.Communicate(output)
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
tmp_path = tmp_file.name
await communicate.save(tmp_path)
yield tmp_path
with gr.Blocks(theme="gradio/monochrome", title="Dorj Assistant") as demo:
gr.HTML("""
<h1 style="text-align: center; style="font-size: 3m;">
DorjGPT
</h1>
""")
with gr.Column():
output_audio = gr.Audio(label="DorjGPT", type="filepath",
interactive=False,
autoplay=True,
elem_classes="audio")
user_input = gr.Textbox(label="Prompt", value="What is Mongolia")
with gr.Tab():
with gr.Row():
translate_btn = gr.Button("Response")
translate_btn.click(fn=generate1, inputs=user_input,
outputs=output_audio, api_name="translate")
if __name__ == "__main__":
demo.queue(max_size=30).launch()