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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 | |
| from deep_translator import GoogleTranslator | |
| def get_translation(context_custom, language = 'en'): | |
| translated = GoogleTranslator(source = 'auto', target = language).translate(text = context_custom) | |
| return translated | |
| #client = InferenceClient("mistralai/Mixtral-8x22B-Instruct-v0.1") | |
| system_instructions1 = "<s>[SYSTEM] You are AI assistant named DorjGPT, 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]" | |
| global history | |
| history = [] | |
| def format_prompt(message, history): | |
| prompt = system_instructions1 | |
| for user_prompt, bot_response in history: | |
| prompt += f"[INST] {user_prompt} [/INST]" | |
| prompt += f" {bot_response}</s> " | |
| prompt += f"[INST] {message} [/INST]" | |
| return prompt | |
| async def generate1(prompt, history=[], 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]" | |
| #prompt_en = get_translation(prompt) | |
| #formatted_prompt = format_prompt(f"{system_instructions1}, {prompt_en}", history) + "[DORJGPT]" | |
| #stream = client.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>","") | |
| output_mn = get_translation(prompt, language="mn") | |
| #history.append([prompt_en, output]) | |
| communicate = edge_tts.Communicate(output_mn, voice="mn-MN-BataaNeural") | |
| 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, | |
| visible=False, | |
| autoplay=True, | |
| elem_classes="audio") | |
| user_input = gr.Textbox(label="Question", value="What is this application?") | |
| with gr.Tab(): | |
| with gr.Row(): | |
| translate_btn = gr.Button("Submit") | |
| translate_btn.click(fn=generate1, inputs=user_input, | |
| outputs=output_audio, api_name="translate") | |
| if __name__ == "__main__": | |
| demo.queue(max_size=30).launch() |