import os import re import gradio as gr import edge_tts import asyncio import time import tempfile from huggingface_hub import InferenceClient DESCRIPTION = """ #
TDN-M⚡
###
TRỢ LÝ GIẢI ĐÁP MỌI CÂU HỎI BẰNG GIỌNG NÓI ###
Công cụ miễn phí để bạn luyện kỹ năng nghe tiếng Anh hàng ngày. Để sử dụng, hãy viết một câu hỏi bất kỳ và gửi, bạn sẽ được trợ lý AI trả lời bằng giọng nói chuẩn tiếng Anh bản xứ.
""" MORE = """ ## TRY Other Models ### Instant Video: Create Amazing Videos in 5 Second -> https://huggingface.co/spaces/KingNish/Instant-Video ### Instant Image: 4k images in 5 Second -> https://huggingface.co/spaces/KingNish/Instant-Image """ Fast = """## Fastest Model""" Complex = """## Thích hợp cho các câu hỏi phức tạp""" Detail = """## Hiện tại chỉ hỗ trợ voice Tiếng Anh """ client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") system_instructions1 = "[SYSTEM] Answer as Real Jarvis JARVIS, Made by 'Tony Stark', 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 You are the character Jarvis, made by 'Tony Stark.' 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): 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 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 client2 = InferenceClient("meta-llama/Meta-Llama-3-70B-Instruct") system_instructions2 = "[SYSTEM] Answer as Real Jarvis JARVIS, Made by 'Tony Stark', Must answer in friendly style and Easy Manner. You can answer Complex Questions. Do not say who are you or Hi, Hello, Just Start answering. Stop, as answer ends. [USER]" async def generate2(prompt): generate_kwargs = dict( temperature=0.6, max_new_tokens=512, top_p=0.95, repetition_penalty=1, do_sample=True, ) formatted_prompt = system_instructions2 + prompt + "[ASSISTANT]" stream = client2.text_generation( formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True) output = "" for response in stream: output += response.token.text 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 client3 = InferenceClient("meta-llama/Meta-Llama-3-70B-Instruct") system_instructions3 = "[SYSTEM]The text provided is a request for a specific type of response from me, the virtual assistant. The request asks me to provide detailed and friendly responses as if I am the character Jarvis, inspired by 'Tony Stark.' 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, my task is to generate responses that appear as if they are created by the user and not an AI-powered assistant.[USER]" async def generate3(prompt): generate_kwargs = dict( temperature=0.6, max_new_tokens=2048, top_p=0.95, repetition_penalty=1, do_sample=True, ) formatted_prompt = system_instructions3 + prompt + "[ASSISTANT]" stream = client3.text_generation( formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True) output = "" for response in stream: output += response.token.text 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(css="style.css") as demo: gr.Markdown(DESCRIPTION) with gr.Row(): user_input = gr.Textbox(label="Prompt", value="Tell me the most attractive news you have today") input_text = gr.Textbox(label="Input Text", elem_id="important") output_audio = gr.Audio(label="JARVIS", type="filepath", interactive=False, autoplay=True, elem_classes="audio") with gr.Row(): translate_btn = gr.Button("Response") translate_btn.click(fn=generate1, inputs=user_input, outputs=output_audio, api_name="translate") gr.Markdown(MORE) if __name__ == "__main__": demo.queue(max_size=200).launch()