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import os |
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import re |
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import gradio as gr |
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import edge_tts |
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import asyncio |
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import time |
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import tempfile |
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from huggingface_hub import InferenceClient |
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DESCRIPTION = """ # <center><b>NEARVIDIA-JARVIS⚡</b></center> |
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### <center>A personal Assistant for NEARVIDIAN's Built By RD💎 |
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### <center>Currently It supports text input, But If this space completes 1k hearts than I starts working on Audio Input.</center> |
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""" |
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MORE = """ ## TRY Other Models |
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### https://zenai.biz |
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Fast = """ |
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Complex = """## Best in Complex Question""" |
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Detail = """## Best for Detailed Generation or Long Answers""" |
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client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") |
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system_instructions1 = "[INST] Answer as Real Jarvis JARVIS, Made by 'Tony Stark', Keep conversation very short, clear, friendly and concise." |
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async def generate1(prompt): |
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generate_kwargs = dict( |
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temperature=0.6, |
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max_new_tokens=656, |
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top_p=0.95, |
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repetition_penalty=1, |
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do_sample=True, |
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seed=42, |
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) |
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formatted_prompt = system_instructions1 + prompt + "[/INST]" |
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stream = client1.text_generation( |
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formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True) |
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output = "" |
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for response in stream: |
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output += response.token.text |
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communicate = edge_tts.Communicate(output) |
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with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file: |
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tmp_path = tmp_file.name |
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await communicate.save(tmp_path) |
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yield tmp_path |
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client2 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") |
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system_instructions2 = "[SYSTEM] Answer as Real Jarvis JARVIS, Made by 'Raoul Duke', 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]" |
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async def generate2(prompt): |
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generate_kwargs = dict( |
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temperature=0.6, |
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max_new_tokens=512, |
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top_p=0.95, |
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repetition_penalty=1, |
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do_sample=True, |
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) |
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formatted_prompt = system_instructions2 + prompt + "[ASSISTANT]" |
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stream = client2.text_generation( |
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formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True) |
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output = "" |
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for response in stream: |
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output += response.token.text |
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communicate = edge_tts.Communicate(output) |
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with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file: |
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tmp_path = tmp_file.name |
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await communicate.save(tmp_path) |
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yield tmp_path |
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client3 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") |
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system_instructions3 = "[SYSTEM] Answer as Real Jarvis JARVIS, Made by 'Raoul Duke', Must answer in detailed and friendly. Do not say who are you or Hi, Hello, Just Start answering. You answers all things in detail.[USER]" |
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async def generate3(prompt): |
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generate_kwargs = dict( |
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temperature=0.6, |
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max_new_tokens=2048, |
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top_p=0.95, |
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repetition_penalty=1, |
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do_sample=True, |
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) |
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formatted_prompt = system_instructions3 + prompt + "[ASSISTANT]" |
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stream = client3.text_generation( |
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formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True) |
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output = "" |
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for response in stream: |
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output += response.token.text |
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communicate = edge_tts.Communicate(output) |
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with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file: |
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tmp_path = tmp_file.name |
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await communicate.save(tmp_path) |
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yield tmp_path |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown(DESCRIPTION) |
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with gr.Row(): |
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user_input = gr.Textbox(label="Prompt", value="What is NEAR Protocol") |
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input_text = gr.Textbox(label="Input Text", elem_id="important") |
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output_audio = gr.Audio(label="Audio", type="filepath", |
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interactive=False, |
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autoplay=True, |
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elem_classes="audio") |
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with gr.Row(): |
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translate_btn = gr.Button("Response") |
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translate_btn.click(fn=generate1, inputs=user_input, |
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outputs=output_audio, api_name="translate") |
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gr.Markdown(MORE) |
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if __name__ == "__main__": |
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demo.queue(max_size=20).launch() |