File size: 4,296 Bytes
2f3b32c 5b7c271 d7fcc73 2f3b32c ab0e126 5b7c271 ab0e126 2f3b32c ab0e126 2f3b32c ab0e126 2f3b32c 5b7c271 2f3b32c ab0e126 79a6033 ab0e126 79a6033 ab0e126 2f3b32c ab0e126 79a6033 ab0e126 288afe4 ab0e126 288afe4 2f3b32c 288afe4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
import os
import re
import gradio as gr
import edge_tts
import asyncio
import time
import tempfile
from huggingface_hub import InferenceClient
DESCRIPTION = """ # <center><b>NEARVIDIA-JARVIS⚡</b></center>
### <center>A personal Assistant for NEARVIDIAN's Built By RD💎
### <center>Currently It supports text input, But If this space completes 1k hearts than I starts working on Audio Input.</center>
"""
MORE = """ ## TRY Other Models
### https://zenai.biz
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 = "[INST] Answer as Real Jarvis JARVIS, Made by 'Tony Stark', Keep conversation very short, clear, friendly and concise."
async def generate1(prompt):
generate_kwargs = dict(
temperature=0.6,
max_new_tokens=656,
top_p=0.95,
repetition_penalty=1,
do_sample=True,
seed=42,
)
formatted_prompt = system_instructions1 + prompt + "[/INST]"
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("mistralai/Mixtral-8x7B-Instruct-v0.1")
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]"
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("mistralai/Mixtral-8x7B-Instruct-v0.1")
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]"
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="What is NEAR Protocol")
input_text = gr.Textbox(label="Input Text", elem_id="important")
output_audio = gr.Audio(label="Audio", 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=20).launch() |