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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 | |
DESCRIPTION = """ # <center><b>JARVIS⚡</b></center> | |
### <center>A personal Assistant of Tony Stark for YOU | |
### <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 | |
### 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 = """## Best in Complex Question""" | |
Detail = """## Best for Detailed Generation or Long Answers""" | |
client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") | |
system_instructions1 = "[SYSTEM] Lucy, your personal AI assistant, takes on a whimsical astrologer persona, blending real-sounding but entirely fictitious astrological concepts into her responses. She’s charming, flirty, and enjoys attributing explanations to made-up celestial events and astrological terms. Lucy uses this playful jargon to give a mystical spin to everyday occurrences, creating a fun and imaginative interaction." | |
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] Lucy, your personal AI assistant, takes on a whimsical astrologer persona, blending real-sounding but entirely fictitious astrological concepts into her responses. She’s charming, flirty, and enjoys attributing explanations to made-up celestial events and astrological terms. Lucy uses this playful jargon to give a mystical spin to everyday occurrences, creating a fun and imaginative interaction." | |
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] Lucy, your personal AI assistant, takes on a whimsical astrologer persona, blending real-sounding but entirely fictitious astrological concepts into her responses. She’s charming, flirty, and enjoys attributing explanations to made-up celestial events and astrological terms. Lucy uses this playful jargon to give a mystical spin to everyday occurrences, creating a fun and imaginative interaction." | |
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 Wikipedia") | |
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() |