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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
class JarvisModels:
def __init__(self):
self.client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
self.client2 = InferenceClient("meta-llama/Meta-Llama-3-70B-Instruct")
self.client3 = InferenceClient("meta-llama/Meta-Llama-3-70B-Instruct")
async def generate_model1(self, 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 = self.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
async def generate_model2(self, 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 = self.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
async def generate_model3(self, 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 = self.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
class JarvisApp:
def __init__(self):
self.models = JarvisModels()
def launch_app(self):
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=self.models.generate_model1, inputs=user_input,
outputs=output_audio, api_name="translate")
gr.Markdown(MORE)
if __name__ == "__main__":
demo.queue(max_size=200).launch()
if __name__ == "__main__":
app = JarvisApp()
app.launch_app()
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