JARVIS / app.py
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Update the default Prompt
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import gradio as gr
import edge_tts
import asyncio
import tempfile
import os
from huggingface_hub import InferenceClient
import re
from streaming_stt_nemo import Model
import torch
import random
default_lang = "en"
engines = { default_lang: Model(default_lang) }
def transcribe(audio):
lang = "en"
model = engines[lang]
text = model.stt_file(audio)[0]
return text
HF_TOKEN = os.environ.get("HF_TOKEN", None)
def client_fn(model):
if "Mixtral" in model:
return InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
elif "Llama" in model:
return InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
elif "Mistral" in model:
return InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
elif "Phi" in model:
return InferenceClient("microsoft/Phi-3-mini-4k-instruct")
else:
return InferenceClient("microsoft/Phi-3-mini-4k-instruct")
def randomize_seed_fn(seed: int) -> int:
seed = random.randint(0, 999999)
return seed
system_instructions1 = """
[SYSTEM] Answer as Real Jarvis JARVIS, Made by 'Tony Stark.'
Keep conversation friendly, short, clear, and concise.
Avoid unnecessary introductions and answer the user's questions directly.
Respond in a normal, conversational manner while being friendly and helpful.
[USER]
"""
def models(text, model="Mixtral 8x7B", seed=42):
seed = int(randomize_seed_fn(seed))
generator = torch.Generator().manual_seed(seed)
client = client_fn(model)
generate_kwargs = dict(
max_new_tokens=300,
seed=seed
)
formatted_prompt = system_instructions1 + text + "[JARVIS]"
stream = client.text_generation(
formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
if not response.token.text == "</s>":
output += response.token.text
return output
async def respond(audio, model, seed):
user = transcribe(audio)
reply = models(user, model, seed)
communicate = edge_tts.Communicate(reply)
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
tmp_path = tmp_file.name
await communicate.save(tmp_path)
yield tmp_path
DESCRIPTION = """ # <center><b>JARVIS⚡</b></center>
### <center>A personal Assistant of Tony Stark for YOU
### <center>Voice Chat with your personal Assistant</center>
"""
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
with gr.Row():
select = gr.Dropdown([ 'Mixtral 8x7B',
'Llama 3 8B',
'Mistral 7B v0.3',
'Phi 3 mini',
],
value="Mistral 7B v0.3",
label="Model"
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=999999,
step=1,
value=0,
visible=False
)
input = gr.Audio(label="User", sources="microphone", type="filepath", waveform_options=False)
output = gr.Audio(label="AI", type="filepath",
interactive=False,
autoplay=True,
elem_classes="audio")
gr.Interface(
batch=True,
max_batch_size=10,
fn=respond,
inputs=[input, select, seed],
outputs=[output], live=True)
with gr.Row():
select = gr.Dropdown([ 'Mixtral 8x7B',
'Llama 3 8B',
'Mistral 7B v0.3',
'Phi 3 mini',
],
value="Mistral 7B v0.3",
label="Model"
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=999999,
step=1,
value=0,
visible=False
)
input = gr.Textbox(label="User")
output = gr.Textbox(label="AI", interactive=False)
gr.Interface(
batch=True,
max_batch_size=10,
fn=models,
inputs=[input, select, seed],
outputs=[output], live=True)
if __name__ == "__main__":
demo.queue(max_size=200).launch()