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Browse files- app.py +56 -0
- requirements.txt +8 -0
- style.css +3 -0
app.py
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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>JARVIS⚡</b></center>
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### <center>A personal Assistant of Tony Stark for YOU
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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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client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
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system_instructions = "[INST] Answer as Real Jarvis JARVIS, Made by 'Tony Stark', Keep conversation very short, clear, friendly and concise."
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async def generate(prompt):
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generate_kwargs = dict(
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temperature=0.6,
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max_new_tokens=100,
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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_instructions + prompt + "[/INST]"
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stream = client.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")
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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=generate, inputs=user_input,
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outputs=output_audio, api_name="translate")
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if __name__ == "__main__":
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demo.queue(max_size=20).launch()
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requirements.txt
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edge-tts
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gradio
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asyncio
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transformers
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torch
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audiosegment
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scipy
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librosa
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style.css
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#important{
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display: none;
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}
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