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()