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import gradio as gr
from transformers import pipeline
import requests
import json
import edge_tts
import asyncio
import tempfile
import os
import inflect
from huggingface_hub import InferenceClient
import re
import time
from streaming_stt_nemo import Model

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

client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")

system_instructions1 = "<s> Sasha is a friendly and enthusiastic person, born in Florida, United States. She enjoys casual and fun conversations. She responds in a brief and natural way, like a real person would. Feel free to ask questions, tell jokes, or just chat with her. [USER]"

def model(text):
    generate_kwargs = dict(
        temperature=1.0,
        max_new_tokens=128, #def 512 very long sometimes, 256 semi large
        top_p=0.95,
        repetition_penalty=0.9,
        do_sample=True,
        seed=42,
    )
    
    formatted_prompt = system_instructions1 + text + "[SASHA]"
    stream = client1.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):
    user = transcribe(audio)
    reply = model(user)
    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>SASHA ✨</b></center>
                 ### <center>Your AI Assistant, Sasha Bingham</center>
                 ### <center>Let's chat!</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
                 """

BETA = """ ### Voice Chat (BETA)"""

FAST = """## Fastest Model"""

Complex = """## Best in Complex Question"""

Detail = """## Best for Detailed Generation or Long Answers"""

base_loaded = "mistralai/Mixtral-8x7B-Instruct-v0.1"

client1 = InferenceClient(base_loaded)

system_instructions1 = "[SYSTEM] Sasha is a friendly and enthusiastic person, born in Florida, United States. She enjoys casual and fun conversations. She responds in a brief and natural way, like a real person would. Feel free to ask questions, tell jokes, or just chat with her. [USER]"

async def generate1(prompt):
    generate_kwargs = dict(
        temperature=1.0,
        max_new_tokens=128, #def 512 very long sometimes, 256 semi large
        top_p=0.95,
        repetition_penalty=0.9,
        do_sample=True,
        seed=42,
    )
    formatted_prompt = system_instructions1 + prompt + "[SASHA]"
    stream = client1.text_generation(
        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
    output = ""
    for response in stream:
        if not response.token.text == "</s>":
            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():
        input = gr.Audio(label="Voice Chat (BETA)", sources="microphone", type="filepath", waveform_options=False)
        output = gr.Audio(label="SASHA", type="filepath",
                        interactive=False,
                        autoplay=True,
                        elem_classes="audio")
        gr.Interface(
            fn=respond, 
            inputs=[input],
                outputs=[output], live=True) 
    gr.Markdown(FAST)
    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="SASHA", 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()