from engine.speech2text import Speech2Text
from engine.ner_engine import NERInfer
from engine.audio_chunk import AudioChunk
import glob
import time
import gradio as gr
import os

audio_chunk = AudioChunk()
speech2text = Speech2Text()
ner_tag = NERInfer()

text = ""
def streaming_process(streaming_audio_file) -> str:
    global text 
    text = speech2text.process_streaming(streaming_audio_file)
    # return text

def output_streaming(text_streaming,text01)-> str:
    text_streaming+=text01
    return text_streaming

def recorded_process(recorded_audio_file) -> str:
    """ 
    to get both input 
    and use speech2text for get text
    """
    text = speech2text.process_microphone(recorded_audio_file)
    result = ner_tag.infer(text)
    return {"text": text, "entities": result}


def uploaded_process(uploaded_audio_file) -> str:
    audio_chunk.chunks_audio(uploaded_audio_file)
    text = speech2text.process_audio()
    result = ner_tag.infer(text)      
    return {"text": text, "entities": result}


def clear_inputs_and_outputs() -> list:
    """
    Clears all inputs and outputs when the user clicks "Clear" button
    """
    audio_chunk.remove_chunk()
    return [None, None, None, None]

text_streaming = ""

with gr.Blocks() as demo:
    """
    buld gradio app

    """
    gr.Markdown(
        """
        ![logo](/file=./image/clicknext_logo2x.png)

        # Automatic Speech Recognition
         
        #####  Experience real-time, accurate, and multilingual speech-to-text conversion with our cutting-edge ASR technology.
        """
    )

    with gr.Tab("Record File"):
        with gr.Row():  
            with gr.Column():
                mic_input = gr.Microphone( type="filepath",label="Record voice")
                with gr.Row():
                    clr_btn = gr.Button(value="Clear", variant="secondary")
                    sub_btn = gr.Button(value="submit")
            with gr.Column():
                lbl_output = gr.HighlightedText(label="Result")

            clr_btn.click(
                fn=clear_inputs_and_outputs,
                    inputs=[],
                    outputs=[mic_input, lbl_output]
            )

            sub_btn.click(
                fn=recorded_process,
                inputs=[mic_input],
                outputs=[lbl_output]
            )

    with gr.Tab("streaming"):
        gr.Interface(
                    fn=streaming_process,
                    inputs=[
                        gr.Microphone(type="filepath", streaming=True)],
                    outputs=[
                    #     # gr.HighlightedText(label="Result"),
                        gr.Textbox(type ="text", label="Result",)],
                    live=True,
                    allow_flagging="never"
                    )
        with gr.Row():  
            with gr.Column():
                print(text)
                text_streaming = output_streaming(text_streaming,text)
                gr.Textbox(value=text, label="Result", autofocus=True)

    with gr.Tab("Upload File"):
        with gr.Row():  
            with gr.Column():
                upl_input = gr.Audio( type="filepath", label="Upload a file")   
                with gr.Row():
                        clr_btn = gr.Button(value="Clear", variant="secondary")
                        sub_btn = gr.Button(value="submit")
                gr.Examples(examples=[
                    os.path.join(os.path.dirname(__file__),"examples/politics.mp3"),
                    os.path.join(os.path.dirname(__file__),"examples/law1.mp3"),
                    os.path.join(os.path.dirname(__file__),"examples/law2.mp3"),
                    os.path.join(os.path.dirname(__file__),"examples/law3.mp3"),
                    os.path.join(os.path.dirname(__file__),"examples/economy.mp3"),
                    os.path.join(os.path.dirname(__file__),"examples/general.mp3")
                    ],
                    inputs = upl_input)
            with gr.Column():
                lbl_output = gr.HighlightedText(label="Result")

            clr_btn.click(
                fn=clear_inputs_and_outputs,
                inputs=[],
                outputs=[upl_input, lbl_output]
            )
            sub_btn.click(
                fn=uploaded_process,
                inputs=[upl_input],
                outputs=[lbl_output]
            )

demo.launch(favicon_path = "./image/fe_logo.png", server_name="0.0.0.0", server_port=8085)