import time
import gradio as gr
from transformers import pipeline
import numpy as np
from openai import OpenAI

transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
qa_model = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")

def predict(message, history, api_key, is_paused):
    client = OpenAI(api_key=api_key)
    history_openai_format = []
    for human, assistant in history:
        history_openai_format.append({"role": "user", "content": human})
        history_openai_format.append({"role": "assistant", "content": assistant})
    history_openai_format.append({"role": "user", "content": message})

    response = client.chat.completions.create(
        model='gpt-4o',
        messages=history_openai_format,
        temperature=1.0,
        stream=True
    )

    partial_message = ""
    for chunk in response:
        print(is_paused)
        if is_paused[0]:  # Check if paused
            
            while is_paused[0]:
                print('paused')
                time.sleep(0.1)
        print('not paused')
        if chunk.choices[0].delta.content:
            partial_message += chunk.choices[0].delta.content
            yield partial_message

def chat_with_api_key(api_key, message, history, is_paused):
    accumulated_message = ""
    for partial_message in predict(message, history, api_key, is_paused):
        if is_paused[0]:  # Check if paused
            break
        accumulated_message = partial_message
        history.append((message, accumulated_message))
        yield message, [[message, accumulated_message]]

def transcribe(audio):
    if audio is None:
        return "No audio recorded."
    sr, y = audio
    y = y.astype(np.float32)
    y /= np.max(np.abs(y))
    return transcriber({"sampling_rate": sr, "raw": y})["text"]

def answer(transcription):
    context = "You are a chatbot answering general questions"
    result = qa_model(question=transcription, context=context)
    return result['answer']

def process_audio(audio):
    if audio is None:
        return "No audio recorded.", []
    transcription = transcribe(audio)
    answer_result = answer(transcription)
    return transcription, [[transcription, answer_result]]

def update_output(api_key, audio_input, state, is_paused):
    if is_paused[0]:  # Check if paused
        yield "", state  # Return current state without making changes
    else:
        message = transcribe(audio_input)
        responses = chat_with_api_key(api_key, message, state, is_paused)
        accumulated_response = ""
        for response, updated_state in responses:
            if is_paused[0]:  # Check if paused
                break
            accumulated_response = response
            yield accumulated_response, updated_state

def clear_all():
    return None, "", []

def toggle_pause(is_paused):
    is_paused[0] = not is_paused[0]
    return is_paused

def update_button_label(is_paused):
    return "Resume" if is_paused[0] else "Pause"

with gr.Blocks() as demo:
    answer_output = gr.Chatbot(label="Answer Result")
    with gr.Row():    
        audio_input = gr.Audio(label="Audio Input", sources=["microphone"], type="numpy")
        with gr.Column():
            api_key = gr.Textbox(label="API Key", placeholder="Enter your API key", type="password")
            transcription_output = gr.Textbox(label="Transcription")
            clear_button = gr.Button("Clear")
            pause_button = gr.Button("Pause")
    
    state = gr.State([])
    is_paused = gr.State([False])  # Using a list to hold the mutable pause state

    audio_input.stop_recording(
        fn=update_output,
        inputs=[api_key, audio_input, state, is_paused],
        outputs=[transcription_output, answer_output]
    )
    
    clear_button.click(
        fn=clear_all,
        inputs=[],
        outputs=[audio_input, transcription_output, answer_output]
    )

    pause_button.click(
        fn=toggle_pause,
        inputs=[is_paused],
        outputs=[is_paused]
    ).then(
        fn=update_button_label,
        inputs=[is_paused],
        outputs=[pause_button]
    )

demo.launch()