import openai

import time

# from elevenlabs import set_api_key

import requests
import whisper


import os


# from elevenlabs import generate, play , stream , save


import gradio as gr


from typing import Optional


from deta import Deta



deta = Deta("d0uj3nfpeok_kuCKpWGv4jeHCqKZVANdW8z37ksTJzUZ")



db1 = deta.Base("elevenlab")



openai.api_key = os.getenv("openaiapikey")


model = whisper.load_model("base")

os.environ['PATH'] += os.pathsep + '/path/to/mpv/'


class Chat:

    def __init__(self , system: Optional[str] = None):
        self.system = system
        self.messages = []

        if system is not None:
            self.messages.append({
                "role": "system",
                "content": system
            })

    def prompt(self, content: str) -> str:
        self.messages.append({
            "role": "user",
            "content": content
        })
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=self.messages
        )
        response_content = response["choices"][0]["message"]["content"]
        self.messages.append({
            "role": "assistant",
            "content": response_content
        })
        return response_content



def read_and_print_file(file_path):
    with open(file_path, 'r') as file:
        return  file.read()


# Example usage:
file_path = 'the_interview_questions'
contents = read_and_print_file(file_path)


chat = Chat(system="""You are a helpfull assistant
""")


from gradio_client import Client
API_URL = "https://sanchit-gandhi-whisper-jax.hf.space/"

# set up the Gradio client
client = Client(API_URL)


def transcribe_audio(audio_path, task="transcribe", return_timestamps=False):
    """Function to transcribe an audio file using the Whisper JAX endpoint."""
    if task not in ["transcribe", "translate"]:
        raise ValueError("task should be one of 'transcribe' or 'translate'.")

    text, runtime = client.predict(
        audio_path,
        task,
        return_timestamps,
        api_name="/predict_1",
    )
    return text
    

def convert_tuples_to_list(tuples_list):
    result = []
    conversation = ""
    for tuple_item in tuples_list:
        result.append(tuple_item[0])  # Append question
        result.append(tuple_item[1])  # Append answer
    for i in result:
        conversation = conversation + i + " \n"
    return conversation


def run_text_prompt(message, chat_history):
    bot_message = chat.prompt(content=message)
    
    db1.update(
        {"value": bot_message},
        "my-key",
    )
    # audio = generate(
    #     text=bot_message,
    #     voice="Antoni",
    # )

    # 
    # (audio)
    # save(audio, 'myvoice.mp3')
    
    chat_history.append((message, bot_message))

    return "", chat_history


def run_audio_prompt(audio, chat_history):
    if audio is None:
        return None, chat_history
    
    message_transcription = transcribe_audio(audio)


    # message_transcription = model.transcribe(audio)["text"]
    _, chat_history = run_text_prompt(message_transcription, chat_history)
    return None, chat_history

def process_text(conversation, text):
  # print(text)
  completion = openai.ChatCompletion.create(model="gpt-3.5-turbo",
                                            messages=[{
                                              "role":
                                              "user",
                                              "content":
                                              conversation + text
                                            }])
  # completion = text;
  # print(completion)
  print(completion.choices[0].message.content.strip())
  return completion.choices[0].message.content.strip()

with gr.Blocks(title="hi") as app2:
    chatbot = gr.Chatbot(label="Agent")

    msg = gr.Textbox(label="Write")
    msg.submit(run_text_prompt, [msg, chatbot], [msg, chatbot])

    with gr.Row():
        audio = gr.Audio(source="microphone", type="filepath", label="Speak")

        fn = run_audio_prompt,
        inputs = [audio, chatbot],
        outputs = [audio, chatbot]


        # fn = run_audio_prompt,
        # inputs = [audio, chatbot],
        # outputs = [audio, chatbot]
        audio.change(run_audio_prompt, [audio, chatbot], [audio, chatbot])
        # send_audio_button = gr.Button("Send Audio", interactive=True)
        # send_audio_button.click(run_audio_prompt, [audio, chatbot], [audio, chatbot])

demo = gr.TabbedInterface([app2], [ "Interview"])

demo.launch(share=False,
                        debug=False,
                         )