import os
import gradio as gr
from dotenv import load_dotenv
import openai
from utils import compress
from google_manager.fassade import Fassade


from description import DESCRIPTION

# fastApi

import fastapi
import gradio as gr


load_dotenv()

# configuring openai package
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
openai.api_key = OPENAI_API_KEY


def load_prompt(path):
    with open(path) as f:
        lines = f.readlines()
        return "".join(lines)


def chat(passage, max_tokens=256, temprature=0, debug=False):

    if debug:
        passage = """
        A car or automobile is a motor vehicle with wheels. Most definitions of cars say that they run primarily on roads, seat one to eight people, have four wheels, and mainly transport people (rather than goods).
        """

    prompt = load_prompt("summary_prompt.txt").replace("<<SUMMARY>>", passage)

    summary = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": prompt}],
    )

    return summary["choices"][0]["message"]["content"].strip()


def transcribe(audio_file):
    audio_file = open(audio_file, "rb")
    transcription = openai.Audio.transcribe("whisper-1", audio_file, language="en")
    transcription = transcription["text"]
    return transcription


def predict(input, history=[]):
    compress(input)
    transcription = transcribe(input)
    answer = chat(transcription)

    # upload the input/answer to google drive
    doc_content = f"""
user:
{transcription}

summary:
{answer}
"""
    # Fassade.upload_to_drive(doc_content)

    history.append((transcription, answer))
    response = history
    return response, history


with gr.Blocks() as Ui:
    gr.Markdown(DESCRIPTION)
    chatbot = gr.Chatbot()
    state = gr.State([])

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

    audio_file.change(predict, [audio_file, state], [chatbot, state])