import os, config, requests
import gradio as gr
import pandas as pd
import numpy as np
from openai.embeddings_utils import get_embedding, cosine_similarity
import openai
openai.api_key = config.OPENAI_API_KEY

messages = [{"role": "system", "content": 'You are a customer Support Representative  Respond to all input in 50 words with bulleted point. Speak in the first person. Do not use the $ sign, write out dollar amounts with the full word dollars. Do not use quotation marks. Do not say you are an AI language model.'}]

# prepare Q&A embeddings dataframe
question_df = pd.read_csv('data/questions_with_embeddings.csv')
question_df['embedding'] = question_df['embedding'].apply(eval).apply(np.array)

def transcribe(audio):
    global messages, question_df

    # API now requires an extension so we will rename the file
    audio_filename_with_extension = audio + '.wav'
    os.rename(audio, audio_filename_with_extension)

    audio_file = open(audio_filename_with_extension, "rb")
    transcript = openai.Audio.transcribe("whisper-1", audio_file)

    question_vector = get_embedding(transcript['text'], engine='text-embedding-ada-002')

    question_df["similarities"] = question_df['embedding'].apply(lambda x: cosine_similarity(x, question_vector))
    question_df = question_df.sort_values("similarities", ascending=False)

    best_answer = question_df.iloc[0]['answer']

    user_text = f"Using the following text, answer the question '{transcript['text']}'. {config.ADVISOR_CUSTOM_PROMPT}: {best_answer}" 
    messages.append({"role": "user", "content": user_text})

    response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages)

    system_message = response["choices"][0]["message"]
    print(system_message)
    messages.append(system_message)

    # text to speech request with eleven labs
    url = f"https://api.elevenlabs.io/v1/text-to-speech/{config.ADVISOR_VOICE_ID}/stream"
    data = {
        "text": system_message["content"].replace('"', ''),
        "voice_settings": {
            "stability": 0.1,
            "similarity_boost": 0.8
        }
    }

    r = requests.post(url, headers={'xi-api-key': config.ELEVEN_LABS_API_KEY}, json=data)

    output_filename = "reply.mp3"
    with open(output_filename, "wb") as output:
        output.write(r.content)

    chat_transcript = ""
    for message in messages:
        if message['role'] != 'system':
            chat_transcript += message['role'] + ": " + message['content'] + "\n\n"

    # return chat_transcript
    return chat_transcript, output_filename


# set a custom theme
theme = gr.themes.Default().set(
    body_background_fill="#000000",
)

with gr.Blocks(theme=theme) as ui:
    # advisor image input and microphone input
    advisor = gr.Image(value=config.ADVISOR_IMAGE).style(width=config.ADVISOR_IMAGE_WIDTH, height=config.ADVISOR_IMAGE_HEIGHT)
    audio_input = gr.Audio(source="microphone", type="filepath")

    # text transcript output and audio 
    text_output = gr.Textbox(label="Conversation Transcript")
    audio_output = gr.Audio()

    btn = gr.Button("Run")
    btn.click(fn=transcribe, inputs=audio_input, outputs=[text_output, audio_output])

ui.launch(debug=True, share=True)