import os
import gradio as gr
import pandas as pd
import ast
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
from openai import OpenAI, ChatCompletion

# Получаем значения OPEN_API_KEY, login, и password
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
login = os.environ.get("login")
password = os.environ.get("password")

# Инициализируем OpenAI API клиент
client = OpenAI(api_key=OPENAI_API_KEY)

# Загружаем данные embeddings
data = pd.read_csv("embeddings.csv")
data["embedding"] = data["embedding"].apply(ast.literal_eval)

# Элементы Gradio интерфейса
model_dropdown = gr.Dropdown(choices=["gpt-3.5-turbo", "gpt-4-1106-preview"], label="Select Model")
temperature_slider = gr.Slider(minimum=0, maximum=1.0, value=0, label="Temperature")
top_p_slider = gr.Slider(minimum=0.01, maximum=1.0, value=1, label="Top P")
textbox_input = gr.Textbox(label="Enter text here")

# Функция поиска отзывов
def search_reviews(df_original, product_description, without_newlines=False, n=1):
    df = df_original.copy()
    if without_newlines:
        product_description = product_description.replace("\n", " ")

    embedding = (
        client.embeddings.create(
            input=[product_description], model="text-embedding-ada-002"
        )
        .data[0]
        .embedding
    )
    df["similarities"] = df["embedding"].apply(
        lambda x: cosine_similarity(
            np.array(x).reshape(1, -1), np.array(embedding).reshape(1, -1)
        )[0][0]
    )
    res = df.sort_values("similarities", ascending=False).head(n)
    return res.reset_index(drop=True)

# Функция генерации ответа
def generate_response(text, model, temperature, top_p):
    reference = search_reviews(data, text, without_newlines=True)["Content"][0]

    completion = client.chat.completions.create(
        model=model,
        temperature=temperature,
        top_p=top_p,
        messages=[
            {
                "role": "system",
                "content": f"Generate high-quality rewritten articles, ensuring logical composition, avoiding over-exaggeration, and refraining from any imaginative elements. Use the provided sample text as a reference for the desired writing style:{reference}",
            },
            {"role": "user", "content": f"{text}"},
        ],
    )
    result = completion.choices[0].message.content
    return result

# Создаём интерфейс Gradio
iface = gr.Interface(
    fn=generate_response,
    inputs=[textbox_input, model_dropdown, temperature_slider, top_p_slider],
    outputs='text'
)

# Запускаем интерфейс Gradio
iface.launch(auth=(login, password))