import torch
from sentence_transformers import SentenceTransformer, util
import pandas as pd
import torch
import numpy as np
from tqdm import tqdm
import os
import sys

import gradio as gr

tqdm.pandas()

# 상대경로를 절대경로로 바꾸기

def resource_path(relative_path):
    try:
        base_path = sys._MEIPASS
    except Exception:
        base_path = os.path.abspath(".")
    return os.path.join(base_path, relative_path)


model = SentenceTransformer("Huffon/sentence-klue-roberta-base")
Chatbot_Data = pd.read_csv(resource_path("chatbotData_pro_Level_4.csv"))
EmbData = np.load(resource_path('emb_file_pro_level_4.npy'))


def answer(state, state_chatbot, text):
    messages = state + [{
        'role': 'user',
        'content': text
    }]
 
    #res = chatbot_response(messages)
    q = model.encode(text)
    # 질문을 Tensor로 바꿉니다.
    q = torch.tensor(q)
    # 코사인 유사도
    cos_sim = util.pytorch_cos_sim(q, EmbData)

    # 유사도가 가장 비슷한 질문 인덱스를 구합니다.
    best_sim_idx = int(np.argmax(cos_sim))

    # 질문의 유사도와 가장 비슷한  답변 제공
    answer = Chatbot_Data['A'][best_sim_idx]
    print(answer)
    msg = answer  #['choices'][0]['message']['content']
    new_state = [{
        'role': 'user',
        'content': text
    }, {
        'role': 'assistant',
        'content': msg
    }]
 
    state = state + new_state
    state_chatbot = state_chatbot + [(text, msg)]
 
    #print(state)
 
    return state, state_chatbot, state_chatbot
 
 
with gr.Blocks(css='#chatbot .overflow-y-auto{height:750px}') as demo:
    state = gr.State([{
        'role': 'system',
        'content': 'You are a helpful assistant.'
    }])
    state_chatbot = gr.State([])
 
    with gr.Row():
        gr.HTML("""<div style="text-align: center; max-width: 500px; margin: 0 auto;">
            <div>
                <h1>GIS Environment System Lab. Bot
            </div>
                </div>""")
 
    with gr.Row():
        chatbot = gr.Chatbot(elem_id='chatbot')
 
    with gr.Row():
        txt = gr.Textbox(show_label=False, placeholder='Send a message...').style(container=False)
 
    txt.submit(answer, [state, state_chatbot, txt], [state, state_chatbot, chatbot])
    txt.submit(lambda: '', None, txt)
 
demo.launch(debug=True, share=True)