from langchain.document_loaders.unstructured import UnstructuredFileLoader 
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage, SystemMessage, Document
from langchain.document_loaders import PyPDFLoader

from transformers import AutoTokenizer, T5ForConditionalGeneration
from retrieval.retrieval import Retrieval, BM25
from datetime import datetime
import os, time, torch
from torch.nn import Softmax
import requests

API_URL = "https://api-inference.huggingface.co/models/CreatorPhan/ViQA-small"
headers = {"Authorization": "Bearer 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"}

class Agent:
    def __init__(self, args=None) -> None:
        self.args = args
        self.choices = args.choices
        self.corpus = Retrieval(k=args.choices)
        
        self.context_value = ""
        self.use_context = False
        self.softmax = Softmax(dim=1)
        self.temp = []
        self.replace_list = torch.load('retrieval/replace.pt')
        
        print("Model is loading...")
        self.model = T5ForConditionalGeneration.from_pretrained(args.model).to(args.device)
        self.tokenizer = AutoTokenizer.from_pretrained(args.tokenizer)
        print("Model loaded!")


    def load_context(self, doc_path):
        print('Loading file:', doc_path.name)
        if doc_path.name[-4:] == '.pdf':
            context = self.read_pdf(doc_path.name)
        else:
            # loader = UnstructuredFileLoader(doc_path.name)
            context = open(doc_path.name, encoding='utf-8').read()
        
        self.retrieval = Retrieval(docs=context)
        self.choices = self.retrieval.k
        self.use_context = True

        return f"Using file from {doc_path.name}"
    
    def API_call(self, prompt):
        response = requests.post(API_URL, headers=headers, json={"inputs": prompt}).json()
        if isinstance(response, list):
            return response[0]['generated_text']
        else:
            time.sleep(3)
            return self.API_call(prompt)

    def asking(self, question):
        timestamp = datetime.now()
        timestamp = timestamp.strftime("[%Y-%m-%d %H:%M:%S]")
        print(timestamp, end=' ')

        s_query = time.time()
        if self.use_context:
            print("Answering with your context:", question)
            contexts = self.retrieval.get_context(question)
        else:
            print("Answering without your context:", question)
            contexts = self.corpus.get_context(question)

        prompts = []
        for context in contexts:
            prompt = f"Trả lời câu hỏi: {question} Trong nội dung: {context['context']}"
            prompts.append(prompt)

        s_token = time.time()
        tokens = self.tokenizer(prompts, max_length=self.args.seq_len, truncation=True, padding='max_length', return_tensors='pt')
        
        s_gen = time.time()
        outputs = self.model.generate(
            input_ids=tokens.input_ids.to(self.args.device),
            attention_mask=tokens.attention_mask.to(self.args.device),
            max_new_tokens=self.args.out_len,
            output_scores=True,
            return_dict_in_generate=True
        )


        s_de = time.time()
        results = []

        scores = self.softmax(outputs.scores[0])
        scores = scores.max(dim=1).values*100
        # print(scores)
        for i in range(self.choices):
            result = contexts[i]
            score = round(scores[i].item())
            result['score'] = score
            
            answer = self.tokenizer.decode(outputs.sequences[i], skip_special_tokens=True)
            result['answer'] = answer
            results.append(result)

        def get_score(record):
            return record['score']**2 * record['score_bm']

        results.sort(key=get_score, reverse=True)

        self.temp = results
        t_mess = "t_query: {:.2f}\t t_token: {:.2f}\t t_gen: {:.2f}\t t_decode: {:.2f}\t".format(
            s_token-s_query, s_gen-s_token, s_de-s_gen, time.time()-s_de
        )
        print(t_mess, len(self.temp))
        if results[0]['score'] > 60:
            return results[0]['answer']
        else:
            return f"Tôi không chắc nhưng câu trả lời có thể là: {results[0]['answer']}\nBạn có thể tham khảo các câu trả lời bên cạnh!"


    
    def get_context(self, context):
        self.context_value = context

        self.retrieval = Retrieval(k=self.choices, docs=context)
        self.choices = self.retrieval.k
        self.use_context = True
        return context
    
    def load_context_file(self, file):
        print('Loading file:', file.name)
        text = ''
        for line in open(file.name, 'r', encoding='utf8'):
            text += line

        self.context_value = text
        return text
    
    def clear_context(self):
        self.context_value = ""
        self.use_context = False
        self.choices = self.args.choices
        return ""

    def replace(self, text):
        for key, value in self.replace_list:
            text = text.replace(key, value)
        return text

    def read_pdf(self, file_path):
        loader = PyPDFLoader(file_path)
        pages = loader.load_and_split()
        text = ''
        for page in pages:
            page_content = page.page_content
            text += self.replace(page_content)

        return text