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import os

import gradio as gr
import requests
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, AutoModelForSeq2SeqLM
from langchain.text_splitter import CharacterTextSplitter
from langchain.indexes import VectorstoreIndexCreator
from langchain.document_loaders import TextLoader
import textwrap
from langchain.chains.question_answering import load_qa_chain
from langchain import HuggingFaceHub, HuggingFacePipeline, OpenAI
from sentence_transformers import SentenceTransformer

from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA



os.environ["OPENAI_API_KEY"] = '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'

class ChatGPT:
   def __init__(self):
       loaders = [TextLoader(os.path.join('./docs', fn), encoding='utf8') for fn in os.listdir('./docs')]
       # loader = TextLoader('./docs/test7.txt', encoding='utf-8')
       # loader = TextLoader('./state_of_the_union.txt', encoding='utf-8')
       # documents = loader.load()
       # print(wrap_text_preserve_newlines(str(documents[0])))
       text_splitter = CharacterTextSplitter(
           separator="\n",
           chunk_size=1000,
           chunk_overlap=0,
           length_function=len,
       )
       embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
       index = VectorstoreIndexCreator(
           embedding=embeddings,
           text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)).from_loaders(loaders)
       # self.docs = text_splitter.split_documents(documents)
       # print(len(docs))
       # embeddings = HuggingFaceEmbeddings(model_name='keepitreal/vietnamese-sbert')

       # embeddings = HuggingFaceEmbeddings()
       # llm = HuggingFaceHub(repo_id="vinai/phobert-base")
       # model = AutoModelForCausalLM.from_pretrained("vinai/phobert-base")
       # tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base")
       # self.db = FAISS.from_documents(self.docs, embeddings)
       # pipe = pipeline(
       #     "text-generation",
       #     model=model,
       #     tokenizer=tokenizer
       # )
       # local_llm = HuggingFacePipeline(pipeline=pipe)
       self.chain = RetrievalQA.from_chain_type(llm=OpenAI(temperature=0.0),
                                           chain_type="stuff",
                                           retriever=index.vectorstore.as_retriever(search_kwargs={"k": 3}),
                                           input_key="question")
       # self.chain = load_qa_chain(llm=OpenAI(temperature=0.0), chain_type="stuff")
       # self.chain = load_qa_chain(llm=local_llm, chain_type="stuff")

   def query(self,question):
       # docs = self.db.similarity_search(question)
       # return (self.chain.run(input_documents=docs, question=question))
       return (self.chain.run(question))


def wrap_text_preserve_newlines(text, width=200):
    # Split the input text into lines based on newline characters
    lines = text.split('\\n')

    # Wrap each line individually
    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]

    # Join the wrapped lines back together using newline characters
    wrapped_text = '\n'.join(wrapped_lines)

    return wrapped_text

os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"
url = "https://raw.githubusercontent.com/hwchase17/langchain/master/docs/modules/state_of_the_union.txt"
# url = "https://raw.githubusercontent.com/NTT123/Vietnamese-Text-To-Speech-Dataset/master/collections.txt"
# url = "https://raw.githubusercontent.com/NTT123/Vietnamese-Text-To-Speech-Dataset/master/collections.txt"


# res = requests.get(url)
# with open("state_of_the_union.txt", "w") as f:
#   f.write(res.text)

# Document Loader

# loader = TextLoader('./collections.txt', encoding='utf-8')
# # loader = TextLoader('./state_of_the_union.txt', encoding='utf-8')
# documents = loader.load()
# # print(wrap_text_preserve_newlines(str(documents[0])))
# text_splitter = CharacterTextSplitter(
#     separator = "\n",
#     chunk_size = 1000,
#     chunk_overlap  = 200,
#     length_function = len,
# )
# docs = text_splitter.split_documents(documents)
# # print(len(docs))
# # embeddings = HuggingFaceEmbeddings(model_name='keepitreal/vietnamese-sbert')
# embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
# # embeddings = HuggingFaceEmbeddings()
#
#
# db = FAISS.from_documents(docs, embeddings)
#
# # query = "giờ làm việc của công ty"
# # docs = db.similarity_search(query)
#
#
# # print(wrap_text_preserve_newlines(str(docs[0].page_content)))
#
#
# # print(wrap_text_preserve_newlines(str(docs[1].page_content)))
# # model_id = 'google/flan-t5-base'# go for a smaller model if you dont have the VRAM
# model_id = 'VietAI/gpt-neo-1.3B-vietnamese-news'# go for a smaller model if you dont have the VRAM
# # tokenizer = AutoTokenizer.from_pretrained(model_id)
# # # model = AutoModelForSeq2SeqLM.from_pretrained(model_id,load_in_8bit=False,low_cpu_mem_usage=True)
# # model = AutoModelForCausalLM.from_pretrained(model_id,load_in_8bit=False,low_cpu_mem_usage=True)
# #
# # pipe = pipeline(
# #     "text-generation",
# #     model=model,
# #     tokenizer=tokenizer,
# #     pad_token_id=20000,
# #     temperature=0.9,
# #     max_length=500
# # )
# # llm=HuggingFaceHub(repo_id="VietAI/gpt-neo-1.3B-vietnamese-news", model_kwargs={"temperature":0.9,"top_k":20,"do_sample":True,"max_length":500})
# # local_llm = HuggingFacePipeline(pipeline=pipe)
# # llm=HuggingFaceHub(repo_id="google/flan-t5-base", model_kwargs={"temperature":0, "max_length":512})
# # llm=HuggingFaceHub(repo_id="VietAI/gpt-neo-1.3B-vietnamese-news", model_kwargs={"temperature":0.1, "max_length":500})
# chain = load_qa_chain(llm=OpenAI(temperature=0.5), chain_type="stuff")
# query = "được quyền lợi gì khi đẻ con"
# # query = "What did the president say about the Economy"
# docs = db.similarity_search(query)
#
# print(chain.run(input_documents=docs,question=query))

chatgpt = ChatGPT()




def chatbot(input_text):

    response = chatgpt.query(input_text)
    return response

iface = gr.Interface(fn=chatbot,
                     inputs=gr.components.Textbox(lines=7, label="Enter your text"),
                     outputs="text",
                     title="Custom-trained AI Chatbot")
iface.launch(share=True)