from langchain.chains import RetrievalQA, ConversationalRetrievalChain
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import DirectoryLoader, TextLoader,PyPDFLoader
from transformers import pipeline, AutoModelForCausalLM
from langchain.llms import HuggingFacePipeline
from langchain.embeddings import HuggingFaceInstructEmbeddings
import gradio as gr
from InstructorEmbedding import INSTRUCTOR
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

from transformers import AutoModelForSequenceClassification, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")

pipe = pipeline(
    "text2text-generation",
    model=model,
    tokenizer=tokenizer,
    max_length=200,
    temperature=0.8,
    top_p=0.95,
    repetition_penalty=1.15,
    do_sample=True
)

local_llm = HuggingFacePipeline(pipeline=pipe)
loader = PyPDFLoader('bipolar.pdf')
# loader = TextLoader('info.txt')
document = loader.load()
text_spliter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_spliter.split_documents(document)
embedding = HuggingFaceInstructEmbeddings()
docsearch = Chroma.from_documents(texts, embedding, persist_directory='db')

retriever = docsearch.as_retriever(search_kwargs={"k": 3})
qa_chain = RetrievalQA.from_chain_type(llm=local_llm,
                                       chain_type="stuff",
                                       retriever=retriever,
                                       return_source_documents=True)

def gradinterface(query,history):
    result = qa_chain({'query': query})
    return result['result']


demo = gr.ChatInterface(fn=gradinterface, title='OUR_OWN_BOT')

if __name__ == "__main__":
    demo.launch(share=True)