from langchain_community.document_loaders import TextLoader , PyPDFLoader
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import HuggingFacePipeline
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from langchain.chains import RetrievalQA
import torch
import gradio as gr

# loader = PyPDFLoader('bipolar.pdf')
loader = TextLoader("info.txt")
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter()
# text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=20)
documents = text_splitter.split_documents(docs)

huggingface_embeddings = HuggingFaceBgeEmbeddings(
    model_name="BAAI/bge-small-en-v1.5",
    model_kwargs={'device':'cpu'},
    encode_kwargs={'normalize_embeddings': True}
)

vector = FAISS.from_documents(documents, huggingface_embeddings)
retriever = vector.as_retriever()

model_name = "facebook/bart-base"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

pipe = pipeline(
    "text2text-generation",
    model=model,
    tokenizer=tokenizer,
    max_length=300,
    temperature=0.9,
    top_p=0.9,
    repetition_penalty=1.15,
    do_sample=True

)
local_llm = HuggingFacePipeline(pipeline=pipe)
qa_chain =  RetrievalQA.from_llm(llm=local_llm, retriever=retriever)



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


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

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