from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain
from langchain.llms import HuggingFacePipeline
from langchain.memory import ConversationBufferMemory
from langchain_community.retrievers import BM25Retriever
from langchain.retrievers import EnsembleRetriever
import pandas as pd
df = pd.read_csv('NLP.csv')
corpus = df['text'][:300]
#Chunking
splitter = RecursiveCharacterTextSplitter(chunk_size=200,chunk_overlap = 10)
texts = sum([splitter.split_text(doc) for doc in corpus], [])
# Embeddings
embeddings = HuggingFaceEmbeddings(model_name='all-mpnet-base-v2')
# Indexing
db = FAISS.from_texts(texts,embeddings)
retriever = db.as_retriever(search_kwargs={'k':5})

# BM25
bm25 = BM25Retriever.from_texts(texts)
bm25.k =5

# Hy_brid retriever
hybrid_retriever = EnsembleRetriever(
    retrievers = [retriever,bm25],
    weights = [0.7,0.3]
)
# Model
llm = HuggingFacePipeline.from_model_id(model_id='google/flan-t5-large',task='text2text-generation')
# Memory
memory = ConversationBufferMemory(memory_key='chat_history',return_messages=True)
# Combine previous steps
qa = ConversationalRetrievalChain.from_llm(llm=llm,retriever=hybrid_retriever,memory=memory)
def ans_ques(ques):
    result = qa({'question':ques})
    return result['answer']
import gradio as gr

demo = gr.Interface(ans_ques,inputs='text',outputs='text')
demo.launch()