import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Qdrant
from langchain.document_loaders import TextLoader
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
import gradio as gr
from qdrant_client import QdrantClient
from langchain.document_loaders import PagedPDFSplitter

#load data
loader = PagedPDFSplitter("Philippine National Formulary 8th Edition.pdf")
docs = loader.load_and_split()


#declare constants here
OPEN_API_KEY = os.environ["OPENAI_API_KEY"]
host = os.environ["QDRANT_HOST"]
api_key = os.environ["QDRANT_API_KEY"]
embeddings = OpenAIEmbeddings()

#initialize vectorstore
qdrant = Qdrant.from_documents(docs, embeddings, host=host, prefer_grpc=True, api_key=api_key)

#query pipeline
def question_answering(question):
    chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
    query = question
    docs = qdrant.similarity_search(query)
    answer = chain.run(input_documents=docs, question=query)
    return answer

with gr.Blocks() as demo:
    gr.Markdown("Start typing below and then click **Run** to see the output.")
    with gr.Row():
        inp = gr.Textbox(placeholder="Ask question here?")
        out = gr.Textbox()
    btn = gr.Button("Run", api_name="search")
    btn.click(fn=question_answering, inputs=inp, outputs=out)

demo.launch(debug=True)