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import os
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI
from langchain.chains import ConversationalRetrievalChain
import pickle
import gradio as gr
import time
def upload_file(file, key):
# Set the Enviroment variable
os.environ["OPENAI_API_KEY"] = key
# load document
loader = PyPDFLoader(file.name)
documents = loader.load()
# split the documents into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
texts = text_splitter.split_documents(documents)
# OPENAI embegddings
embeddings = OpenAIEmbeddings()
# create the vectorestore to use as the index
db = FAISS.from_documents(documents, embeddings)
with open("vectorstore.pkl", "wb") as f:
pickle.dump(db, f)
return file.name
with gr.Blocks() as demo:
openai_key = gr.Textbox(label="OPENAI API KEY")
file_output = gr.File(label="Please select a pdf file wait for the document to be displayed here")
upload_button = gr.UploadButton("Click to upload a pdf document", file_types=["pdf"], file_count="single")
upload_button.upload(upload_file, inputs = [upload_button, openai_key], outputs= file_output)
chatbot = gr.Chatbot(label="Chat")
msg = gr.Textbox(label="Enter your query")
clear = gr.Button("Clear")
def user(user_message, history):
return "", history + [[user_message, None]]
def bot(history):
user_message = history[-1][0]
with open("vectorstore.pkl", "rb") as f:
vectorstore = pickle.load(f)
llm = OpenAI(temperature=0)
qa = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)
hist = []
if history[-1][1] != None:
hist = history
result = qa({"question": user_message, "chat_history": hist})
history[-1][1] = result['answer']
return history
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
bot, chatbot, chatbot
)
clear.click(lambda: None, None, chatbot, queue=False)
demo.launch() |