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from pydantic import NoneStr | |
import os | |
from langchain.chains.question_answering import load_qa_chain | |
from langchain.document_loaders import UnstructuredFileLoader | |
from langchain.embeddings.openai import OpenAIEmbeddings | |
from langchain.llms import OpenAI | |
from langchain.text_splitter import CharacterTextSplitter | |
from langchain.vectorstores import FAISS | |
from pypdf import PdfReader | |
import mimetypes | |
import validators | |
import requests | |
import tempfile | |
import gradio as gr | |
import openai | |
def get_empty_state(): | |
return {"knowledge_base": None} | |
def create_knowledge_base(docs): | |
# split into chunks | |
text_splitter = CharacterTextSplitter( | |
separator="\n", chunk_size=500, chunk_overlap=0, length_function=len | |
) | |
chunks = text_splitter.split_documents(docs) | |
# Create embeddings | |
embeddings = OpenAIEmbeddings() | |
knowledge_base = FAISS.from_documents(chunks, embeddings) | |
return knowledge_base | |
def upload_file(file_obj): | |
try: | |
loader = UnstructuredFileLoader(file_obj.name, strategy="fast") | |
docs = loader.load() | |
knowledge_base = create_knowledge_base(docs) | |
except: | |
text="Try Another file" | |
return file_obj.name, text | |
return file_obj.name, {"knowledge_base": knowledge_base} | |
def upload_via_url(url): | |
if validators.url(url): | |
r = requests.get(url) | |
if r.status_code != 200: | |
raise ValueError( | |
"Check the url of your file; returned status code %s" % r.status_code | |
) | |
content_type = r.headers.get("content-type") | |
file_extension = mimetypes.guess_extension(content_type) | |
temp_file = tempfile.NamedTemporaryFile(suffix=file_extension, delete=False) | |
temp_file.write(r.content) | |
file_path = temp_file.name | |
loader = UnstructuredFileLoader(file_path, strategy="fast") | |
docs = loader.load() | |
with open(file_path, mode="rb") as f: | |
pass | |
knowledge_base = create_knowledge_base(docs) | |
return file_path, {"knowledge_base": knowledge_base} | |
else: | |
raise ValueError("Please enter a valid URL") | |
def answer_question(question, state): | |
try: | |
knowledge_base = state["knowledge_base"] | |
docs = knowledge_base.similarity_search(question) | |
llm = OpenAI(temperature=0.4) | |
chain = load_qa_chain(llm, chain_type="stuff") | |
response = chain.run(input_documents=docs, question=question) | |
return response | |
except: | |
return "Please upload Proper Document" | |
title = """<br><br><br><div style="text-align: center;max-width: 700px;"> | |
<h1><a style="display:inline-block; margin-left: 1em; text-decoration:none; font-weight:bold;" href="https://www.adople.com">ADOPLE AI</a> - Document QA</h1> | |
</p>""" | |
with gr.Blocks(css="style.css",theme=gr.themes.Soft()) as demo: | |
state = gr.State(get_empty_state()) | |
with gr.Column(elem_id="col-container"): | |
gr.HTML(title) | |
gr.Markdown("**Upload your file**") | |
with gr.Row(elem_id="row-flex"): | |
with gr.Column(scale=0.85): | |
file_url = gr.Textbox( | |
value="", | |
label="Upload your file", | |
placeholder="Enter a url", | |
show_label=False, | |
visible=True,elem_classes="filenameshow") | |
with gr.Column(scale=0.15, min_width=160): | |
upload_button = gr.UploadButton("Browse File", file_types=[".txt", ".pdf", ".doc", ".docx"],elem_classes="filenameshow") | |
file_output = gr.File(elem_classes="filenameshow") | |
with gr.Row(): | |
with gr.Column(scale=1, min_width=0): | |
user_question = gr.Textbox(value="",label='Question Box :',show_label=True, placeholder="Ask a question about your file:",elem_classes="spaceH") | |
with gr.Row(): | |
with gr.Column(scale=1, min_width=0): | |
answer = gr.Textbox(value="",label='Answer Box :',show_label=True, placeholder="",lines=5) | |
file_url.submit(upload_via_url, file_url, [file_output, state]) | |
upload_button.upload(upload_file, upload_button, [file_output,state]) | |
user_question.submit(answer_question, [user_question, state], [answer]) | |
demo.launch() |