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import gradio as gr
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 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=200, length_function=len
)
chunks = text_splitter.split_documents(docs)
embeddings = OpenAIEmbeddings()
knowledge_base = FAISS.from_documents(chunks, embeddings)
return knowledge_base
def upload_multiple_urls(urls):
all_docs = []
file_paths = []
for url in urls:
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()
all_docs.extend(docs)
file_paths.append(file_path)
else:
raise ValueError("Please enter a valid URL")
knowledge_base = create_knowledge_base(all_docs)
return file_paths, {"knowledge_base": knowledge_base}
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"
def process_files(urls, question):
file_output, state = upload_multiple_urls([url.strip() for url in urls.split(',')])
answer = answer_question(question, state)
return answer
with gr.Blocks(css="style.css",theme=gr.themes.Soft()) as demo:
with gr.Row():
gr.HTML("""<center><img class="" align="" src="https://companieslogo.com/img/orig/RAND.AS_BIG-0f1935a4.png?t=1651813778" alt="Image" width="210" height="210"></center>""")
with gr.Row():
gr.HTML("""<center><h1>Mulit-URL QA</h1></center>""")
with gr.Row():
url = gr.Textbox(label="URL")
with gr.Row():
Question = gr.Textbox(label="Question")
with gr.Column(scale=0.30):
enter_btn = gr.Button(value="Enter").style(height=100)
with gr.Row():
Output = gr.Textbox(label="Answer")
enter_btn.click(process_files,[url,Question],Output)
demo.title = "URL QA"
demo.launch() |