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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"
with gr.Blocks(css="style.css",theme=gr.themes.Soft()) as demo:
state = gr.State(get_empty_state())
gr.HTML("""<h1 class="leftimage" align="left" style="color:#100C08;font-size:3vw;font-weight: bold;">ADOPLE AI</h1>
<img class="rightimage" align="right" src="https://www.dmgflooringltd.co.uk/wp-content/uploads/NHS.png" alt="Image" width="180" height="180">""")
with gr.Column(elem_id="col-container"):
gr.HTML(
"""<hr style="border-top: 5px solid white;">"""
)
gr.HTML(
"""<br>
<h1 style="text-align:center;">
NHS Document QA
</h1> """
)
gr.HTML(
"""<hr style="border-top: 5px solid white;">"""
)
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=False
# )
with gr.Column(scale=0.90, min_width=160):
file_output = gr.File(elem_classes="filenameshow")
with gr.Column(scale=0.10, min_width=160):
upload_button = gr.UploadButton(
"Browse File", file_types=[".txt", ".pdf", ".doc", ".docx"],
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.queue().launch()
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