Spaces:
Sleeping
Sleeping
File size: 4,244 Bytes
87b3a2f ca6370e 87b3a2f 3a05b97 87b3a2f 3a05b97 87b3a2f 3a05b97 87b3a2f ca6370e 87b3a2f 64245e1 f854776 6814921 4ea05d0 87b3a2f 3a05b97 ba27df6 87b3a2f 44e7e2f c64ea71 44e7e2f c64ea71 87b3a2f fee00c8 87b3a2f fee00c8 87b3a2f 5f091d6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
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() |