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() 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("""

ADOPLE AI

Image""") with gr.Column(elem_id="col-container"): gr.HTML( """
""" ) gr.HTML( """

NHS Document QA

""" ) gr.HTML( """
""" ) 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()