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| # from pydantic.v1 import BaseSettings | |
| 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",post_processors=[clean_extra_whitespace]) | |
| 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("""<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",".json",".csv"], | |
| 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() | |