| import gradio as gr |
| import os |
| import time |
|
|
| from langchain.document_loaders import OnlinePDFLoader |
|
|
| from langchain.text_splitter import CharacterTextSplitter |
|
|
|
|
| from langchain.llms import OpenAI |
|
|
| from langchain.embeddings import OpenAIEmbeddings |
|
|
| from langchain.llms import AzureOpenAI |
| import openai |
|
|
| from langchain.vectorstores import Chroma |
|
|
| from langchain.chains import ConversationalRetrievalChain |
|
|
| def loading_pdf(): |
| return "Loading..." |
|
|
| def pdf_changes(pdf_doc, open_ai_key): |
| if True: |
| os.environ['OPENAI_API_KEY'] = open_ai_key |
| os.environ['OPENAI_API_TYPE'] = 'azure' |
| |
| os.environ['OPENAI_API_VERSION'] = '0301' |
| |
| os.environ['OPENAI_API_BASE'] = 'https://nand3.openai.azure.com' |
| |
| llm = AzureOpenAI(deployment_name="Nand", model_name="gpt-35-turbo") |
| |
| loader = OnlinePDFLoader(pdf_doc.name) |
| documents = loader.load() |
| text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) |
| texts = text_splitter.split_documents(documents) |
| embeddings = OpenAIEmbeddings(model="gpt-35-turbo", chunk_size=1000) |
| |
| db = Chroma.from_documents(texts, embeddings) |
| retriever = db.as_retriever() |
| global qa |
| qa = ConversationalRetrievalChain.from_llm( |
| llm=AzureOpenAI(deployment_name="Nand", model_name="gpt-35-turbo"), |
| retriever=retriever, |
| return_source_documents=False) |
| return "Ready" |
| else: |
| return "You forgot OpenAI API key" |
|
|
| def add_text(history, text): |
| history = history + [(text, None)] |
| return history, "" |
|
|
| def bot(history): |
| response = infer(history[-1][0], history) |
| history[-1][1] = "" |
| |
| for character in response: |
| history[-1][1] += character |
| time.sleep(0.05) |
| yield history |
| |
|
|
| def infer(question, history): |
| |
| res = [] |
| for human, ai in history[:-1]: |
| pair = (human, ai) |
| res.append(pair) |
| |
| chat_history = res |
| |
| query = question |
| result = qa({"question": query, "chat_history": chat_history}) |
| |
| return result["answer"] |
|
|
| css=""" |
| #col-container {max-width: 700px; margin-left: auto; margin-right: auto;} |
| """ |
|
|
| title = """ |
| <div style="text-align: center;max-width: 700px;"> |
| <h1>Chat with PDF • OpenAI</h1> |
| <p style="text-align: center;">Upload a .PDF from your computer, click the "Load PDF to LangChain" button, <br /> |
| when everything is ready, you can start asking questions about the pdf ;) <br /> |
| This version is set to store chat history, and uses OpenAI as LLM, don't forget to copy/paste your OpenAI API key</p> |
| </div> |
| """ |
|
|
|
|
| with gr.Blocks(css=css) as demo: |
| with gr.Column(elem_id="col-container"): |
| gr.HTML(title) |
| |
| with gr.Column(): |
| openai_key = gr.Textbox(label="You OpenAI API key", type="password") |
| pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file") |
| with gr.Row(): |
| langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False) |
| load_pdf = gr.Button("Load pdf to langchain") |
| |
| chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350) |
| question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ") |
| submit_btn = gr.Button("Send Message") |
| load_pdf.click(loading_pdf, None, langchain_status, queue=False) |
| load_pdf.click(pdf_changes, inputs=[pdf_doc, openai_key], outputs=[langchain_status], queue=False) |
| question.submit(add_text, [chatbot, question], [chatbot, question]).then( |
| bot, chatbot, chatbot |
| ) |
| submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then( |
| bot, chatbot, chatbot) |
|
|
| demo.launch() |
