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import gradio as gr |
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import os |
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import time |
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import threading |
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from langchain.document_loaders import OnlinePDFLoader |
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from langchain.text_splitter import CharacterTextSplitter |
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from langchain.llms import OpenAI |
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from langchain.embeddings import OpenAIEmbeddings |
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from langchain.vectorstores import Chroma |
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from langchain.chains import ConversationalRetrievalChain |
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os.environ['OPENAI_API_KEY'] = os.getenv("Your_API_Key") |
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last_interaction_time = 0 |
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def loading_pdf(): |
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return "Working on the upload. Also, pondering the usefulness of sporks..." |
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def pdf_changes(pdf_doc): |
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loader = OnlinePDFLoader(pdf_doc.name) |
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documents = loader.load() |
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text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100) |
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texts = text_splitter.split_documents(documents) |
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embeddings = OpenAIEmbeddings() |
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db = Chroma.from_documents(texts, embeddings) |
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retriever = db.as_retriever() |
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global qa |
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qa = ConversationalRetrievalChain.from_llm( |
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llm=OpenAI(temperature=0.2, model_name="gpt-3.5-turbo"), |
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retriever=retriever, |
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return_source_documents=False) |
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return "Ready" |
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def clear_data(): |
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global qa |
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qa = None |
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return "Data cleared" |
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def add_text(history, text): |
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global last_interaction_time |
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last_interaction_time = time.time() |
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history = history + [(text, None)] |
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return history, "" |
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def bot(history): |
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response = infer(history[-1][0], history) |
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formatted_response = "**Bot:** \n" + ' \n'.join(response.split('. ')) |
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history[-1][1] = formatted_response |
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return history |
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def infer(question, history): |
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res = [] |
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for human, ai in history[:-1]: |
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pair = (human, ai) |
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res.append(pair) |
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chat_history = res |
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query = question |
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result = qa({"question": query, "chat_history": chat_history, "system:":"This is a world-class summarizing AI, be helpful."}) |
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return result["answer"] |
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def auto_clear_data(): |
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global qa, last_interaction_time |
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if time.time() - last_interaction_time > 600: |
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qa = None |
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def periodic_clear(): |
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while True: |
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auto_clear_data() |
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time.sleep(60) |
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threading.Thread(target=periodic_clear).start() |
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css = """ |
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#col-container {max-width: 700px; margin-left: auto; margin-right: auto;} |
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""" |
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title = """ |
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<div style="text-align: center;max-width: 700px;"> |
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<h1>CauseWriter Chat with PDF • OpenAI</h1> |
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<p style="text-align: center;">Upload a .PDF from your computer, click the "Load PDF to LangChain" button, <br /> |
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when everything is ready, you can start asking questions about the pdf. <br /> |
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This version is set to store chat history and uses OpenAI as LLM.</p> |
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</div> |
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""" |
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with gr.Blocks(css=css) as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.HTML(title) |
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with gr.Column(): |
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pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file") |
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with gr.Row(): |
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langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False) |
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load_pdf = gr.Button("Convert PDF to Magic AI language") |
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clear_btn = gr.Button("Clear Data") |
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chatbot = gr.Chatbot([], elem_id="chatbot").style(height=450) |
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question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter") |
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submit_btn = gr.Button("Send Message") |
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load_pdf.click(loading_pdf, None, langchain_status, queue=False) |
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load_pdf.click(pdf_changes, inputs=[pdf_doc], outputs=[langchain_status], queue=False) |
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clear_btn.click(clear_data, outputs=[langchain_status], queue=False) |
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question.submit(add_text, [chatbot, question], [chatbot, question]).then( |
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bot, chatbot, chatbot |
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) |
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submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then( |
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bot, chatbot, chatbot |
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) |
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demo.launch() |
