import gradio as gr from langchain.document_loaders import OnlinePDFLoader from langchain.text_splitter import CharacterTextSplitter text_splitter = CharacterTextSplitter(chunk_size=350, chunk_overlap=0) from langchain.llms import HuggingFaceHub flan_ul2 = HuggingFaceHub(repo_id="google/flan-ul2", model_kwargs={"temperature":0.1, "max_new_tokens":300}) from langchain.embeddings import HuggingFaceHubEmbeddings embeddings = HuggingFaceHubEmbeddings() from langchain.vectorstores import Chroma from langchain.chains import RetrievalQA def pdf_changes(pdf_doc): loader = OnlinePDFLoader(pdf_doc.name) documents = loader.load() texts = text_splitter.split_documents(documents) db = Chroma.from_documents(texts, embeddings) retriever = db.as_retriever() global qa qa = RetrievalQA.from_chain_type(llm=flan_ul2, chain_type="stuff", retriever=retriever, return_source_documents=True) return "Ready" def add_text(history, text): history = history + [(text, None)] return history, "" def bot(history): response = infer(history[-1][0]) history[-1][1] = response return history def infer(question): query = question result = qa({"query": query}) return result with gr.Blocks() as demo: with gr.Column(): pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file") langchain_status = gr.Textbox() load_pdf = gr.Button("Load pdf to langchain") chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350) question = gr.Textbox(label="Question") load_pdf.click(pdf_changes, pdf_doc, langchain_status, queue=False) question.submit(add_text, [chatbot, question], [chatbot, question]).then( bot, chatbot, chatbot ) demo.launch()