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) documents = loader.load() texts = text_splitter.split_documents(documents) db = Chroma.from_documents(texts, embeddings) retriever = db.as_retriever() qa = RetrievalQA.from_chain_type(llm=flan_ul2, chain_type="stuff", retriever=retriever, return_source_documents=True) return "Ready" 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") langchain_status = gr.Textbox() pdf_doc.change(pdf_changes, pdf_doc, langchain_status, queue=False) question = gr.Textbox(label="Your Question") answer = gr.Textbox(label="Anwser") submit_button = gr.Button("Send Question") pdf_doc.change(pdf_changes, pdf_doc, langchain_status, queue=False) submit_button.click(infer, inputs=[question], outputs=[answer]) demo.launch()