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| import gradio as gr | |
| from langchain.document_loaders import OnlinePDFLoader | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.prompts import PromptTemplate | |
| # from langhchain.llms import openai | |
| from langchain.llms import OpenAI | |
| text_splitter = CharacterTextSplitter(chunk_size=350, chunk_overlap=0) | |
| from langchain.llms import HuggingFaceHub | |
| # flan_ul2 = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature":0.1, "max_new_tokens":300}) | |
| flan_ul2 = OpenAI() | |
| global qa | |
| from langchain.embeddings import HuggingFaceHubEmbeddings, OpenAIEmbeddings | |
| # embeddings = HuggingFaceHubEmbeddings() | |
| embeddings = OpenAIEmbeddings() | |
| from langchain.vectorstores import Chroma | |
| from langchain.chains import RetrievalQA | |
| from langchain.document_loaders import PyPDFLoader | |
| def loading_pdf(): | |
| return "Loading..." | |
| def pdf_changes(pdf_doc): | |
| # loader = OnlinePDFLoader(pdf_doc.name) | |
| loader = PyPDFLoader(pdf_doc.name) | |
| documents = loader.load() | |
| texts = text_splitter.split_documents(documents) | |
| db = Chroma.from_documents(texts, embeddings) | |
| retriever = db.as_retriever() | |
| prompt_template = """You have been given a pdf or pdfs. You must search these pdfs. | |
| If you don't know the answer, just say that you don't know, don't try to make up an answer. | |
| Only answer the question. | |
| {context} | |
| Question: {sample.question} | |
| Answer:""" | |
| PROMPT = PromptTemplate( | |
| template=prompt_template, input_variables=["context", "question"] | |
| ) | |
| chain_type_kwargs = {"prompt": PROMPT} | |
| global qa | |
| qa = RetrievalQA.from_chain_type( | |
| llm=flan_ul2, | |
| chain_type="stuff", | |
| retriever=retriever, | |
| return_source_documents=True, | |
| chain_type_kwargs=chain_type_kwargs, | |
| ) | |
| 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['result'] | |
| return history | |
| def infer(question): | |
| query = question | |
| result = qa({"query": query}) | |
| return result | |
| 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</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 ;)</p> | |
| </div> | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.HTML(title) | |
| # with gr.Blocks() as demo: | |
| with gr.Column(): | |
| pdf_doc = gr.File() | |
| # pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="filepath") #try filepath for type if binary does not work | |
| 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) | |
| with gr.Row(): | |
| question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ") | |
| load_pdf.click(loading_pdf, None, langchain_status, queue=False) | |
| 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() | |