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import gradio as gr |
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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 HuggingFaceHub |
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from langchain.embeddings import HuggingFaceHubEmbeddings |
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from langchain.vectorstores import Chroma |
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from langchain.chains import RetrievalQA |
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def loading_pdf(): |
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return "Loading..." |
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def pdf_changes(pdf_doc, repo_id): |
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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=350, chunk_overlap=0) |
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texts = text_splitter.split_documents(documents) |
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embeddings = HuggingFaceHubEmbeddings() |
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db = Chroma.from_documents(texts, embeddings) |
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retriever = db.as_retriever() |
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llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature":0.1, "max_new_tokens":300}) |
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global qa |
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qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True) |
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return "Ready" |
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def add_text(history, text): |
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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]) |
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history[-1][1] = response['result'] |
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return history |
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def infer(question): |
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query = question |
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result = qa({"query": query}) |
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return result |
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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>Chat with PDF</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 ;)</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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repo_id = gr.Dropdown(label="LLM", choices=["google/flan-ul2", "stabilityai/stablelm-tuned-alpha-3b", "databricks/dolly-v2-3b", "Writer/camel-5b-hf" ], value="google/flan-ul2") |
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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("Load pdf to langchain") |
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chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350) |
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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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repo_id.change(pdf_changes, inputs=[pdf_doc, repo_id], outputs=[langchain_status], queue=False) |
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load_pdf.click(pdf_changes, inputs=[pdf_doc, repo_id], 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() |
