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| # Import necessary libraries | |
| import gradio as gr | |
| from langchain.document_loaders import OnlinePDFLoader | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.llms import HuggingFaceHub | |
| from langchain.embeddings import HuggingFaceHubEmbeddings | |
| from langchain.vectorstores import Chroma | |
| from langchain.chains import RetrievalQA | |
| # Define a function to display "Loading..." when loading a PDF | |
| def loading_pdf(): | |
| return "Loading..." | |
| # Define a function to process PDF changes | |
| def pdf_changes(pdf_doc, repo_id): | |
| # Initialize the OnlinePDFLoader to load the PDF document | |
| loader = OnlinePDFLoader(pdf_doc.name) | |
| documents = loader.load() | |
| # Split the loaded documents into chunks using CharacterTextSplitter | |
| text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=50) | |
| texts = text_splitter.split_documents(documents) | |
| # Initialize HuggingFaceHubEmbeddings for embeddings | |
| embeddings = HuggingFaceHubEmbeddings() | |
| # Create a Chroma vector store from the text chunks and embeddings | |
| db = Chroma.from_documents(texts, embeddings) | |
| # Convert the vector store to a retriever | |
| retriever = db.as_retriever() | |
| # Initialize an HuggingFaceHub language model (LLM) | |
| llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0.25, "max_new_tokens": 1000}) | |
| # Create a RetrievalQA chain with the LLM, retriever, and return_source_documents option | |
| global qa | |
| qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=False) | |
| return "Ready" | |
| # Define a function to add text to a history | |
| def add_text(history, text): | |
| history = history + [(text, None)] | |
| return history, "" | |
| # Define a bot function to generate responses | |
| def bot(history): | |
| response = infer(history[-1][0]) | |
| history[-1][1] = response['result'] | |
| return history | |
| # Define an inference function to query the LLM | |
| def infer(query): | |
| result = qa({"query": query}) | |
| return result | |
| # Define custom CSS styles | |
| css = """ | |
| #col-container {max-width: 700px; margin-left: auto; margin-right: auto;} | |
| """ | |
| # Define a title HTML for the interface | |
| 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> | |
| """ | |
| # Create the Gradio interface | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.HTML(title) | |
| with gr.Column(): | |
| # Create a file input for loading PDF | |
| pdf_doc = gr.File(label="Load a PDF", file_types=['.pdf'], type="file", value="AhmedS_Resume.pdf") | |
| # Create a dropdown for selecting the LLM | |
| repo_id = gr.Dropdown(label="LLM", choices=["HuggingFaceH4/zephyr-7b-alpha", "google/flan-t5-xxl", "gpt2-xl"], value="HuggingFaceH4/zephyr-7b-alpha") | |
| with gr.Row(): | |
| langchain_status = gr.Textbox(label="Status", placeholder="Waiting...", interactive=False) | |
| load_pdf = gr.Button("Load PDF to LangChain") | |
| chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350) | |
| query = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ") | |
| submit_btn = gr.Button("Send message") | |
| # Set up actions for UI elements | |
| repo_id.change(pdf_changes, inputs=[pdf_doc, repo_id], outputs=[langchain_status], queue=False) | |
| load_pdf.click(pdf_changes, inputs=[pdf_doc, repo_id], outputs=[langchain_status], queue=False) | |
| query.submit(add_text, [chatbot, query], [chatbot, query]).then(bot, chatbot, chatbot) | |
| submit_btn.click(add_text, [chatbot, query], [chatbot, query]).then(bot, chatbot, chatbot) | |
| # Launch the Gradio interface | |
| demo.launch() | |