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| import gradio as gr | |
| import os | |
| import time | |
| from langchain.document_loaders import OnlinePDFLoader #for laoding the pdf | |
| from langchain.embeddings import OpenAIEmbeddings # for creating embeddings | |
| from langchain.vectorstores import Chroma # for the vectorization part | |
| from langchain.chains import ConversationalRetrievalChain # for conversing with chatGPT | |
| from langchain.chat_models import ChatOpenAI # the LLM model we'll use (ChatGPT) | |
| def loading_pdf(): | |
| return "Loading..." | |
| def pdf_changes(pdf_doc, open_ai_key): | |
| if openai_key is not None: | |
| os.environ['OPENAI_API_KEY'] = open_ai_key | |
| #Load the pdf file | |
| loader = OnlinePDFLoader(pdf_doc.name) | |
| pages = loader.load_and_split() | |
| #Create an instance of OpenAIEmbeddings, which is responsible for generating embeddings for text | |
| embeddings = OpenAIEmbeddings() | |
| #To create a vector store, we use the Chroma class, which takes the documents (pages in our case), the embeddings instance, and a directory to store the vector data | |
| vectordb = Chroma.from_documents(pages, embedding=embeddings) | |
| #Finally, we create the bot using the ConversationalRetrievalChain class | |
| #A ConversationalRetrievalChain is similar to a RetrievalQAChain, except that the ConversationalRetrievalChain allows for | |
| #passing in of a chat history which can be used to allow for follow up questions. | |
| global pdf_qa | |
| pdf_qa = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0, model_name="gpt-4"), vectordb.as_retriever(), return_source_documents=False) | |
| return "Ready" | |
| else: | |
| return "Please provide an OpenAI API key" | |
| def add_text(history, text): | |
| history = history + [(text, None)] | |
| return history, "" | |
| def bot(history): | |
| response = infer(history[-1][0], history) | |
| history[-1][1] = "" | |
| for character in response: | |
| history[-1][1] += character | |
| time.sleep(0.05) | |
| yield history | |
| def infer(question, history): | |
| results = [] | |
| for human, ai in history[:-1]: | |
| pair = (human, ai) | |
| results.append(pair) | |
| chat_history = results | |
| print(chat_history) | |
| query = question | |
| result = pdf_qa({"question": query, "chat_history": chat_history}) | |
| print(result) | |
| return result["answer"] | |
| css=""" | |
| #col-container {max-width: 700px; margin-left: auto; margin-right: auto;} | |
| """ | |
| title = """ | |
| <div style="text-align: center;max-width: 700px;"> | |
| <h1>Chatbot for PDFs - GPT-4</h1> | |
| <p style="text-align: center;">Upload a .PDF, click the "Load PDF to LangChain" button, <br /> | |
| Wait for the Status to show Ready, start typing your questions. <br /> | |
| The app is set to store chat-history and is built on GPT-4</p> | |
| </div> | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.HTML(title) | |
| with gr.Column(): | |
| openai_key = gr.Textbox(label="Your OpenAI API key", type="password") | |
| pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file") | |
| 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) | |
| question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ") | |
| submit_btn = gr.Button("Send Message") | |
| load_pdf.click(loading_pdf, None, langchain_status, queue=False) | |
| load_pdf.click(pdf_changes, inputs=[pdf_doc, openai_key], outputs=[langchain_status], queue=False) | |
| question.submit(add_text, [chatbot, question], [chatbot, question]).then( | |
| bot, chatbot, chatbot | |
| ) | |
| submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then( | |
| bot, chatbot, chatbot) | |
| demo.launch() |