ysharma/LangChain_wandbBot

1from langchain.llms import OpenAI2from langchain.chains.qa_with_sources import load_qa_with_sources_chain3from langchain.docstore.document import Document4import requests5import pathlib6import subprocess7import tempfile8import os9import gradio as gr10import pickle11 12# using a vector space for our search13from langchain.embeddings.openai import OpenAIEmbeddings14from langchain.vectorstores.faiss import FAISS15from langchain.text_splitter import CharacterTextSplitter16                17#loading FAISS search index from disk18with open("search_index.pickle", "rb") as f:19    search_index = pickle.load(f)20 21#Get GPT3 response using Langchain22def print_answer(question, openai):   #openai_embeddings23    #search_index = get_search_index()24    chain = load_qa_with_sources_chain(openai) #(OpenAI(temperature=0)) 25    response = (26        chain(27            {28                "input_documents": search_index.similarity_search(question, k=4),29                "question": question,30            },31            return_only_outputs=True,32        )["output_text"]33    )34    if len(response.split('\n')[-1].split())>2:35        response = response.split('\n')[0] + ', '.join([' <a href="' + response.split('\n')[-1].split()[i] + '" target="_blank"><u>Click Link' + str(i) + '</u></a>' for i in range(1,len(response.split('\n')[-1].split()))])36    else: 37        response = response.split('\n')[0] + ' <a href="' + response.split('\n')[-1].split()[-1] + '" target="_blank"><u>Click Link</u></a>'38    return response39 40 41def chat(message, history, openai_api_key):42    #openai_embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)43    openai = OpenAI(temperature=0, openai_api_key=openai_api_key )44    #os.environ["OPENAI_API_KEY"] = openai_api_key45    history = history or []46    message = message.lower()47    response = print_answer(message, openai)   #openai_embeddings48    history.append((message, response))49    return history, history50 51 52with gr.Blocks() as demo:53  gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">54        <div55        style="56            display: inline-flex;57            align-items: center;58            gap: 0.8rem;59            font-size: 1.75rem;60        "61        >62        <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">63            wandb QandA - LangChain Bot64        </h1>65        </div>66        <p style="margin-bottom: 10px; font-size: 94%">67        Hi, I'm a Q and A wandb expert bot, start by typing in your OpenAI API key, questions/issues you are facing in your wandb implementations and then press enter.<br>68        <a href="https://huggingface.co/spaces/ysharma/InstructPix2Pix_Chatbot?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate Space with GPU Upgrade for fast Inference & no queue<br> 69        Built using <a href="https://langchain.readthedocs.io/en/latest/" target="_blank">LangChain</a> and <a href="https://github.com/gradio-app/gradio" target="_blank">Gradio</a> for the wandb Repo70        </p>71    </div>""")  72  with gr.Row():73    question = gr.Textbox(label = 'Type in your questions about wandb here and press Enter!', placeholder = 'What questions do you want to ask about the wandb library?')74    openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here")75  state = gr.State()76  chatbot = gr.Chatbot()77  question.submit(chat, [question, state, openai_api_key], [chatbot, state])78 79if __name__ == "__main__":80    demo.launch()