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from langchain.llms import OpenAI |
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from langchain.chains.qa_with_sources import load_qa_with_sources_chain |
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from langchain.docstore.document import Document |
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import requests |
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import pathlib |
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import subprocess |
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import tempfile |
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import os |
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import gradio as gr |
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import pickle |
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from langchain.embeddings.openai import OpenAIEmbeddings |
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from langchain.vectorstores.faiss import FAISS |
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from langchain.text_splitter import CharacterTextSplitter |
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with open("search_index.pickle", "rb") as f: |
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search_index = pickle.load(f) |
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def print_answer(question, openai): |
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chain = load_qa_with_sources_chain(openai) |
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response = ( |
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chain( |
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{ |
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"input_documents": search_index.similarity_search(question, k=4), |
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"question": question, |
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}, |
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return_only_outputs=True, |
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)["output_text"] |
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) |
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if len(response.split('\n')[-1].split())>2: |
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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()))]) |
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else: |
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response = response.split('\n')[0] + ' <a href="' + response.split('\n')[-1].split()[-1] + '" target="_blank"><u>Click Link</u></a>' |
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return response |
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def chat(message, history, openai_api_key): |
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openai = OpenAI(temperature=0, openai_api_key=openai_api_key ) |
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history = history or [] |
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message = message.lower() |
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response = print_answer(message, openai) |
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history.append((message, response)) |
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return history, history |
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with gr.Blocks() as demo: |
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gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;"> |
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<div |
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style=" |
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display: inline-flex; |
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align-items: center; |
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gap: 0.8rem; |
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font-size: 1.75rem; |
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" |
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> |
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<h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;"> |
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$RepoName QandA - LangChain Bot |
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</h1> |
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</div> |
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<p style="margin-bottom: 10px; font-size: 94%"> |
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Hi, I'm a Q and A $RepoName expert bot, start by typing in your OpenAI API key, questions/issues you are facing in your $RepoName implementations and then press enter.<br> |
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<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> |
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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 $RepoName Repo |
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</p> |
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</div>""") |
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with gr.Row(): |
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question = gr.Textbox(label = 'Type in your questions about $RepoName here and press Enter!', placeholder = 'What questions do you want to ask about the $RepoName library?') |
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openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here") |
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state = gr.State() |
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chatbot = gr.Chatbot() |
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question.submit(chat, [question, state, openai_api_key], [chatbot, state]) |
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if __name__ == "__main__": |
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demo.launch() |
