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Upload app.py with huggingface_hub

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  1. app.py +80 -0
app.py ADDED
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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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+
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+ # using a vector space for our search
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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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+
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+ #loading FAISS search index from disk
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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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+
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+ #Get GPT3 response using Langchain
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+ def print_answer(question, openai): #openai_embeddings
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+ #search_index = get_search_index()
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+ chain = load_qa_with_sources_chain(openai) #(OpenAI(temperature=0))
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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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+
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+
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+ def chat(message, history, openai_api_key):
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+ #openai_embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
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+ openai = OpenAI(temperature=0, openai_api_key=openai_api_key )
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+ #os.environ["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) #openai_embeddings
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+ history.append((message, response))
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+ return history, history
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+
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+
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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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+ lumukso 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 lumukso expert bot, start by typing in your OpenAI API key, questions/issues you are facing in your lumukso 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 lumukso 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 lumukso here and press Enter!', placeholder = 'What questions do you want to ask about the lumukso 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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+
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+ if __name__ == "__main__":
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+ demo.launch()