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1 Parent(s): ba99eba

Create app.py

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  1. app.py +46 -0
app.py ADDED
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+ import streamlit as st
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+ from langchain.llms import OpenAI
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+ from langchain.text_splitter import CharacterTextSplitter
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+ from langchain.embeddings import OpenAIEmbeddings
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+ from langchain.vectorstores import Chroma
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+ from langchain.chains import RetrievalQA
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+
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+ def generate_response(uploaded_file, openai_api_key, query_text):
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+ # Load document if file is uploaded
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+ if uploaded_file is not None:
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+ documents = [uploaded_file.read().decode()]
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+ # Split documents into chunks
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+ text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
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+ texts = text_splitter.create_documents(documents)
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+ # Select embeddings
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+ embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
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+ # Create a vectorstore from documents
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+ db = Chroma.from_documents(texts, embeddings)
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+ # Create retriever interface
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+ retriever = db.as_retriever()
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+ # Create QA chain
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+ qa = RetrievalQA.from_chain_type(llm=OpenAI(openai_api_key=openai_api_key), chain_type='stuff', retriever=retriever)
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+ return qa.run(query_text)
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+
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+ # Page title
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+ st.set_page_config(page_title='πŸ¦œπŸ”— Ask the Doc App')
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+ st.title('πŸ¦œπŸ”— Ask the Doc App')
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+
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+ # File upload
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+ uploaded_file = st.file_uploader('Upload an article', type='txt')
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+ # Query text
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+ query_text = st.text_input('Enter your question:', placeholder = 'Please provide a short summary.', disabled=not uploaded_file)
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+
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+ # Form input and query
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+ result = []
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+ with st.form('myform', clear_on_submit=True):
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+ openai_api_key = st.text_input('OpenAI API Key', type='password', disabled=not (uploaded_file and query_text))
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+ submitted = st.form_submit_button('Submit', disabled=not(uploaded_file and query_text))
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+ if submitted and openai_api_key.startswith('sk-'):
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+ with st.spinner('Calculating...'):
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+ response = generate_response(uploaded_file, openai_api_key, query_text)
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+ result.append(response)
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+ del openai_api_key
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+
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+ if len(result):
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+ st.info(response)