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Create app.py
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app.py
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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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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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# Page title
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st.set_page_config(page_title='🦜🔗 Ask the Doc App')
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st.title('🦜🔗 Ask the Doc App')
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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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# 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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if len(result):
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st.info(response)
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