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from zipfile import ZipFile |
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import os |
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import streamlit as st |
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from lfqa import prepare, answer |
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with ZipFile("doc_store.zip","r") as zip_ref: |
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zip_ref.extractall('.') |
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DEFAULT_DOCS_FROM_RETRIEVER = int(os.getenv("DEFAULT_DOCS_FROM_RETRIEVER", "3")) |
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DEFAULT_QUESTION_AT_STARTUP = os.getenv("DEFAULT_QUESTION_AT_STARTUP", "Tell me something about Arya Stark?") |
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def set_state_if_absent(key, value): |
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if key not in st.session_state: |
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st.session_state[key] = value |
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def reset_results(*args): |
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st.session_state.answer = None |
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st.session_state.results = None |
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def main(pipe): |
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st.set_page_config(page_title="Haystack Demo", page_icon="https://haystack.deepset.ai/img/HaystackIcon.png") |
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set_state_if_absent("question", DEFAULT_QUESTION_AT_STARTUP) |
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set_state_if_absent("results", None) |
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st.write("# Long-Form Question Answering") |
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st.markdown(""" |
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This demo takes its data from a selection of Wikipedia pages on the topic of the **Game of Thrones** TV series |
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""") |
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st.sidebar.header("Options") |
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top_k_retriever = st.sidebar.slider( |
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"Max. number of documents from retriever", |
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min_value=1, |
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max_value=10, |
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value=DEFAULT_DOCS_FROM_RETRIEVER, |
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step=1, |
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on_change=reset_results, |
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) |
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st.sidebar.markdown( |
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""" |
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<style> |
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a {{ |
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text-decoration: none; |
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}} |
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.haystack-footer {{ |
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text-align: center; |
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}} |
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.haystack-footer h4 {{ |
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margin: 0.1rem; |
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padding:0; |
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}} |
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footer {{ |
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opacity: 0; |
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}} |
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</style> |
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<div class="haystack-footer"> |
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<hr /> |
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<h4>Built with <a href="https://www.deepset.ai/haystack">Haystack</a></h4> |
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<p>Get it on <a href="https://github.com/deepset-ai/haystack/">GitHub</a> - Read the <a href="https://haystack.deepset.ai/overview/intro">Docs</a></p> |
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<small>Dataset link: <a href="https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt12.zip"">Game of Thrones Wiki</a> <br />See the <a href="https://creativecommons.org/licenses/by-sa/3.0/">License</a> (CC BY-SA 3.0).</small> |
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</div> |
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""", |
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unsafe_allow_html=True, |
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) |
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question = st.text_input( |
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value=st.session_state.question, |
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max_chars=100, |
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on_change=reset_results, |
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label="question", |
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label_visibility="hidden", |
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) |
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col1, col2 = st.columns(2) |
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col1.markdown("<style>.stButton button {width:100%;}</style>", unsafe_allow_html=True) |
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run_pressed = col1.button("Run") |
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run_query = run_pressed or (question != st.session_state.question) |
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if run_query and question: |
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reset_results() |
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st.session_state.question = question |
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with st.spinner( |
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"🧠 Performing neural search on documents... \n "): |
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try: |
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st.session_state.results = answer(pipe, question, top_k_retriever) |
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except Exception as e: |
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if "The server is busy processing requests" in str(e) or "503" in str(e): |
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st.error("🧑🌾 All our workers are busy! Try again later.") |
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else: |
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st.error("🐞 An error occurred during the request.") |
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return |
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if st.session_state.results: |
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st.session_state.answer = st.session_state.results['answers'][0].answer |
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st.write(st.session_state.answer) |
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st.write('Doc IDs:') |
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st.write(st.session_state.results['answers'][0].meta['doc_ids']) |
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st.write('Doc Scores:') |
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st.write(st.session_state.results['answers'][0].meta['doc_scores']) |
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for i in range(top_k_retriever): |
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st.write(st.session_state.results['answers'][0].meta['content'][i]) |
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st.markdown('---\n') |
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pipe = prepare() |
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main(pipe) |
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