Spaces:
Sleeping
Sleeping
orionweller
commited on
Commit
•
02405e8
1
Parent(s):
49bc621
add options to turn features off
Browse files
app.py
CHANGED
@@ -174,7 +174,13 @@ with st.sidebar:
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st.header("Upload qrels")
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qrels_file = st.file_uploader("Choose a file", key="qrels")
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qrels = load_local_qrels(qrels_file)
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-
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## make sure all qids in qrels are in queries and write out a warning if not
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if queries is not None and qrels is not None:
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missing_qids = set(qrels.keys()) - set(queries.keys()) | set(queries.keys()) - set(qrels.keys())
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@@ -271,7 +277,12 @@ if corpus is not None and queries is not None and qrels is not None:
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# relevant
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relevant_docs = list(qrels[str(inst_num)].keys())[:n_relevant_docs]
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doc_texts = [(doc_id, corpus[doc_id]["title"] if "title" in corpus[doc_id] else "", corpus[doc_id]["text"]) for doc_id in relevant_docs]
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-
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# make a selectbox of these splitting words (allow multiple)
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container.subheader("Splitting Words")
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@@ -284,7 +295,7 @@ if corpus is not None and queries is not None and qrels is not None:
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highlighted_texts = []
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highlighted_titles = []
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for (docid, title, text) in tqdm.tqdm(doc_texts):
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-
if not len(splitting_word_select):
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highlighted_texts.append(text)
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highlighted_titles.append(title)
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continue
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st.header("Upload qrels")
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qrels_file = st.file_uploader("Choose a file", key="qrels")
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qrels = load_local_qrels(qrels_file)
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# add a checkbox to turn off highlighting
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st.header("Highlighting Off")
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highlighting_off = st.checkbox("Turn off highlighting", key="highlighting_off")
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# add a checkbox to turn off word suggestions
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st.header("Word Suggestions Off")
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word_suggestions_off = st.checkbox("Turn off word suggestions", key="word_suggestions_off")
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## make sure all qids in qrels are in queries and write out a warning if not
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if queries is not None and qrels is not None:
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missing_qids = set(qrels.keys()) - set(queries.keys()) | set(queries.keys()) - set(qrels.keys())
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# relevant
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relevant_docs = list(qrels[str(inst_num)].keys())[:n_relevant_docs]
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doc_texts = [(doc_id, corpus[doc_id]["title"] if "title" in corpus[doc_id] else "", corpus[doc_id]["text"]) for doc_id in relevant_docs]
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if word_suggestions_off:
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splitting_words = []
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else:
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splitting_words = find_dividing_words([item[1] + " " + item[2] for item in doc_texts])
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# make a selectbox of these splitting words (allow multiple)
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container.subheader("Splitting Words")
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highlighted_texts = []
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highlighted_titles = []
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for (docid, title, text) in tqdm.tqdm(doc_texts):
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if not len(splitting_word_select) or highlighting_off:
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highlighted_texts.append(text)
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highlighted_titles.append(title)
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continue
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