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import streamlit as st |
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from tqdm import tqdm |
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from utils.summarize_utils.summ_utils import count_caps, dupe_check, purpose_issue, route_miss |
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tqdm.pandas() |
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import logging, sys |
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logging.disable(sys.maxsize) |
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import warnings |
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warnings.filterwarnings('ignore') |
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from utils.summarize_utils.summwrap import get_data, get_summ |
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from utils.summarize_utils.headlinewrap import get_headline |
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import pandas as pd |
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pd.set_option('display.max_colwidth', 800) |
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def get_summary_app(study_id): |
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study_id_list = study_id.split(",") |
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print(study_id.split(",")) |
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df = get_data(study_id_list) |
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df.head(2) |
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df['LastUpdatePostDate'] = pd.to_datetime(df['LastUpdatePostDate']).dt.strftime('%d %b %Y') |
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df['summary'] = df.progress_apply(lambda x: get_summ( |
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x['OverallStatus'], |
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x['DesignAllocation'], |
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x['DesignMasking'], |
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x['Phase'], |
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x['DesignInterventionModel'], |
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x['OrgStudyId'], |
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x['SecondaryId'], |
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x['LocationCountry'], |
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x['EnrollmentCount'], |
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x['OfficialTitle'], |
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x['BriefSummary'], |
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x['DetailedDescription'], |
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x['WhyStopped'], |
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x['NCTId'], |
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x['LastUpdatePostDate'] |
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), |
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axis=1 |
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) |
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df['headline'] = df.progress_apply(lambda x: get_headline( |
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x['OverallStatus'], |
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x['Phase'], |
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x['OrgStudyId'], |
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x['SecondaryId'], |
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x['Condition'] |
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), |
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axis=1 |
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) |
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df['Review_Required'] = df['summary'].apply(lambda summary: purpose_issue(summary)) |
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df['Review_Required'] = df.apply(lambda row: dupe_check(row['summary'], row['Review_Required']), axis=1) |
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df['Review_Required'] = df.apply(lambda row: count_caps(row['summary'], row['Review_Required']), axis=1) |
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df['Review_Required'] = df.apply( |
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lambda row: route_miss(row['summary'], row['Review_Required'], row['InterventionDescription']), axis=1) |
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final_df = df[['NCTId', 'summary', 'Review_Required', 'headline']] |
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ids = df['NCTId'].to_markdown() |
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summary_output = df['summary'].to_markdown() |
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headline_output = df['headline'].to_markdown() |
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return headline_output, summary_output |
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