awacke1 commited on
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352dcb2
1 Parent(s): f4fa322

Create backup1.app.py

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  1. backup1.app.py +52 -0
backup1.app.py ADDED
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+ import streamlit as st
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+ import pandas as pd
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+ import os
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+ import glob
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+
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+ # Load the provider specialty dataset CSV
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+ @st.cache_resource
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+ def load_specialties(csv_file='Provider-Specialty.csv'):
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+ return pd.read_csv(csv_file)
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+
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+ specialties = load_specialties()
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+
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+ # User interface for specialty selection
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+ st.title('Provider Specialty Analyzer')
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+
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+ # Dropdown for selecting a specialty
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+ specialty_options = specialties['Display Name'].unique()
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+ selected_specialty = st.selectbox('Select a Specialty', options=specialty_options)
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+
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+ # Display specialties matching the selected option or search keyword
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+ search_keyword = st.text_input('Or search for a keyword in specialties')
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+ if search_keyword:
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+ filtered_specialties = specialties[specialties.apply(lambda row: row.astype(str).str.contains(search_keyword, case=False).any(), axis=1)]
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+ else:
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+ filtered_specialties = specialties[specialties['Display Name'] == selected_specialty]
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+
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+ st.dataframe(filtered_specialties)
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+
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+ # Function to find and process text files with two-letter names
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+ def process_state_files(specialty_code):
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+ files = glob.glob('./*.txt')
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+ state_files = [file for file in files if len(os.path.basename(file).split('.')[0]) == 2]
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+ results = []
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+
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+ for file in state_files:
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+ state_df = pd.read_csv(file, names=['Code', 'Grouping', 'Classification', 'Specialization', 'Definition', 'Notes', 'Display Name', 'Section'])
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+ filtered_df = state_df[state_df['Code'] == specialty_code]
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+ if not filtered_df.empty:
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+ results.append((os.path.basename(file), filtered_df))
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+
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+ return results
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+
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+ # Show DataFrame UI for files matching the specialty code in the selected state
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+ if st.button('Analyze Text Files for Selected Specialty'):
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+ specialty_code = specialties[specialties['Display Name'] == selected_specialty].iloc[0]['Code']
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+ state_data = process_state_files(specialty_code)
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+ if state_data:
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+ for state, df in state_data:
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+ st.subheader(f"Providers in {state} with Specialty '{selected_specialty}':")
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+ st.dataframe(df)
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+ else:
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+ st.write("No matching records found in text files for the selected specialty.")