File size: 2,794 Bytes
553d692 be2d971 d56f2c2 553d692 be2d971 553d692 be2d971 553d692 be2d971 553d692 be2d971 553d692 be2d971 553d692 be2d971 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
import streamlit as st
import pandas as pd
import os
import glob
# Load the provider specialty dataset CSV
@st.cache(allow_output_mutation=True)
def load_specialties(csv_file='Provider-Specialty.csv'):
return pd.read_csv(csv_file)
specialties = load_specialties()
# User interface for specialty selection
st.title('🔍 Provider Specialty Analyzer')
# Markdown outline for the fields description
st.markdown("""
## Specialty Fields Description 📋
- **Code**: Unique identifier for the specialty.
- **Grouping**: General category the specialty belongs to.
- **Classification**: More detailed classification within the grouping.
- **Specialization**: Further specialization details if applicable.
- **Definition**: Brief description of the specialty.
- **Notes**: Any additional notes or historical information.
- **Display Name**: The common name used to display the specialty.
- **Section**: The section of healthcare the specialty falls under.
""")
# Dropdown for selecting a specialty
specialty_options = specialties['Display Name'].unique()
selected_specialty = st.selectbox('Select a Specialty 📂', options=specialty_options)
# Display specialties matching the selected option or search keyword
search_keyword = st.text_input('Or search for a keyword in specialties 🔍')
if search_keyword:
filtered_specialties = specialties[specialties.apply(lambda row: row.astype(str).str.contains(search_keyword, case=False).any(), axis=1)]
else:
filtered_specialties = specialties[specialties['Display Name'] == selected_specialty]
st.dataframe(filtered_specialties)
# Function to find and process text files with two-letter names
def process_state_files(specialty_code):
files = glob.glob('./*.txt')
state_files = [file for file in files if len(os.path.basename(file).split('.')[0]) == 2]
results = []
for file in state_files:
state_df = pd.read_csv(file, names=['Code', 'Grouping', 'Classification', 'Specialization', 'Definition', 'Notes', 'Display Name', 'Section'])
filtered_df = state_df[state_df['Code'] == specialty_code]
if not filtered_df.empty:
results.append((os.path.basename(file).split('.')[0], filtered_df))
return results
# Show DataFrame UI for files matching the specialty code in the selected state
if st.button('Analyze Text Files for Selected Specialty 📊'):
specialty_code = specialties[specialties['Display Name'] == selected_specialty].iloc[0]['Code']
state_data = process_state_files(specialty_code)
if state_data:
for state, df in state_data:
st.subheader(f"Providers in {state} with Specialty '{selected_specialty}':")
st.dataframe(df)
else:
st.write("No matching records found in text files for the selected specialty. 🚫")
|