Create backup2.app.py

backup2.app.py

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+import streamlit as st
+import pandas as pd
+import os
+import glob
+
+# Cache the loading of specialties and state files for efficiency
+@st.cache_resource
+def load_specialties(csv_file='Provider-Specialty.csv'):
+    return pd.read_csv(csv_file)
+
+@st.cache_resource
+def find_state_files():
+    return [file for file in glob.glob('./*.csv') if len(os.path.basename(file).split('.')[0]) == 2]
+
+# Load the provider specialty dataset
+specialties = load_specialties()
+
+# User interface for specialty selection
+st.title('Provider Specialty Analyzer 📊')
+
+# Markdown outline with emojis for specialty fields
+st.markdown('''
+## Specialty Fields Description 📝
+- **Code**: Unique identifier for the specialty 🆔
+- **Grouping**: General category of the specialty 🏷️
+- **Classification**: Specific type of practice within the grouping 🎯
+- **Specialization**: Further refinement of the classification if applicable 🔍
+- **Definition**: Brief description of the specialty 📖
+- **Notes**: Additional information or updates about the specialty 🗒️
+- **Display Name**: Common name of the specialty 🏷️
+- **Section**: Indicates the section of healthcare it belongs to 📚
+''')
+
+# 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)
+
+# State selection UI with MN as the default option for testing
+state_files = find_state_files()
+state_options = sorted([os.path.basename(file).split('.')[0] for file in state_files])
+selected_state = st.selectbox('Select a State (optional) 🗺️', options=state_options, index=state_options.index('MN') if 'MN' in state_options else 0)
+use_specific_state = st.checkbox('Filter by selected state only? ✅', value=True)
+
+# Function to process state files and match taxonomy codes
+def process_files(specialty_codes, specific_state='MN'):
+    results = []
+    file_to_process = f'./{specific_state}.csv' if use_specific_state else state_files
+    
+    for file in [file_to_process] if use_specific_state else state_files:
+        state_df = pd.read_csv(file, header=None)  # Assume no header for simplicity
+        for code in specialty_codes:
+            # Filter rows where the 48th column matches the specialty code
+            filtered_df = state_df[state_df[47] == code]
+            if not filtered_df.empty:
+                results.append((os.path.basename(file).replace('.csv', ''), filtered_df))
+    
+    return results
+
+# Button to initiate analysis
+if st.button('Analyze Text Files for Selected Specialty 🔍'):
+    specialty_codes = filtered_specialties['Code'].unique()
+    state_data = process_files(specialty_codes, selected_state if use_specific_state else 'MN')
+    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.")