Update app.py

app.py

@@ -3,19 +3,20 @@ import pandas as pd
 import os
 import glob
 
-# Cache the loading of specialties and state files for efficiency
-@st.cache_resource
+# Cache the loading of specialties for efficiency
+@st.cache(allow_output_mutation=True)
 def load_specialties(csv_file='Provider-Specialty.csv'):
     return pd.read_csv(csv_file)
 
-@st.cache_resource
+# Cache the finding of state files to avoid repeated file system access
+@st.cache(allow_output_mutation=True)
 def find_state_files():
     return [file for file in glob.glob('./*.csv') if len(os.path.basename(file).split('.')[0]) == 2]
 
 specialties = load_specialties()
 
-# UI for specialty selection
-st.title('Provider Specialty Analyzer 📊')
+# UI for specialty selection with an engaging title
+st.title('Provider Specialty Analyzer with Code Grouping and Classification 📊')
 
 st.markdown('''
 ## Specialty Fields Description 📝
@@ -29,9 +30,11 @@ st.markdown('''
 - **Section**: Indicates the section of healthcare it belongs to 📚
 ''')
 
+# Allows users to select or search for a specialty
 specialty_options = specialties['Display Name'].unique()
 selected_specialty = st.selectbox('Select a Specialty 🩺', options=specialty_options)
 
+# Keyword search functionality
 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)]
@@ -40,31 +43,34 @@ else:
 
 st.dataframe(filtered_specialties)
 
-# State selection with MN as default for testing
+# State selection UI with default selection 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)
+
+# Checkbox to filter by selected state only
 use_specific_state = st.checkbox('Filter by selected state only? ✅', value=True)
 
+# Process files based on specialty codes and state selection
 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)  # Assuming no header for simplicity
-        for code in specialty_codes:
-            filtered_df = state_df[state_df[47].isin(specialty_codes)]  # Match against 48th column
-            if not filtered_df.empty:
-                results.append((os.path.basename(file).replace('.csv', ''), filtered_df))
+        filtered_df = state_df[state_df[47].isin(specialty_codes)]  # Assuming the code is in the 48th column
+        if not filtered_df.empty:
+            results.append((os.path.basename(file).replace('.csv', ''), filtered_df))
     
     return results
 
+# Button to initiate the analysis
 if st.button('Analyze Text Files for Selected Specialty 🔍'):
     specialty_codes = filtered_specialties['Code'].tolist()
-    state_data = process_files(specialty_codes, selected_state if use_specific_state else 'MN')
+    state_data = process_files(specialty_codes, selected_state if use_specific_state else None)
     if state_data:
         for state, df in state_data:
-            st.subheader(f"Providers in {state} with Specialties related to '{search_keyword}':")
+            st.subheader(f"Providers in {state} with Specialties related to '{search_keyword or selected_specialty}':")
             st.dataframe(df)
     else:
         st.write("No matching records found in text files for the selected specialties.")