Create backup1.app.py

backup1.app.py

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+import streamlit as st
+import pandas as pd
+import os
+import glob
+
+# Load the provider specialty dataset CSV
+@st.cache_resource
+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')
+
+# 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), 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.")