Update app.py

app.py

@@ -72,44 +72,40 @@ def display_tiles(df, cols):
   
 # Function to detect the format and standardize the data  
 def standardize_data(df):  
-    # Check for the first format  
-    if 'Issued Date' in df.columns and 'Expiry Date' in df.columns:  
-        required_columns = [  
-            'Issued Date', 'Expiry Date', 'Cert. No', 'Company Name', 'Address', 'Region',   
-            'Factory Type', 'Contact', 'Phone', 'E-mail', 'Status', 'Member Since'  
-        ]  
-        # Standardize column names to a common format  
+    format_1_columns = {'Issued Date', 'Expiry Date', 'Cert. No', 'Company Name', 'Address', 'Region',   
+                        'Factory Type', 'Contact', 'Phone', 'E-mail', 'Status', 'Member Since'}  
+    format_2_columns = {'Name', 'Address', 'Tel', 'Cuisine', 'Expiry DateDD/MM/YY', 'Location', 'Restaurant Type',   
+                        'Website', 'Directions'}  
+      
+    if format_1_columns.issubset(df.columns):  
         df = df.rename(columns={  
             'Issued Date': 'Issued Date',  
             'Expiry Date': 'Expiry Date',  
             'Cert. No': 'Cert No',  
-            'Company Name': 'Name',  
+            'Company Name': 'Company Name',  
             'Address': 'Address',  
-            'Region': 'Location',  
-            'Factory Type': 'Restaurant Type',  
+            'Region': 'Region',  
+            'Factory Type': 'Factory Type',  
             'Contact': 'Contact',  
-            'Phone': 'Tel',  
+            'Phone': 'Phone',  
             'E-mail': 'E-mail',  
             'Status': 'Status',  
             'Member Since': 'Member Since'  
         })  
-    # Check for the second format  
-    elif 'Name' in df.columns and 'Expiry DateDD/MM/YY' in df.columns:  
-        required_columns = [  
-            'Name', 'Address', 'Tel', 'Cuisine', 'Expiry DateDD/MM/YY', 'Location', 'Restaurant Type', 'Website', 'Directions'  
-        ]  
-        # Standardize column names to a common format  
+        required_columns = list(format_1_columns)  
+    elif format_2_columns.issubset(df.columns):  
         df = df.rename(columns={  
-            'Name': 'Name',  
+            'Name': 'Company Name',  
             'Address': 'Address',  
-            'Tel': 'Tel',  
-            'Cuisine': 'Cuisine',  
+            'Tel': 'Phone',  
+            'Cuisine': 'Factory Type',  
             'Expiry DateDD/MM/YY': 'Expiry Date',  
-            'Location': 'Location',  
-            'Restaurant Type': 'Restaurant Type',  
+            'Location': 'Region',  
+            'Restaurant Type': 'Factory Type',  
             'Website': 'Website',  
             'Directions': 'Directions'  
         })  
+        required_columns = list(format_1_columns)  # Use the same required columns for consistency  
     else:  
         st.error("Unsupported file format")  
         return None, []  
@@ -230,10 +226,10 @@ else:
                 company_name_filter = st.text_input("Company Name contains")  
             with col2:  
                 # Filter by Location  
-                location_filter = st.multiselect("Location", df['Location'].drop_duplicates())  
+                location_filter = st.multiselect("Location", df['Region'].drop_duplicates())  
             with col3:  
                 # Filter by Restaurant Type  
-                restaurant_type_filter = st.multiselect("Restaurant Type", df['Restaurant Type'].drop_duplicates())  
+                restaurant_type_filter = st.multiselect("Factory Type", df['Factory Type'].drop_duplicates())  
             with col4:  
                 # Filter by Expiry Date  
                 expiry_date_filter = st.date_input("Expiry Date", [])  
@@ -242,11 +238,11 @@ else:
             filtered_df = df.copy()  
   
             if company_name_filter:  
-                filtered_df = filtered_df[filtered_df['Name'].str.contains(company_name_filter, case=False, na=False)]  
+                filtered_df = filtered_df[filtered_df['Company Name'].str.contains(company_name_filter, case=False, na=False)]  
             if location_filter:  
-                filtered_df = filtered_df[filtered_df['Location'].isin(location_filter)]  
+                filtered_df = filtered_df[filtered_df['Region'].isin(location_filter)]  
             if restaurant_type_filter:  
-                filtered_df = filtered_df[filtered_df['Restaurant Type'].isin(restaurant_type_filter)]  
+                filtered_df = filtered_df[filtered_df['Factory Type'].isin(restaurant_type_filter)]  
             if expiry_date_filter:  
                 if len(expiry_date_filter) == 1:  
                     filtered_df = filtered_df[filtered_df['Expiry Date'] == expiry_date_filter[0]]