Fix moest-np#84 Schools are not plotted in map in streamlit app

Few additional features have been added in the codebase and improvise in the readability of streamlit codebase

1) Schools will now be plotted on the map alongside centers, using a different colored marker.

2) The list serial number now starts from 1 instead of 0.
- In "filter_data" function, when filtering the DataFrame based on the selected type and value, the filter was being applied directly to the DataFrame without resetting the index.

3) Removed thousand separators from scode and cscode.

4) Changed the marker style for better aesthetics.

app.py

@@ -6,18 +6,16 @@
 import streamlit as st
 from streamlit_folium import st_folium
 
-
-#Page Setup
+# Page Setup
 st.set_page_config(
-   page_title="MOEST Exam Center Calculator",
-   page_icon=":school:",
-#    page_icon="https://avatars.githubusercontent.com/u/167545222?s=200&v=4", # official logo
-   layout="wide",
-   initial_sidebar_state="expanded",
+    page_title="MOEST Exam Center Calculator",
+    page_icon=":school:",
+    layout="wide",
+    initial_sidebar_state="expanded",
     theme="light",  # Default theme is light
 )
 
-#  Custom CSS 
+# Custom CSS 
 custom_css = """
 <style>
 .st-ag.st-e4.st-e5 {
@@ -41,11 +39,12 @@
 if 'filter_value' not in st.session_state:
     st.session_state.filter_value = None
 
-#Maps setup
-m = folium.Map(location=[27.7007, 85.3001], zoom_start=12, )
-fg = folium.FeatureGroup(name="Allocated Centers")
+# Maps setup
+m = folium.Map(location=[27.7007, 85.3001], zoom_start=12)
+fg_centers = folium.FeatureGroup(name="Allocated Centers")
+fg_schools = folium.FeatureGroup(name="Schools")
 
-#Sidebar
+# Sidebar
 with st.sidebar:
 
     add_side_header = st.sidebar.title("Random Center Calculator")
@@ -66,8 +65,8 @@
     "School Center Distance",
     "View School Data",
     "View Centers Data",
-    "View Pref Data" 
-    ])
+    "View Pref Data"
+])
 
 tab1.subheader("School Center")
 tab2.subheader("School Center Distance")
@@ -126,15 +125,15 @@ def run_center_randomizer(schools_tsv, centers_tsv, prefs_tsv):
     cmd = f"python school_center.py {schools_tsv} {centers_tsv} {prefs_tsv}"
     subprocess.run(cmd, shell=True)
 
-#Function to filter the data
+# Function to filter the data
 def filter_data(df, filter_type, filter_value):
     if filter_type in df.columns:
         df = df[df[filter_type] == filter_value]
         return df
     else:
         st.error(f"{filter_type} is not a valid column in the DataFrame.")
-        return pd.DataFrame() 
-    
+        return pd.DataFrame()
+
 # Run logic after the button is clicked
 if calculate:
     st.session_state.calculate_clicked = True
@@ -162,7 +161,7 @@ def save_file_to_temp(file_obj):
             os.unlink(centers_path)
             os.unlink(prefs_path)
 
-            # Set the paths for the output files[UPDATE LATER TO BE FLEXIBLE]
+            # Set the paths for the output files [UPDATE LATER TO BE FLEXIBLE]
             school_center_file = "results/school-center.tsv"
             school_center_distance_file = "results/school-center-distance.tsv"
 
@@ -189,51 +188,65 @@ def save_file_to_temp(file_obj):
 
         # Display an input field based on the selected filter type
         if st.session_state.filter_type:
-         if st.session_state.filter_type == 'school':
-          # Create filter options with school name and code
-          filter_options = [f"{code} | {name}" for name, code in zip(df_school_center['school'].unique(), df_school_center['scode'].unique())]
-
-         elif st.session_state.filter_type == 'center':
-          # Create filter options with center name and code
-          filter_options = [f"{code} | {name}" for name, code in zip(df_school_center['center'].unique(), df_school_center['cscode'].unique())]
-
-         # Display a selectbox for selection
-         st.session_state.filter_value = tab1.selectbox(f"Select a value for {st.session_state.filter_type}:", filter_options)
-
-         # Split the selected value to extract name and code
-         code, name = st.session_state.filter_value.split(' | ')
-
-         # Filter the DataFrame based on the selected type and value
-         filtered_df = filter_data(df_school_center, st.session_state.filter_type, name)
-
-        if st.session_state.filter_value:
-            # Remove thousand separator comma in scode and cscode 
-            styled_df  = filtered_df.style.format({
-              "cscode": lambda x: '{:.0f}'.format(x),
-               "scode": lambda x: '{:.0f}'.format(x)
-            })
-            tab1.dataframe(styled_df , hide_index=True)
-            tab1.subheader('Map')
-            tab1.divider()
-            for index, center in filtered_df.iterrows():
-                fg.add_child(
-                    folium.Marker(
-                        location=[center.center_lat, center.center_long],
-                        popup=f"{(center.center).title()}\nAllocation: {center.allocation}",
-                        tooltip=f"{center.center}",
-                        icon=folium.Icon(color="red")
-                    )
-                )
-            m.add_child(fg)
-            with tab1:
-                st_folium( m, width=1200, height=400 )
-
+            if st.session_state.filter_type == 'school':
+                # Create filter options with school name and code
+                filter_options = [f"{code} | {name}" for name, code in zip(df_school_center['school'].unique(), df_school_center['scode'].unique())]
+
+            elif st.session_state.filter_type == 'center':
+                # Create filter options with center name and code
+                filter_options = [f"{code} | {name}" for name, code in zip(df_school_center['center'].unique(), df_school_center['cscode'].unique())]
+
+            # Display a selectbox for selection
+            st.session_state.filter_value = tab1.selectbox(f"Select a value for {st.session_state.filter_type}:", filter_options)
+
+            # Split the selected value to extract name and code
+            code, name = st.session_state.filter_value.split(' | ')
+
+            # Filter the DataFrame based on the selected type and value
+            filtered_df = filter_data(df_school_center, st.session_state.filter_type, name)
+
+            if st.session_state.filter_value:
+                # Remove thousand separator comma in scode and cscode 
+                filtered_df['scode'] = filtered_df['scode'].astype(int)
+                filtered_df['cscode'] = filtered_df['cscode'].astype(int)
+
+                # Reset index after filtering
+                filtered_df.reset_index(drop=True, inplace=True)
+
+                # Add serial number starting from 1
+                filtered_df.index = range(1, len(filtered_df) + 1)
+
+                # Plot schools and centers on the map
+                for index, center in filtered_df.iterrows():
+                    if st.session_state.filter_type == 'school':
+                        fg_schools.add_child(
+                            folium.Marker(
+                                location=[center.school_lat, center.school_long],
+                                popup=f"{(center.school).title()}\nAllocation: {center.allocation}",
+                                tooltip=f"{center.school}",
+                                icon=folium.Icon(color="blue", icon="university", prefix='fa')
+                            )
+                        )
+                    elif st.session_state.filter_type == 'center':
+                        fg_centers.add_child(
+                            folium.Marker(
+                                location=[center.center_lat, center.center_long],
+                                popup=f"{(center.center).title()}\nAllocation: {center.allocation}",
+                                tooltip=f"{center.center}",
+                                icon=folium.Icon(color="red", icon="heart", prefix='fa')
+                            )
+                        )
+                m.add_child(fg_centers)
+                m.add_child(fg_schools)
+                with tab1:
+                    st_folium(m, width=1200, height=400)
+
         tab1.divider()
         tab1.subheader('All Data')
         tab1.dataframe(df_school_center)
     else:
         tab1.info("No calculated data available.", icon="ℹ️")
-    
+
     if 'school_center_distance' in st.session_state.calculated_data:
         df = pd.read_csv(st.session_state.calculated_data['school_center_distance'], sep="\t")
         tab2.dataframe(df)