Feat: Add filters and map (#69)

Author: dhirajraut1

app.py

@@ -1,8 +1,10 @@
 import os
+import folium
 import tempfile
 import subprocess
 import pandas as pd
 import streamlit as st
+from streamlit_folium import st_folium
 
 #Page Setup
 st.set_page_config(
@@ -13,12 +15,28 @@
    initial_sidebar_state="expanded",
 )
 
+# Session setup
+if 'calculate_clicked' not in st.session_state:
+    st.session_state.calculate_clicked = False
+if 'calculation_completed' not in st.session_state:
+    st.session_state.calculation_completed = False
+if 'calculated_data' not in st.session_state:
+    st.session_state.calculated_data = {}
+if 'filter_type' not in st.session_state:
+    st.session_state.filter_type = None
+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")
+
 #Sidebar
 with st.sidebar:
 
-    add_side_header = st.sidebar.title(
-    "Random Center Calculator"
-    )
+    add_side_header = st.sidebar.title("Random Center Calculator")
+
     schools_file = st.sidebar.file_uploader("Upload School/College file", type="tsv")
     centers_file = st.sidebar.file_uploader("Upload Centers file", type="tsv")
     prefs_file = st.sidebar.file_uploader("Upload Preferences file", type="tsv")
@@ -65,50 +83,99 @@ 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
+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() 
+    
 # Run logic after the button is clicked
 if calculate:
-
+    st.session_state.calculate_clicked = True
+    
     def save_file_to_temp(file_obj):
         with tempfile.NamedTemporaryFile(delete=False) as temp_file:
             file_obj.seek(0)  # Go to the start of the file
             temp_file.write(file_obj.read())
             return temp_file.name
 
-    # Ensure all files are uploaded
-    if schools_file and centers_file and prefs_file:
-        schools_path = save_file_to_temp(schools_file)
-        centers_path = save_file_to_temp(centers_file)
-        prefs_path = save_file_to_temp(prefs_file)
-
-        # Run the program with the temporary file paths
-        run_center_randomizer(schools_path, centers_path, prefs_path)
-
-        # Set the paths for the output files
-        school_center_file = "results/school-center.tsv"
-        school_center_distance_file = "results/school-center-distance.tsv"
-
-        # Delete the temporary files after use
-        os.unlink(schools_path)
-        os.unlink(centers_path)
-        os.unlink(prefs_path)
-
-        # Display data in the specified tabs
-        if school_center_file:
-            df = pd.read_csv(school_center_file, sep="\t")
-            tab1.dataframe(df)
-        else:
-            tab1.error("School Center file not found.")
-        
-        if school_center_distance_file:
-            df = pd.read_csv(school_center_distance_file, sep="\t")
-            tab2.dataframe(df)
+    if st.session_state.calculate_clicked:
+        # Ensure all files are uploaded
+        if schools_file and centers_file and prefs_file:
+            schools_path = save_file_to_temp(schools_file)
+            centers_path = save_file_to_temp(centers_file)
+            prefs_path = save_file_to_temp(prefs_file)
+
+            # Run the program with the temporary file paths
+            run_center_randomizer(schools_path, centers_path, prefs_path)
+            st.toast("Calculation successful!", icon="🎉")
+            st.session_state.calculation_completed = True
+
+            # Delete the temporary files after use
+            os.unlink(schools_path)
+            os.unlink(centers_path)
+            os.unlink(prefs_path)
+
+            # 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"
+
+            # Store calculated data in session state
+            if school_center_file and school_center_distance_file:
+                st.session_state.calculated_data['school_center'] = school_center_file
+                st.session_state.calculated_data['school_center_distance'] = school_center_distance_file
+            else:
+                tab1.error("Calculated data not found", icon="🚨")
+
         else:
-            tab2.error("School Center Distance file not found.")
+            st.sidebar.error("Please upload all required files.", icon="🚨")
 
-        st.toast("Calculation successful!", icon="🎉")
-        
-    else:
-        st.sidebar.error("Please upload all required files.", icon="🚨")
-else:
+elif not st.session_state.calculate_clicked:
     tab1_msg = tab1.info("Results will be shown only after the calculation is completed.", icon="ℹ️")
     tab2_msg = tab2.info("Results will be shown only after the calculation is completed.", icon="ℹ️")
+
+if st.session_state.calculate_clicked and st.session_state.calculation_completed:
+    # Display data from session state
+    if 'school_center' in st.session_state.calculated_data:
+        df_school_center = pd.read_csv(st.session_state.calculated_data['school_center'], sep="\t")
+        allowed_filter_types = ['scode', 'school', 'cscode', 'center']
+        st.session_state.filter_type = tab1.radio("Choose a filter type:", allowed_filter_types)
+
+        # Display an input field based on the selected filter type
+        if st.session_state.filter_type:
+            st.session_state.filter_value = tab1.selectbox(f"Select a value for {st.session_state.filter_type}:", df_school_center[st.session_state.filter_type].unique())
+        # Filter the DataFrame based on the selected filter type and value
+        if st.session_state.filter_value:
+            filtered_df = filter_data(df_school_center, st.session_state.filter_type, st.session_state.filter_value)
+            tab1.dataframe(filtered_df)
+            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 )
+        
+        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)
+    else:
+        tab2.error("School Center Distance file not found.")
+elif st.session_state.calculate_clicked and not st.session_state.calculated_data:
+    tab1.error("School Center data not found in session state.")

requirements.txt

@@ -5,3 +5,5 @@ pytest-sugar==1.0.0
 pre-commit==3.6.0
 pytest-cov==5.0.0
 streamlit==1.33.0
+streamlit_folium==0.19.1
+folium==0.16.0

school_center.py

@@ -264,6 +264,8 @@ def get_output_filename():
                               "cscode", 
                               "center", 
                               "center_address", 
+                              "center_lat",
+                              "center_long",
                               "allocation", 
                               "distance_km"])
 
@@ -321,6 +323,8 @@ def get_output_filename():
                                       c['cscode'], 
                                       c['name'],
                                       c['address'], 
+                                      c['lat'],
+                                      c['long'],
                                       allocations[s['scode']][c['cscode']], 
                                       c['distance_km']])