Update search.py (#11)

- Update search.py (d4a856db407dc38a138cfc01842d967e83090be6)

search.py

@@ -1,5 +1,17 @@
 import streamlit as st
 import pandas as pd
+import bisect
+
+def binary_search_nearest(df, target):
+    """Find the nearest values using binary search."""
+    application_numbers = df['Application Number'].tolist()
+    pos = bisect.bisect_left(application_numbers, target)
+
+    # Find the nearest neighbors
+    before = application_numbers[pos - 1] if pos > 0 else None
+    after = application_numbers[pos] if pos < len(application_numbers) else None
+
+    return before, after
 
 def search_application(df):
     user_input = st.text_input("Enter your Application Number (including IRL if applicable):")
@@ -24,8 +36,8 @@ def search_application(df):
                 return
             application_number = int(user_input)
 
-        # Check for the application number in the DataFrame
-        result = df[df['Application Number'] == str(application_number)]
+        # Search for the application number
+        result = df[df['Application Number'] == application_number]
 
         if not result.empty:
             decision = result.iloc[0]['Decision']
@@ -38,14 +50,25 @@ def search_application(df):
         else:
             st.warning(f"No record found for Application Number: {application_number}.")
 
-            # Find nearest application numbers
-            df['Application Number'] = df['Application Number'].astype(int)
-            df['Difference'] = abs(df['Application Number'] - application_number)
-            nearest_records = df.nsmallest(2, 'Difference')
+            # Find nearest application numbers using binary search
+            before, after = binary_search_nearest(df, application_number)
+
+            # Display nearest records
+            nearest_records = pd.DataFrame({
+                "Nearest Application": ["Before", "After"],
+                "Application Number": [before, after],
+                "Decision": [
+                    df[df['Application Number'] == before]['Decision'].values[0] if before else None,
+                    df[df['Application Number'] == after]['Decision'].values[0] if after else None
+                ],
+                "Difference": [
+                    application_number - before if before else None,
+                    after - application_number if after else None
+                ]
+            }).dropna()
 
             if not nearest_records.empty:
                 st.subheader("Nearest Application Numbers")
-                nearest_records['Nearest Application'] = ['Before', 'After']
-                st.table(nearest_records[['Nearest Application', 'Application Number', 'Decision', 'Difference']].reset_index(drop=True))
+                st.table(nearest_records.reset_index(drop=True))
             else:
                 st.info("No nearest application numbers found.")