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):")

    if user_input:
        # Validate input
        if "irl" in user_input.lower():
            try:
                application_number = int("".join(filter(str.isdigit, user_input.lower().split("irl")[-1])))
                if len(str(application_number)) < 8:
                    st.warning("Please enter a valid application number with at least 8 digits after IRL.")
                    return
            except ValueError:
                st.error("Invalid input after IRL. Please enter only digits.")
                return
        else:
            if not user_input.isdigit() or len(user_input) < 8:
                st.warning("Please enter at least 8 digits for your VISA application number.")
                return
            elif len(user_input) > 8:
                st.warning("The application number cannot exceed 8 digits. Please correct your input.")
                return
            application_number = int(user_input)

        # Search for the application number
        result = df[df['Application Number'] == application_number]

        if not result.empty:
            decision = result.iloc[0]['Decision']
            if decision.lower() == 'refused':
                st.error(f"Application Number: {application_number}\n\nDecision: **Refused**")
            elif decision.lower() == 'approved':
                st.success(f"Application Number: {application_number}\n\nDecision: **Approved**")
            else:
                st.info(f"Application Number: {application_number}\n\nDecision: **{decision}**")
        else:
            st.warning(f"No record found for Application Number: {application_number}.")

            # 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")
                st.table(nearest_records.reset_index(drop=True))
            else:
                st.info("No nearest application numbers found.")