Replace income categories with salary slider

app.py

@@ -8,7 +8,7 @@ from src.assumptions import (
     BASELINE,
     BASELINE_REFERENCE_OPTIONS,
     DATA_QUALITY_NOTES,
-    INCOME_THRESHOLD_LABELS,
+    INCOME_SLIDER_MAX_UAH,
     SALARY_ANCHORS_UAH,
     SOURCE_LINKS,
 )
@@ -30,10 +30,6 @@ def title_label(value: str) -> str:
     return value.replace("_", " ").title()
 
 
-def income_label(value: str) -> str:
-    return INCOME_THRESHOLD_LABELS[value]
-
-
 def format_percent(value: int | float) -> str:
     if value == 0:
         return "0%"
@@ -122,17 +118,23 @@ with st.sidebar:
             175,
             help="Interpolates a demo height-distribution coefficient.",
         )
-        income_level = st.selectbox(
-            "Income threshold",
-            ["any", "above_median", "top_25", "top_10"],
-            format_func=income_label,
+        income_min_uah = st.slider(
+            "Minimum monthly income, UAH",
+            min_value=0,
+            max_value=INCOME_SLIDER_MAX_UAH,
+            value=30_000,
+            step=5_000,
             help=(
-                "Salary anchors: Work.ua current benchmark is about "
+                "Scenario salary threshold. 0 means no income filter. Salary anchors: Work.ua current benchmark is about "
                 f"{format_count(SALARY_ANCHORS_UAH['workua_current_average'])} UAH/month; "
                 f"KSE cites Work.ua January 2026 median at {format_count(SALARY_ANCHORS_UAH['kse_workua_jan_2026_median'])} UAH/month. "
-                "Top 25 and Top 10 are scenario thresholds, not official percentiles."
+                "Very high thresholds such as 200,000 or 500,000 UAH/month use demo tail assumptions, not official percentiles."
             ),
         )
+        st.caption(
+            "Selected income threshold: "
+            f"{'Any income' if income_min_uah == 0 else format_count(income_min_uah) + ' UAH/month'}"
+        )
         education_level = st.selectbox(
             "Education filter",
             ["any", "higher_education", "graduate_plus"],
@@ -208,7 +210,7 @@ criteria = Criteria(
     region_scope=region_scope,
     relationship_status=relationship_status,
     min_height_cm=min_height,
-    income_level=income_level,
+    income_min_uah=income_min_uah,
     education_level=education_level,
     children_status=children_status,
     future_children=future_children,
@@ -268,8 +270,8 @@ st.write(
     f"({format_count(BASELINE_REFERENCE_OPTIONS['sssu_jan_2022_total']['value'])})."
 )
 st.write(
-    f"`Above median` currently means roughly {format_count(SALARY_ANCHORS_UAH['workua_current_average'])} UAH/month "
-    "as a public job-market benchmark. Higher salary bands are scenario cutoffs until a validated percentile source is added."
+    f"The income slider uses {format_count(SALARY_ANCHORS_UAH['workua_current_average'])} UAH/month as the current public job-market benchmark. "
+    "High values such as 200,000 or 500,000 UAH/month are supported as scenario stress-test cutoffs, not official salary percentiles."
 )
 
 st.subheader("Data quality notes")

src/assumptions.py

@@ -36,12 +36,18 @@ SALARY_ANCHORS_UAH = {
     "workua_current_average": 28_600,
 }
 
-INCOME_THRESHOLD_LABELS = {
-    "any": "Any income",
-    "above_median": "Above median (about 28,600 UAH/month)",
-    "top_25": "Top 25 scenario (about 45,000 UAH/month)",
-    "top_10": "Top 10 scenario (about 70,000 UAH/month)",
-}
+INCOME_SLIDER_MAX_UAH = 500_000
+
+INCOME_CURVE_POINTS_UAH = [
+    (0, 1.0),
+    (SALARY_ANCHORS_UAH["official_pfu_2025_average"], 0.55),
+    (SALARY_ANCHORS_UAH["workua_current_average"], 0.42),
+    (45_000, 0.25),
+    (70_000, 0.10),
+    (100_000, 0.055),
+    (200_000, 0.015),
+    (500_000, 0.002),
+]
 
 SOURCE_LINKS = [
     {
@@ -108,13 +114,6 @@ HEIGHT_FACTORS = {
     190: 0.04,
 }
 
-INCOME_FACTORS = {
-    "any": 1.0,
-    "above_median": 0.42,
-    "top_25": 0.25,
-    "top_10": 0.10,
-}
-
 EDUCATION_FACTORS = {
     "any": 1.0,
     "higher_education": 0.38,

src/model_pool.py

@@ -11,7 +11,7 @@ from .assumptions import (
     FUTURE_CHILDREN_FACTORS,
     HEIGHT_FACTORS,
     HOUSING_FACTORS,
-    INCOME_FACTORS,
+    INCOME_CURVE_POINTS_UAH,
     LANGUAGE_FACTORS,
     MILITARY_STATUS_FACTORS,
     PETS_FACTORS,
@@ -32,7 +32,7 @@ class Criteria:
     region_scope: str
     relationship_status: str
     min_height_cm: int
-    income_level: str
+    income_min_uah: int
     education_level: str
     children_status: str
     future_children: str
@@ -87,6 +87,22 @@ def height_factor(min_height_cm: int) -> float:
     return HEIGHT_FACTORS[lower] + ratio * (HEIGHT_FACTORS[upper] - HEIGHT_FACTORS[lower])
 
 
+def income_factor(income_min_uah: int) -> float:
+    points = sorted(INCOME_CURVE_POINTS_UAH)
+    if income_min_uah <= points[0][0]:
+        return points[0][1]
+    if income_min_uah >= points[-1][0]:
+        return points[-1][1]
+
+    lower = max(point for point in points if point[0] <= income_min_uah)
+    upper = min(point for point in points if point[0] >= income_min_uah)
+    if lower == upper:
+        return lower[1]
+
+    ratio = (income_min_uah - lower[0]) / (upper[0] - lower[0])
+    return lower[1] + ratio * (upper[1] - lower[1])
+
+
 def model_factors(criteria: Criteria) -> list[tuple[str, float]]:
     return [
         ("Target population", TARGET_POPULATION_FACTORS[criteria.target_population]),
@@ -94,7 +110,7 @@ def model_factors(criteria: Criteria) -> list[tuple[str, float]]:
         ("Region scope", REGION_FACTORS[criteria.region_scope]),
         ("Relationship status", RELATIONSHIP_STATUS_FACTORS[criteria.relationship_status]),
         ("Minimum height", height_factor(criteria.min_height_cm)),
-        ("Income threshold", INCOME_FACTORS[criteria.income_level]),
+        ("Minimum income", income_factor(criteria.income_min_uah)),
         ("Education filter", EDUCATION_FACTORS[criteria.education_level]),
         ("Children status", CHILDREN_STATUS_FACTORS[criteria.children_status]),
         ("Future children", FUTURE_CHILDREN_FACTORS[criteria.future_children]),