Add Doris Kitchen household recipe app

home/doris-kitchen/app/services/scoring.py

@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+import re
+from typing import Any
+
+
+def normalize_token(value: str) -> str:
+    value = re.sub(r"[^a-z0-9]+", " ", value.lower())
+    return " ".join(part for part in value.split() if part)
+
+
+def extract_ingredients(payload: dict[str, Any]) -> list[str]:
+    out: list[str] = []
+    seen: set[str] = set()
+    for item in payload.get("items") or []:
+        name = normalize_token(str(item.get("name") or ""))
+        if not name or name in seen:
+            continue
+        seen.add(name)
+        out.append(name)
+    return out
+
+
+def extract_tags(payload: dict[str, Any]) -> list[str]:
+    return [normalize_token(str(tag)) for tag in (payload.get("tags") or []) if normalize_token(str(tag))]
+
+
+def score_recipe(query: str, payload: dict[str, Any], profile: dict[str, Any]) -> tuple[int, list[str]]:
+    score = 50
+    reasons: list[str] = []
+    haystack = " ".join(
+        [
+            normalize_token(str(payload.get("name") or "")),
+            normalize_token(str(payload.get("description") or "")),
+            " ".join(extract_ingredients(payload)),
+            " ".join(extract_tags(payload)),
+        ]
+    )
+    query_terms = [term for term in normalize_token(query).split() if len(term) > 2]
+    if query_terms:
+        matched = sum(1 for term in query_terms if term in haystack)
+        score += matched * 6
+        if matched:
+            reasons.append(f"Matches query terms: {matched}")
+    ingredients = extract_ingredients(payload)
+    tags = extract_tags(payload)
+    approved_ingredients = {normalize_token(k): int(v) for k, v in (profile.get("approved_ingredients") or {}).items()}
+    rejected_ingredients = {normalize_token(k): int(v) for k, v in (profile.get("rejected_ingredients") or {}).items()}
+    approved_tags = {normalize_token(k): int(v) for k, v in (profile.get("approved_tags") or {}).items()}
+    rejected_tags = {normalize_token(k): int(v) for k, v in (profile.get("rejected_tags") or {}).items()}
+    soft_blacklist = {normalize_token(item.get("name", "")) for item in (profile.get("soft_blacklist") or []) if item.get("enabled")}
+    for term in sorted(soft_blacklist):
+        if term and term in haystack:
+            score -= 30
+            reasons.append(f"Soft blacklist term: {term}")
+    liked_hits = 0
+    for ingredient in ingredients:
+        liked_hits += approved_ingredients.get(ingredient, 0)
+        score -= min(rejected_ingredients.get(ingredient, 0) * 7, 18)
+    for tag in tags:
+        liked_hits += approved_tags.get(tag, 0)
+        score -= min(rejected_tags.get(tag, 0) * 5, 12)
+    if liked_hits:
+        score += min(liked_hits * 3, 18)
+        reasons.append("Aligned with prior approvals")
+    title = str(payload.get("name") or "")
+    if len(title.split()) <= 10:
+        score += 3
+        reasons.append("Clear short title")
+    total_time = payload.get("time")
+    if isinstance(total_time, int):
+        if total_time <= 45:
+            score += 6
+            reasons.append("Weeknight-friendly time")
+        elif total_time >= 120:
+            score -= 6
+    description = str(payload.get("description") or "")
+    if description.count("\n") >= 3:
+        score += 4
+        reasons.append("Structured instructions")
+    return max(0, min(100, score)), reasons