| """ |
| title: Smart Matcher |
| author: nerdur |
| version: 0.1 |
| description: Sparuje stavke iz dvije liste i vraća ranking s confidence scorom. |
| """ |
|
|
| from pydantic import BaseModel |
| from typing import Optional |
| import difflib |
|
|
|
|
| class Tools: |
| class Valves(BaseModel): |
| threshold: float = 0.3 |
|
|
| def __init__(self): |
| self.valves = self.Valves() |
|
|
| def match_items( |
| self, |
| list_a: str, |
| list_b: str, |
| __user__: Optional[dict] = None, |
| ) -> str: |
| """ |
| Match items between two comma-separated lists and return ranked pairs with confidence scores. |
| :param list_a: First list of items, comma-separated |
| :param list_b: Second list of items, comma-separated |
| :return: Markdown table with matched pairs and confidence scores |
| """ |
| items_a = [x.strip() for x in list_a.split(",") if x.strip()] |
| items_b = [x.strip() for x in list_b.split(",") if x.strip()] |
|
|
| if not items_a or not items_b: |
| return "❌ Both lists must have at least one item." |
|
|
| results = [] |
| used_b = set() |
|
|
| for a in items_a: |
| best_match = None |
| best_score = 0.0 |
| for b in items_b: |
| if b in used_b: |
| continue |
| score = difflib.SequenceMatcher(None, a.lower(), b.lower()).ratio() |
| if score > best_score: |
| best_score = score |
| best_match = b |
| if best_match and best_score >= self.valves.threshold: |
| results.append((a, best_match, best_score)) |
| used_b.add(best_match) |
| else: |
| results.append((a, "— no match —", 0.0)) |
|
|
| results.sort(key=lambda x: x[2], reverse=True) |
|
|
| lines = ["| | Item A | Item B | Confidence |", "|---|---|---|---|"] |
| for i, (a, b, score) in enumerate(results): |
| star = "⭐" if i == 0 and score > 0 else "" |
| pct = f"{int(score * 100)}%" |
| lines.append(f"| {star} | **{a}** | {b} | {pct} |") |
|
|
| return "\n".join(lines) |
|
|
