from sentence_transformers import SentenceTransformer, util

MODELS = {
  "all-MiniLM-L6-v2": SentenceTransformer("all-MiniLM-L6-v2"),
  "multi-qa-MiniLM-L6-cos-v1": SentenceTransformer("multi-qa-MiniLM-L6-cos-v1"),
  "paraphrase-MiniLM-L3-v2": SentenceTransformer("paraphrase-MiniLM-L3-v2"),
  "all-mpnet-base-v2": SentenceTransformer("all-mpnet-base-v2"),
  "distilbert-base-nli-mean-tokens": SentenceTransformer("distilbert-base-nli-mean-tokens"),
}

def score_fit(text: str, goal: str, method: str) -> dict:
    results = {}
    for name, model in MODELS.items():
        emb1 = model.encode(text, convert_to_tensor=True)
        emb2 = model.encode(goal, convert_to_tensor=True)
        cos  = util.cos_sim(emb1, emb2).item()
        score = max(0, min(100, int((cos + 1) * 50)))
        results[name] = score
    return results