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33203e9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import re
import unicodedata
import pandas as pd
def limpar_texto(texto):
if pd.isna(texto):
return ""
texto = str(texto).lower().strip()
texto = unicodedata.normalize("NFKD", texto)
texto = "".join(c for c in texto if not unicodedata.combining(c))
texto = re.sub(r"[\n\r\t]", " ", texto)
texto = re.sub(r"[^a-z0-9\s]", " ", texto)
texto = re.sub(r"\s+", " ", texto).strip()
return texto
def mapear_categoria(cat):
cat = limpar_texto(cat)
if "acai" in cat:
return "acai"
if "pastel" in cat or "pastel de pizza" in cat:
return "pastel"
if "pizza" in cat:
return "pizza"
if "hamburg" in cat or "burger" in cat:
return "hamburguer"
if "sushi" in cat or "japones" in cat or "oriental" in cat:
return "japones"
if "suco" in cat:
return "suco"
if "bebida" in cat or "refrigerante" in cat or "refri" in cat:
return "bebida"
return cat
def inferir_categoria_consulta(query):
q = limpar_texto(query)
if "acai" in q:
return "acai"
if "pastel" in q or "pastel de pizza" in q:
return "pastel"
if "pizza" in q:
return "pizza"
if "hamburguer" in q or "burger" in q or "x bacon" in q:
return "hamburguer"
if "sushi" in q or "temaki" in q:
return "japones"
if "suco" in q:
return "suco"
if "coca" in q or "refrigerante" in q or "refri" in q:
return "bebida"
return None
def bonus_lexical(query, *texts):
q = limpar_texto(query)
referencias = [limpar_texto(texto) for texto in texts if texto]
bonus = 0.0
for termo in q.split():
if any(termo in referencia for referencia in referencias):
bonus += 0.03
return bonus
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