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from fastapi import FastAPI
import spacy
import json
import difflib
app = FastAPI()
# Ladda svensk spaCy-modell
nlp = spacy.load("sv_core_news_sm")
# Ladda entiteter från entities.json
with open("entities.json") as f:
entities = json.load(f)
ITEMS = set(entities["items"])
COLORS = set(entities["colors"])
PRICES = set(entities["prices"])
def correct_spelling(word, valid_words, threshold=0.8):
"""Korrigera stavfel genom att hitta närmaste match i valid_words."""
matches = difflib.get_close_matches(word, valid_words, n=1, cutoff=threshold)
return matches[0] if matches else word
@app.post("/parse")
async def parse_user_request(request: str):
if not request or len(request) > 200:
return {"error": "Ogiltig eller för lång begäran"}
try:
# Analysera text med spaCy
doc = nlp(request)
# Extrahera entiteter
entities = {}
for token in doc:
text = token.text.lower()
# Prioritera definierade varor med stavfelskorrigering
corrected_text = correct_spelling(text, ITEMS)
if corrected_text in ITEMS:
entities["VARA"] = corrected_text
elif token.pos_ == "NOUN" and not entities.get("VARA"):
entities["VARA"] = corrected_text
# Identifiera färger och priser
elif text in COLORS:
entities["FÄRG"] = text
elif text in PRICES:
entities["PRIS"] = text
# Om ingen vara hittades
if "VARA" not in entities:
return {"result": "error:ingen vara"}
# Skapa strukturerad sträng
result_parts = [f"vara:{entities['VARA']}"]
if "FÄRG" in entities:
result_parts.append(f"färg:{entities['FÄRG']}")
if "PRIS" in entities:
result_parts.append(f"pris:{entities['PRIS']}")
return {"result": ",".join(result_parts)}
except Exception as e:
return {"error": f"Fel vid parsning: {str(e)}"}
@app.get("/")
async def root():
return {"message": "Request Parser API is running!"} |