Implement Word Vector

Author: ppirch

routers/word_vector.py

@@ -0,0 +1,63 @@
+from fastapi import APIRouter, Query, HTTPException
+from pythainlp import word_vector
+from enum import Enum
+from typing import List, Optional
+from pydantic import BaseModel
+
+router = APIRouter()
+
+
+class MostSimilarCosmulWord(BaseModel):
+    word: str
+    score: float
+
+
+class DoesntMatchResponse(BaseModel):
+    doesnt_match: str = ""
+
+
+class MostSimilarCosmulResponse(BaseModel):
+    most_similar_cosmul: List[MostSimilarCosmulWord] = []
+
+
+class SentenceVectorizerResponse(BaseModel):
+    sentence_vectorizer: List[List[float]] = []
+
+
+class SimilarityResponse(BaseModel):
+    similarity: float = -1
+
+
+@router.get('/doesnt-match', response_model=DoesntMatchResponse)
+def doesnt_match(words: List[str] = Query(None)):
+    try:
+        return {"doesnt_match": word_vector.doesnt_match(words)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e).replace("\"", ""))
+
+
+@router.get('/most-similar-cosmul', response_model=MostSimilarCosmulResponse)
+def most_similar_cosmul(listPositive: List[str] = Query([]), listNegative: List[str] = Query([])):
+    try:
+        words = word_vector.most_similar_cosmul(listPositive, listNegative)
+        res = [{"word": word, "score": score} for word, score in words]
+        return {"most_similar_cosmul": res}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e).replace("\"", ""))
+
+
+@router.get('/sentence-vectorizer', response_model=SentenceVectorizerResponse)
+def sentence_vectorizer(q: str = ""):
+    try:
+        vector = word_vector.sentence_vectorizer(q, use_mean=True).tolist()
+        return {"sentence_vectorizer": vector}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e).replace("\"", ""))
+
+
+@router.get('/similarity', response_model=SimilarityResponse)
+def similarity(word1: str = "", word2: str = ""):
+    try:
+        return {"similarity": word_vector.similarity(word1, word2)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e).replace("\"", ""))