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from sentence_transformers import SentenceTransformer, util
from fastapi import FastAPI
import joblib
from sentence_transformers import SentenceTransformer
app = FastAPI()
model = SentenceTransformer(
'Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
@app.get("/")
def root():
return {"message": "Welcom to FastAPI with Logistic Regression"}
@app.post("/dimensions/")
def get_dimension(message: str):
message_embedding = model.encode([message])
return {"dimensions": message_embedding.shape[1]}
@app.post("/classify/")
def classify(message: str):
loaded_model = joblib.load('sms_classifier_model.pkl')
message_embedding = model.encode([message])
prediction = loaded_model.predict(message_embedding)
return {"Predicted Category": f"{prediction[0]}"}
@app.post("/cosine-similarity")
def calculate_cosine_similarity(sentence1:str, sentence2:str):
embeddings = model.encode([sentence1, sentence2])
cosine_sim = util.cos_sim(embeddings[0], embeddings[1])
# return str(cosine_sim[0])
return round(cosine_sim.item(), 3)
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