app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoModel
from sentence_transformers import SentenceTransformer, models
import logging

logging.basicConfig(level=logging.INFO)
logger=logging.getLogger(__name__)

logger.info("Server Starting")
try:
    logger.info("Loading model")
    MODEL_NAME = "Sid-the-sloth/leetcode_unixcoder_final"
    device="cpu"
    # tokenizer=AutoTokenizer.from_pretrained(MODEL_NAME)
    # embedding_model=AutoModel.from_pretrained(MODEL_NAME)
    # pooling_model=models.Pooling(embedding_model.get_word_embedding_dimension())

    model=SentenceTransformer(MODEL_NAME,
        # modules=[embedding_model,pooling_model],
        device=device
    )

    logger.info("Model Loaded")
except Exception as e:
    logger.error("Failed to load Model %s",e)
    model=None

app=FastAPI()

#Req and Response Pydantic models
class EmbedRequest(BaseModel):
    text : str

class EmbedResponse(BaseModel):
    embedding: list[float]

@app.get("/")
def root_status():
    return {"status":"ok","model":model is not None}

@app.post("/embed",response_model=EmbedResponse)
def get_embedding(request: EmbedRequest):
    if model is None:
        raise HTTPException(status_code=503,detail="/Model could not be loaded")
    try:
        
        embedding = model.encode(request.text).tolist()
        return EmbedResponse(embedding=embedding)
    except Exception as e:
        logger.error("Error during embedding generation %s",e)
        raise HTTPException(status_code=500,detail="Error generating embeddings")