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from fastapi import FastAPI, File, UploadFile
import torch
from dotenv import load_dotenv
import logging
import os
from PIL import Image
from transformers import CLIPProcessor, CLIPModel
load_dotenv()
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
app = FastAPI(title='CLIP API',
description='Returns CLIP embedding for text and image')
HF_TOKEN = os.getenv('hf_token')
logger.info("Loading CLIP processor and model")
try:
processor = CLIPProcessor.from_pretrained(
"openai/clip-vit-large-patch14", use_auth_token=HF_TOKEN
)
clip_model = CLIPModel.from_pretrained(
"openai/clip-vit-large-patch14", use_auth_token=HF_TOKEN)
clip_model.eval()
logger.info("CLIP model loaded successfully")
except Exception as e:
logger.error(f"Failed to load CLIP model : {e}")
raise
def get_text_embedding(text: str):
try:
inputs = processor(text=[text], return_tensors="pt",
padding=True, truncation=True)
with torch.no_grad():
text_embeddings = clip_model.get_text_features(**inputs)
logger.info("Text embedding generated")
return text_embeddings.squeeze(0).tolist()
except Exception as e:
logger.error(f"Error while generating embedding : {e}")
raise
@app.get("/")
async def root():
logger.info("Root endpoint accessed")
return {"message": "Welcome to the CLIP embedding API."}
@app.get("/embedding")
async def get_embedding_text(text: str):
logger.info(f"Embedding endpoint called with text")
embedding = get_text_embedding(text)
return {"embedding": embedding, "dimension": len(embedding)}
@app.post("/clip/process")
async def process_image(file: UploadFile = File(...)):
logger.info("Processing image")
image = Image.open(file.file).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
embeddings = clip_model.get_image_features(**inputs)
return {"embedding": embeddings.tolist()}
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