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import os
import time
import requests
from PIL import Image
from io import BytesIO
from fastapi import APIRouter, HTTPException
from scripts.data_model import ImageInput, ImageOutput
from utils.pipeline import load_model
from utils.log import logger
router = APIRouter()
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
MODEL_PATH = os.path.join(BASE_DIR, "ml-models", "vit-human-pose-classification/")
@router.post(
"/image_classification",
response_model=ImageOutput,
summary="Image Classification",
description="Classify the image using a pre-trained model."
)
def image_classification(input: ImageInput)-> ImageOutput:
try:
pipe = load_model(MODEL_PATH, is_image_model=True)
image = input.url
logger.info(f"Image URLs: {image[-1]}")
start = time.time()
output = pipe(image[-1])
end = time.time()
prediction_time = int((end-start)*1000)
labels = [x["label"] for x in output]
scores = [x["score"] for x in output]
return ImageOutput(
user_id=input.user_id,
url=input.url,
model_name="vit-human-pose-classification",
label=labels,
score=scores,
prediction_time=prediction_time
)
except Exception as e:
raise HTTPException(status_code=500, detail=f"Failed to process image classification: {e}") |