main.py

from typing import Union
import dotenv
import traceback
import json
import io
import os
import base64
from fastapi import FastAPI, File, HTTPException, UploadFile, Response
import models.face_classifier as classifier
from fastapi.middleware.cors import CORSMiddleware
from PIL import Image
from rembg import remove
from utils.helpers import image_to_base64, calculate_mask_area


dotenv.load_dotenv()

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


# CLIENT = create_client(os.getenv("ROBOFLOW_API_KEY"))
# model = FaceClassifierModel(client=CLIENT)

model = classifier.FaceSegmentationModel()



@app.post("/segment/", summary="Classify skin type based on image given",tags=["Classify"])
async def predict_image(file: UploadFile = File(...)):
    try:
        # Menangani file yang diunggah
        image_file = await file.read()
        pil_image= Image.open(io.BytesIO(image_file)).convert("RGB")
        
        # resize image to 512x512
        pil_image = pil_image.resize((500, 500))
        
        image_bg_removed= remove(pil_image, bgcolor=(0,0,255,255))
        
        # Memanggil metode classify untuk melakukan klasifikasi
        results = model.infer(image_bg_removed)
        
        print(len(results))
        
        background_element = next((element for element in results if element['label'] == 'background'), None)
        
        if background_element:
            background_area = calculate_mask_area(background_element['mask'], True)
        else:
            background_area = 0
        
        # change the mask to base64 and calculate the score
        for i in range(len(results)):
            results[i]["mask"] = image_to_base64(results[i]["mask"])
            if results[i]["label"] == "background":
                continue
            mask_area = calculate_mask_area(results[i]["mask"])
            score = mask_area / (500 * 500 - background_area)
            results[i]["score"] = score
            
            
        # add original image base 64 as original image:
        image_bg_removed = image_bg_removed.convert("RGB")
        
        response = {
            "original_image": image_to_base64(image_bg_removed),
            "segmentation_results":results
        }
        
        # Kembalikan hasil klasifikasi
        return Response(content=json.dumps(response), status_code=200)
    
    except Exception as e:
        # Mendapatkan stack trace
        error_traceback = traceback.format_exc()
        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")