import base64
import os
import uuid
from fastapi import HTTPException
from PIL import Image
from backend.app.pipeline.inference_pipeline import InferencePipeline
from backend.app.controllers.tag_extractor import get_tags_from_clip
from backend.app.schemas.clothing_schemas import (
UploadClothingItemRequest,
UploadClothingItemResponse,
TagRequest,
TagResponse
)
# Upload directory for temporary storage
UPLOAD_DIR = "uploads"
os.makedirs(UPLOAD_DIR, exist_ok=True)
# Minimum confidence for AWS Rekognition detection
MIN_CONFIDENCE = 50
async def handle_upload_clothing_item(payload: UploadClothingItemRequest) -> UploadClothingItemResponse:
"""
Endpoint logic for handling clothing image upload and multi-garment tagging.
Uses AWS Rekognition for detection, and CLIP for tag extraction per garment.
"""
# Step 1: Decode image
try:
image_data = base64.b64decode(payload.image_base64)
filename = payload.filename or f"{uuid.uuid4().hex}.jpg"
image_path = os.path.join(UPLOAD_DIR, filename)
with open(image_path, "wb") as f:
f.write(image_data)
image = Image.open(image_path).convert("RGB")
except Exception as e:
raise HTTPException(status_code=400, detail=f"Invalid image data: {str(e)}")
# Step 2: Run Automated Inference Pipeline
try:
with open(image_path, "rb") as f:
image_bytes = f.read()
pipeline = InferencePipeline(min_confidence=MIN_CONFIDENCE)
results = pipeline.process_image(image, image_bytes)
except Exception as e:
raise HTTPException(status_code=500, detail=f"Inference Pipeline error: {str(e)}")
# Step 3: Return structured response
return UploadClothingItemResponse(
id=str(uuid.uuid4()),
filename=filename,
tags={"garments": results}
)
async def handle_tag_request(payload: TagRequest) -> TagResponse:
"""
Optional: a simpler endpoint to return tags from a single image
(without Rekognition detection).
"""
try:
image_data = base64.b64decode(payload.image_base64)
temp_filename = f"temp_{uuid.uuid4().hex}.jpg"
image_path = os.path.join(UPLOAD_DIR, temp_filename)
with open(image_path, "wb") as f:
f.write(image_data)
image = Image.open(image_path).convert("RGB")
tags_result = get_tags_from_clip(image)
return TagResponse(tags=tags_result.get("tags", {}))
except Exception as e:
raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")
