271 lines
8.2 KiB
Python
271 lines
8.2 KiB
Python
import os
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import uuid
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import logging
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from functools import wraps
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from flask import Flask, request, jsonify
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from flask_cors import CORS
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from config import DATASETS_DIR, MODELS_DIR, TEMP_DIR, MAX_CONTENT_LENGTH, API_KEY
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from services.extract_service import extract_frames
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from services.train_service import train_model
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from services.verify_service import verify_face
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from services.embedding_service import (
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compute_embedding_from_frames,
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compute_embedding_from_image,
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compute_embedding_from_video
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)
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logging.basicConfig(
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level=logging.INFO,
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format='[%(asctime)s] %(levelname)s: %(message)s'
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)
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logger = logging.getLogger(__name__)
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app = Flask(__name__)
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app.config['MAX_CONTENT_LENGTH'] = MAX_CONTENT_LENGTH
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CORS(app)
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def require_api_key(f):
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@wraps(f)
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def decorated(*args, **kwargs):
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key = request.headers.get('X-API-Key', '')
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if key != API_KEY:
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return jsonify({"status": "error", "message": "API key tidak valid."}), 401
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return f(*args, **kwargs)
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return decorated
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@app.route('/health', methods=['GET'])
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def health():
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model_exists = os.path.exists(os.path.join(MODELS_DIR, 'face_model.pkl'))
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return jsonify({
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"status": "ok",
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"model_loaded": model_exists,
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"datasets_dir": DATASETS_DIR,
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"models_dir": MODELS_DIR,
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})
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@app.route('/extract-frames', methods=['POST'])
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@require_api_key
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def api_extract_frames():
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if 'video' not in request.files:
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return jsonify({"status": "error", "message": "File video tidak ditemukan."}), 400
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user_id = request.form.get('user_id')
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target_frames = int(request.form.get('target_frames', 200))
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if not user_id:
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return jsonify({"status": "error", "message": "user_id wajib diisi."}), 400
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video_file = request.files['video']
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temp_video = os.path.join(TEMP_DIR, f"enroll_{user_id}_{uuid.uuid4().hex[:8]}.mp4")
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try:
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video_file.save(temp_video)
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output_dir = os.path.join(DATASETS_DIR, str(user_id))
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result = extract_frames(temp_video, output_dir, target_frames=target_frames)
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logger.info(
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f"Extract frames berhasil untuk user {user_id}. "
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f"Frames: {result['total_extracted']}"
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)
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return jsonify({
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"status": "success",
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"message": f"Berhasil mengekstrak {result['total_extracted']} frame wajah frontal",
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**result
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})
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except Exception as e:
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logger.error(f"Extract frames GAGAL untuk user {user_id}: {str(e)}")
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return jsonify({"status": "error", "message": str(e)}), 500
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finally:
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if os.path.exists(temp_video):
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os.remove(temp_video)
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@app.route('/train-model', methods=['POST'])
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@require_api_key
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def api_train_model():
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data = request.get_json(silent=True) or {}
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approved_user_ids = data.get('approved_user_ids')
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if not approved_user_ids or not isinstance(approved_user_ids, list):
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return jsonify({
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"status": "error",
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"message": "approved_user_ids wajib diisi sebagai array."
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}), 400
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approved_str = [str(uid) for uid in approved_user_ids]
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try:
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logger.info(
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f"Memulai training model SVM. "
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f"Approved users: {approved_str}"
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)
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result = train_model(DATASETS_DIR, MODELS_DIR, approved_str)
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logger.info(
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f"Training selesai. Users: {result['total_users']}, "
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f"Test: {result['test_accuracy']*100:.2f}%"
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)
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return jsonify({
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"status": "success",
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"message": (
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f"Model SVM berhasil dilatih. "
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f"{result['total_users']} user, "
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f"Test={result['test_accuracy']*100:.2f}%"
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),
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**result
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})
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except Exception as e:
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logger.error(f"Training model GAGAL: {str(e)}")
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return jsonify({"status": "error", "message": str(e)}), 500
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@app.route('/verify-face', methods=['POST'])
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@require_api_key
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def api_verify_face():
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if 'file' not in request.files:
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return jsonify({"status": "error", "message": "File tidak ditemukan."}), 400
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user_id = request.form.get('user_id')
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is_video = request.form.get('is_video', 'false').lower() == 'true'
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if not user_id:
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return jsonify({"status": "error", "message": "user_id wajib diisi."}), 400
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uploaded_file = request.files['file']
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ext = 'mp4' if is_video else 'jpg'
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temp_file = os.path.join(TEMP_DIR, f"verify_{user_id}_{uuid.uuid4().hex[:8]}.{ext}")
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try:
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uploaded_file.save(temp_file)
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result = verify_face(MODELS_DIR, user_id, temp_file, is_video=is_video)
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logger.info(
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f"Verifikasi user {user_id}: "
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f"status={result.get('verification_status')}, "
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f"match={result.get('match')}, "
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f"svm_df={result.get('svm_df')}, "
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f"confidence={result.get('confidence')}, "
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f"predicted={result.get('predicted_user')}, "
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f"blur={result.get('blur_score')}, "
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f"frames_approved={result.get('frames_approved')}/{result.get('frames_total')}"
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)
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return jsonify(result)
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except Exception as e:
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logger.error(f"Verifikasi GAGAL untuk user {user_id}: {str(e)}")
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return jsonify({"status": "error", "message": str(e)}), 500
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finally:
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if os.path.exists(temp_file):
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os.remove(temp_file)
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@app.route('/extract-and-embed', methods=['POST'])
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@require_api_key
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def api_extract_and_embed():
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if 'video' not in request.files:
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return jsonify({"status": "error", "message": "File video tidak ditemukan."}), 400
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user_id = request.form.get('user_id')
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target_frames = int(request.form.get('target_frames', 200))
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if not user_id:
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return jsonify({"status": "error", "message": "user_id wajib diisi."}), 400
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video_file = request.files['video']
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temp_video = os.path.join(TEMP_DIR, f"enroll_{user_id}_{uuid.uuid4().hex[:8]}.mp4")
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try:
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video_file.save(temp_video)
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output_dir = os.path.join(DATASETS_DIR, str(user_id))
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result = extract_frames(temp_video, output_dir, target_frames=target_frames)
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embedding = compute_embedding_from_frames(output_dir)
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logger.info(
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f"Extract & embed berhasil untuk user {user_id}. "
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f"Frames: {result['total_extracted']}, "
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f"Embedding dim: {len(embedding)}"
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)
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return jsonify({
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"status": "success",
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"message": f"Berhasil mengekstrak {result['total_extracted']} frame dan menghitung embedding",
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"embedding": embedding,
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**result
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})
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except Exception as e:
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logger.error(f"Extract & embed GAGAL untuk user {user_id}: {str(e)}")
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return jsonify({"status": "error", "message": str(e)}), 500
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finally:
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if os.path.exists(temp_video):
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os.remove(temp_video)
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@app.route('/get-embedding', methods=['POST'])
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@require_api_key
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def api_get_embedding():
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if 'file' not in request.files:
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return jsonify({"status": "error", "message": "File tidak ditemukan."}), 400
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is_video = request.form.get('is_video', 'false').lower() == 'true'
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uploaded_file = request.files['file']
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ext = 'mp4' if is_video else 'jpg'
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temp_file = os.path.join(TEMP_DIR, f"embed_{uuid.uuid4().hex[:8]}.{ext}")
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try:
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uploaded_file.save(temp_file)
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if is_video:
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embedding, blur_score = compute_embedding_from_video(temp_file, target_frames=10)
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else:
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embedding, blur_score = compute_embedding_from_image(temp_file)
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logger.info(
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f"Get embedding berhasil. "
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f"Dim: {len(embedding)}, blur: {round(blur_score, 1)}"
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)
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return jsonify({
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"status": "success",
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"embedding": embedding,
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"blur_score": round(blur_score, 1),
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})
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except Exception as e:
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logger.error(f"Get embedding GAGAL: {str(e)}")
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return jsonify({"status": "error", "message": str(e)}), 500
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finally:
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if os.path.exists(temp_file):
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os.remove(temp_file)
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if __name__ == '__main__':
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logger.info("=" * 50)
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logger.info("MPG HRIS - Flask ML API Server")
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logger.info(f"Datasets: {DATASETS_DIR}")
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logger.info(f"Models: {MODELS_DIR}")
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logger.info("=" * 50)
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app.run(host='0.0.0.0', port=5000, debug=True)
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