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72b9a21 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 | import os
import json
import io
import base64
from datetime import datetime
import numpy as np
import torch
import torch.nn as nn
from torchvision import transforms, models
import joblib
from PIL import Image
from flask import Flask, request, jsonify
from flask_cors import CORS
from supabase import create_client, Client
app = Flask(__name__)
CORS(app)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
MODEL_DIR = os.path.join(os.path.dirname(__file__), "models")
model_path = os.path.join(MODEL_DIR, "svm_densenet201_rbf.joblib")
meta_path = os.path.join(MODEL_DIR, "metadata.json")
svm_model = None
class_names = None
IMG_SIZE = 224
supabase_url = os.environ.get('SUPABASE_URL')
supabase_key = os.environ.get('SUPABASE_ANON_KEY')
supabase: Client = None
if supabase_url and supabase_key:
try:
supabase = create_client(supabase_url, supabase_key)
print("✓ Supabase client initialized")
except Exception as e:
print(f"⚠ Failed to initialize Supabase: {e}")
supabase = None
else:
print("⚠ Supabase credentials not found, predictions won't be saved to database")
def load_model():
global svm_model, class_names, IMG_SIZE
try:
if os.path.exists(model_path):
svm_model = joblib.load(model_path)
print("✓ SVM model loaded successfully")
else:
print(f"⚠ Model file not found at {model_path}")
print(" Using simulation mode until model is uploaded")
svm_model = None
if os.path.exists(meta_path):
with open(meta_path, "r") as f:
meta = json.load(f)
class_names = meta.get("class_names", ["3 Bulan", "6 Bulan", "9 Bulan"])
IMG_SIZE = meta.get("img_size", 224)
print(f"✓ Metadata loaded: {class_names}")
else:
class_names = ["3 Bulan", "6 Bulan", "9 Bulan"]
print(f"⚠ Metadata not found, using default classes: {class_names}")
except Exception as e:
print(f"Error loading model: {str(e)}")
svm_model = None
class_names = ["3 Bulan", "6 Bulan", "9 Bulan"]
densenet = models.densenet201(weights=models.DenseNet201_Weights.DEFAULT)
densenet.eval()
feature_extractor = densenet.features.to(device)
gap = nn.AdaptiveAvgPool2d((1, 1)).to(device)
eval_tfms = transforms.Compose([
transforms.Resize((IMG_SIZE, IMG_SIZE)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225]),
])
def decode_base64_image(base64_string):
if ',' in base64_string:
base64_string = base64_string.split(',')[1]
image_data = base64.b64decode(base64_string)
image = Image.open(io.BytesIO(image_data)).convert("RGB")
return image
def preprocess_image(image):
x = eval_tfms(image).unsqueeze(0)
return x
@torch.no_grad()
def extract_features(img_tensor):
img_tensor = img_tensor.to(device)
feats = feature_extractor(img_tensor)
feats = torch.relu(feats)
feats = gap(feats)
feats = feats.view(feats.size(0), -1)
return feats.cpu().numpy()
def simulate_prediction():
probabilities = np.random.dirichlet(np.ones(len(class_names)), size=1)[0]
pred_idx = int(np.argmax(probabilities))
pred_label = class_names[pred_idx]
confidence = float(probabilities[pred_idx])
return pred_label, confidence, probabilities
def predict_with_model(features):
proba = svm_model.predict_proba(features)[0]
pred_idx = int(np.argmax(proba))
pred_label = class_names[pred_idx]
confidence = float(proba[pred_idx])
return pred_label, confidence, proba
@app.route('/health', methods=['GET'])
def health_check():
return jsonify({
'status': 'healthy',
'model_loaded': svm_model is not None,
'device': str(device),
'classes': class_names
})
def save_to_database(pred_label, confidence, prob_dict, mode, image_data_url=None):
if not supabase:
return None
try:
prediction_data = {
'predicted_class': pred_label,
'confidence': confidence,
'probabilities': prob_dict,
'mode': mode,
'created_at': datetime.utcnow().isoformat()
}
if image_data_url:
prediction_data['image_data'] = image_data_url[:1000]
result = supabase.table('predictions').insert(prediction_data).execute()
return result.data[0] if result.data else None
except Exception as e:
print(f"⚠ Failed to save to database: {e}")
return None
@app.route('/classify', methods=['POST'])
def classify_image():
try:
data = request.json
if not data or 'image' not in data:
return jsonify({'error': 'No image data provided'}), 400
image_base64 = data['image']
image = decode_base64_image(image_base64)
img_tensor = preprocess_image(image)
if svm_model is not None:
features = extract_features(img_tensor)
pred_label, confidence, probabilities = predict_with_model(features)
else:
pred_label, confidence, probabilities = simulate_prediction()
prob_dict = {class_names[i]: float(probabilities[i]) for i in range(len(class_names))}
mode = 'real' if svm_model is not None else 'simulation'
db_record = save_to_database(pred_label, confidence, prob_dict, mode, data['image'])
response = {
'predicted_class': pred_label,
'confidence': confidence,
'probabilities': prob_dict,
'mode': mode
}
if db_record:
response['id'] = db_record.get('id')
response['saved_to_db'] = True
else:
response['saved_to_db'] = False
return jsonify(response)
except Exception as e:
return jsonify({
'error': 'Classification failed',
'message': str(e)
}), 500
@app.route('/reload-model', methods=['POST'])
def reload_model():
try:
load_model()
return jsonify({
'status': 'success',
'model_loaded': svm_model is not None,
'classes': class_names
})
except Exception as e:
return jsonify({
'status': 'error',
'message': str(e)
}), 500
if __name__ == '__main__':
os.makedirs(MODEL_DIR, exist_ok=True)
load_model()
port = int(os.environ.get('PORT', 5000))
app.run(host='0.0.0.0', port=port, debug=False)
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