Upload app.py

app.py

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+from flask import Flask, render_template, request, jsonify
+import os
+import base64
+import numpy as np
+import cv2
+from ultralytics import YOLO
+
+app = Flask(__name__)
+
+# =======================================================
+# 🔥 LAZY LOAD — Model hanya diload saat pertama dipakai
+# =======================================================
+YOLO_MODEL = None
+
+def load_yolo():
+    global YOLO_MODEL
+    
+    if YOLO_MODEL is not None:
+        return YOLO_MODEL
+    
+    try:
+        # Prefer load dari file lokal (HuggingFace tidak download ulang)
+        if os.path.exists("yolov8n.pt"):
+            print("🚀 Loading YOLO locally...")
+            YOLO_MODEL = YOLO("yolov8n.pt")
+        else:
+            print("⬇️ Downloading YOLO from Ultralytics...")
+            YOLO_MODEL = YOLO("yolov8n.pt")  # auto-download 6MB
+        print("✅ YOLO Ready!")
+    except Exception as e:
+        print("❌ YOLO gagal load:", e)
+        YOLO_MODEL = None
+    
+    return YOLO_MODEL
+
+
+# =======================================================
+# 🔥 HOME PAGE
+# =======================================================
+@app.route("/")
+def home():
+    return render_template("object_detection.html")
+
+
+# =======================================================
+# 🔥 YOLO DETECTION API
+# =======================================================
+@app.route("/api/object_detect", methods=["POST"])
+def object_detect_api():
+    model = load_yolo()
+    
+    if model is None:
+        return jsonify({"ok": False, "msg": "Model YOLO gagal load!"}), 500
+    
+    if "image" not in request.files:
+        return jsonify({"ok": False, "msg": "Tidak ada file gambar"}), 400
+    
+    file = request.files["image"]
+    img_bytes = file.read()
+
+    # Convert image
+    nparr = np.frombuffer(img_bytes, np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+    if img is None:
+        return jsonify({"ok": False, "msg": "Gambar tidak bisa dibaca"}), 400
+
+    # YOLO inference
+    results = model(img)
+
+    objects = []
+    found = False
+
+    for result in results:
+        for box in result.boxes:
+            found = True
+            x1, y1, x2, y2 = [int(v) for v in box.xyxy[0]]
+            conf = float(box.conf[0])
+            cls = int(box.cls.item())
+            label = model.names.get(cls, str(cls))
+
+            objects.append({
+                "label": label,
+                "confidence": round(conf * 100, 2)
+            })
+
+            # Draw box
+            cv2.rectangle(img, (x1, y1), (x2, y2), (30, 200, 255), 3)
+            cv2.putText(
+                img,
+                f"{label} {conf:.2f}",
+                (x1, y1 - 5),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.6,
+                (30, 200, 255),
+                2
+            )
+
+    _, buffer = cv2.imencode(".jpg", img)
+    img_base64 = base64.b64encode(buffer).decode()
+
+    return jsonify({
+        "ok": True,
+        "found": found,
+        "objects": objects,
+        "image": img_base64
+    })
+
+
+# =======================================================
+# LOCAL RUN
+# =======================================================
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)