Update app.py

app.py

@@ -38,92 +38,62 @@ def detect_combined(image):
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
         image.save(temp_file, format="JPEG")
         temp_path = temp_file.name
-
+    
     try:
-        # ========== [1] YOLO: Deteksi Produk Nestlé (Per Class) ========== 
+        # YOLO Detection (Nestlé products)
         yolo_pred = yolo_model.predict(temp_path, confidence=50, overlap=80).json()
-
-        # Hitung per class Nestlé
         nestle_class_count = {}
         nestle_boxes = []
         for pred in yolo_pred['predictions']:
             class_name = pred['class']
             nestle_class_count[class_name] = nestle_class_count.get(class_name, 0) + 1
             nestle_boxes.append((pred['x'], pred['y'], pred['width'], pred['height']))
-
         total_nestle = sum(nestle_class_count.values())
-
-        # ========== [2] DINO-X: Deteksi Kompetitor ========== 
-        image_url = dinox_client.upload_file(temp_path)
-        task = DinoxTask(
-            image_url=image_url,
-            prompts=[TextPrompt(text=DINOX_PROMPT)],
-            bbox_threshold=0.4,
-            targets=[DetectionTarget.BBox]
-        )
-        dinox_client.run_task(task)
-        dinox_pred = task.result.objects
-
-        # Filter & Hitung Kompetitor
+        
+        # CountGD Detection (Competitor products)
+        url = "https://api.landing.ai/v1/tools/text-to-object-detection"
+        files = {"image": open(temp_path, "rb")}
+        data = {"prompts": ["mixed box"], "model": "countgd"}
+        headers = {"Authorization": "Basic COUNTGD_API_KEY"}  # Replace with actual API key
+        response = requests.post(url, files=files, data=data, headers=headers)
+        result = response.json()
+        
         competitor_class_count = {}
         competitor_boxes = []
-        for obj in dinox_pred:
-            dinox_box = obj.bbox
-            # Filter objek yang sudah terdeteksi oleh YOLO (Overlap detection)
-            if not is_overlap(dinox_box, nestle_boxes):  # Ignore if overlap with YOLO detections
-                class_name = obj.category.strip().lower()  # Normalisasi nama kelas
-                competitor_class_count[class_name] = competitor_class_count.get(class_name, 0) + 1
-                competitor_boxes.append({
-                    "class": class_name,
-                    "box": dinox_box,
-                    "confidence": obj.score
-                })
-
+        if 'data' in result:
+            for obj in result['data'][0]:
+                if 'bounding_box' in obj:
+                    x, y, x2, y2 = obj['bounding_box']
+                    class_name = "unclassified"  # CountGD does not classify, so use generic label
+                    competitor_class_count[class_name] = competitor_class_count.get(class_name, 0) + 1
+                    competitor_boxes.append((x, y, x2, y2))
         total_competitor = sum(competitor_class_count.values())
-
-        # ========== [3] Format Output ========== 
-        result_text = "Product Nestle\n\n"
+        
+        # Format Output
+        result_text = "Product Nestlé\n\n"
         for class_name, count in nestle_class_count.items():
             result_text += f"{class_name}: {count}\n"
-        result_text += f"\nTotal Products Nestle: {total_nestle}\n\n"
-
-        #result_text += "Competitor Products\n\n"
-        if competitor_class_count:
-            result_text += f"Total Unclassified Products: {total_competitor}\n"  # Hanya total, tidak per kelas
-        else:
-            result_text += "No Unclassified Products detected\n"
-
-        # ========== [4] Visualisasi ========== 
+        result_text += f"\nTotal Products Nestlé: {total_nestle}\n\n"
+        result_text += f"Total Unclassified Products: {total_competitor}\n" if total_competitor else "No Unclassified Products detected\n"
+        
+        # Visualization
         img = cv2.imread(temp_path)
-
-        # Nestlé (Hijau)
         for pred in yolo_pred['predictions']:
             x, y, w, h = pred['x'], pred['y'], pred['width'], pred['height']
             cv2.rectangle(img, (int(x-w/2), int(y-h/2)), (int(x+w/2), int(y+h/2)), (0,255,0), 2)
-            cv2.putText(img, pred['class'], (int(x-w/2), int(y-h/2-10)), 
-                       cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,255,0), 3)
-
-        # Kompetitor (Merah) dengan nama 'unclassified'
-        for comp in competitor_boxes:
-            x1, y1, x2, y2 = comp['box']
-            
-            # Define a list of target classes to rename
-            unclassified_classes = ["beverage", "cans", "bottle", "mixed box"]
-            
-            # Normalize the class name to be case-insensitive and check if it's in the unclassified list
-            display_name = "unclassified" if any(class_name in comp['class'].lower() for class_name in unclassified_classes) else comp['class']
-            
-            cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 2)
-            cv2.putText(img, f"{display_name} {comp['confidence']:.2f}",
-                        (int(x1), int(y1-10)), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 3)
-            
+            cv2.putText(img, pred['class'], (int(x-w/2), int(y-h/2-10)), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,255,0), 3)
+        
+        for x1, y1, x2, y2 in competitor_boxes:
+            cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (0,0,255), 2)
+            cv2.putText(img, "unclassified", (int(x1), int(y1-10)), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,0,255), 3)
+        
         output_path = "/tmp/combined_output.jpg"
         cv2.imwrite(output_path, img)
-
         return output_path, result_text
-
+    
     except Exception as e:
         return temp_path, f"Error: {str(e)}"
+    
     finally:
         os.remove(temp_path)