Add exception handling

app.py

@@ -180,93 +180,90 @@ def translate_label(language_label, label):
 
 def detect_objects(image, language_selector, translated_model_selector, threshold):
     """Enhanced object detection with adjustable threshold and better info"""
-    # Get the actual model key from the translated name
-
-    def detect_objects(image, language_selector, translated_model_selector, threshold):
-        try:
-            if image is None:
-                return None, "Por favor, sube una imagen antes de detectar objetos."
-
-            model_selector = get_model_key_from_translation(translated_model_selector, language_selector)
-            print(f"Processing image. Language: {language_selector}, Model: {model_selector}, Threshold: {threshold}")
-
-            model, processor = load_model(model_selector)
-
-            inputs = processor(images=image, return_tensors="pt")
-            outputs = model(**inputs)
-
-            target_sizes = torch.tensor([image.size[::-1]])
-            results = processor.post_process_object_detection(
-                outputs, threshold=threshold, target_sizes=target_sizes
-            )[0]
-
-            image_with_boxes = image.copy()
-            draw = ImageDraw.Draw(image_with_boxes)
-
-            detection_info = f"Detected {len(results['scores'])} objects with threshold {threshold}\n"
-            detection_info += f"Model: {translated_model_selector} ({model_selector})\n\n"
-
-            colors = {
-                'high': 'red',  # > 0.8
-                'medium': 'orange',  # 0.5-0.8
-                'low': 'yellow'  # < 0.5
-            }
-
-            detected_objects = []
-
-            for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-                confidence = score.item()
-                box = [round(x, 2) for x in box.tolist()]
-                if confidence > 0.8:
-                    color = colors['high']
-                elif confidence > 0.5:
-                    color = colors['medium']
-                else:
-                    color = colors['low']
-
-                draw.rectangle(box, outline=color, width=3)
-                label_text = model.config.id2label[label.item()]
-                translated_label = translate_label(language_selector, label_text)
-                display_text = f"{translated_label}: {round(confidence, 3)}"
-                detected_objects.append({
-                    'label': label_text,
-                    'translated': translated_label,
-                    'confidence': confidence,
-                    'box': box
-                })
-
-                try:
-                    image_width = image.size[0]
-                    font_size = max(image_width // 40, 12)
-                    font = get_font(font_size)
-                    text_bbox = draw.textbbox((0, 0), display_text, font=font)
-                    text_width = text_bbox[2] - text_bbox[0]
-                    text_height = text_bbox[3] - text_bbox[1]
-                except:
-                    font = get_font(12)
-                    text_width = 50
-                    text_height = 20
-
-                text_bg = [
-                    box[0], box[1] - text_height - 4,
-                            box[0] + text_width + 4, box[1]
-                ]
-                draw.rectangle(text_bg, fill="black")
-                draw.text((box[0] + 2, box[1] - text_height - 2), display_text, fill="white", font=font)
-
-            if detected_objects:
-                detection_info += "Objects found:\n"
-                for obj in sorted(detected_objects, key=lambda x: x['confidence'], reverse=True):
-                    detection_info += f"- {obj['translated']} ({obj['label']}): {obj['confidence']:.3f}\n"
+    try:
+        if image is None:
+            return None, "Por favor, sube una imagen antes de detectar objetos."
+
+        model_selector = get_model_key_from_translation(translated_model_selector, language_selector)
+        print(f"Processing image. Language: {language_selector}, Model: {model_selector}, Threshold: {threshold}")
+
+        model, processor = load_model(model_selector)
+
+        inputs = processor(images=image, return_tensors="pt")
+        outputs = model(**inputs)
+
+        target_sizes = torch.tensor([image.size[::-1]])
+        results = processor.post_process_object_detection(
+            outputs, threshold=threshold, target_sizes=target_sizes
+        )[0]
+
+        image_with_boxes = image.copy()
+        draw = ImageDraw.Draw(image_with_boxes)
+
+        detection_info = f"Detected {len(results['scores'])} objects with threshold {threshold}\n"
+        detection_info += f"Model: {translated_model_selector} ({model_selector})\n\n"
+
+        colors = {
+            'high': 'red',      # > 0.8
+            'medium': 'orange', # 0.5-0.8
+            'low': 'yellow'     # < 0.5
+        }
+
+        detected_objects = []
+
+        for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+            confidence = score.item()
+            box = [round(x, 2) for x in box.tolist()]
+            if confidence > 0.8:
+                color = colors['high']
+            elif confidence > 0.5:
+                color = colors['medium']
             else:
-                detection_info += "No objects detected. Try lowering the threshold."
+                color = colors['low']
+
+            draw.rectangle(box, outline=color, width=3)
+            label_text = model.config.id2label[label.item()]
+            translated_label = translate_label(language_selector, label_text)
+            display_text = f"{translated_label}: {round(confidence, 3)}"
+            detected_objects.append({
+                'label': label_text,
+                'translated': translated_label,
+                'confidence': confidence,
+                'box': box
+            })
+
+            try:
+                image_width = image.size[0]
+                font_size = max(image_width // 40, 12)
+                font = get_font(font_size)
+                text_bbox = draw.textbbox((0, 0), display_text, font=font)
+                text_width = text_bbox[2] - text_bbox[0]
+                text_height = text_bbox[3] - text_bbox[1]
+            except:
+                font = get_font(12)
+                text_width = 50
+                text_height = 20
+
+            text_bg = [
+                box[0], box[1] - text_height - 4,
+                        box[0] + text_width + 4, box[1]
+            ]
+            draw.rectangle(text_bg, fill="black")
+            draw.text((box[0] + 2, box[1] - text_height - 2), display_text, fill="white", font=font)
 
-            return image_with_boxes, detection_info
-        except Exception as e:
-            import traceback
-            print("ERROR EN DETECT_OBJECTS:", e)
-            traceback.print_exc()
-            return None, f"Error detecting objects: {e}"
+        if detected_objects:
+            detection_info += "Objects found:\n"
+            for obj in sorted(detected_objects, key=lambda x: x['confidence'], reverse=True):
+                detection_info += f"- {obj['translated']} ({obj['label']}): {obj['confidence']:.3f}\n"
+        else:
+            detection_info += "No objects detected. Try lowering the threshold."
+
+        return image_with_boxes, detection_info
+    except Exception as e:
+        import traceback
+        print("ERROR EN DETECT_OBJECTS:", e)
+        traceback.print_exc()
+        return None, f"Error detecting objects: {e}"
 
 
 def build_app():
@@ -353,4 +350,4 @@ load_model("DETR ResNet-50")
 # Launch the application
 if __name__ == "__main__":
     app = build_app()
-    app.launch()
+    app.launch()