Add exception handling

app.py

@@ -181,102 +181,92 @@ 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
-    model_selector = get_model_key_from_translation(translated_model_selector, language_selector)
 
-    print(f"Processing image. Language: {language_selector}, Model: {model_selector}, Threshold: {threshold}")
-
-    # Load the selected model
-    model, processor = load_model(model_selector)
-
-    # Process the image
-    inputs = processor(images=image, return_tensors="pt")
-    outputs = model(**inputs)
-
-    # Convert model output to usable detection results with custom threshold
-    target_sizes = torch.tensor([image.size[::-1]])
-    results = processor.post_process_object_detection(
-        outputs, threshold=threshold, target_sizes=target_sizes
-    )[0]
-
-    # Create a copy of the image for drawing
-    image_with_boxes = image.copy()
-    draw = ImageDraw.Draw(image_with_boxes)
-
-    # Detection info
-    detection_info = f"Detected {len(results['scores'])} objects with threshold {threshold}\n"
-    detection_info += f"Model: {translated_model_selector} ({model_selector})\n\n"
-
-    # Colors for different confidence levels
-    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()]
-
-        # Choose color based on confidence
-        if confidence > 0.8:
-            color = colors['high']
-        elif confidence > 0.5:
-            color = colors['medium']
-        else:
-            color = colors['low']
-
-        # Draw bounding box
-        draw.rectangle(box, outline=color, width=3)
-
-        # Prepare label text
-        label_text = model.config.id2label[label.item()]
-        translated_label = translate_label(language_selector, label_text)
-        display_text = f"{translated_label}: {round(confidence, 3)}"
-
-        # Store detection info
-        detected_objects.append({
-            'label': label_text,
-            'translated': translated_label,
-            'confidence': confidence,
-            'box': box
-        })
-
-        # Calculate text position and size - FIXED FONT USAGE
+    def detect_objects(image, language_selector, translated_model_selector, threshold):
         try:
-            image_width = image.size[0]
-            # Calculate the font size for drawing labels, ensuring it scales with image width but is never smaller than 50 pixels.
-            font_size = max(image_width // 40, 12)  # Adjust font size based on image width
-            font = get_font(font_size)  # Use the fixed font function
-
-            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:
-            # Fallback for older PIL versions
-            font = get_font(12)  # Use the fixed font function
-            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]
-
-        # Draw text background
-        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)
-
-    # Create detailed detection info
-    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
+            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"
+            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():