Adding error catching

app.py

@@ -4,6 +4,10 @@ from PIL import Image, ImageDraw, ImageFont
 from transformers import DetrImageProcessor, DetrForObjectDetection
 from pathlib import Path
 import transformers
+import warnings
+import traceback
+
+warnings.filterwarnings("ignore", message=".*copying from a non-meta parameter.*")
 
 # Global variables to cache models
 current_model = None
@@ -38,14 +42,9 @@ def load_model(model_key):
     return current_model, current_processor
 
 
-
-
-
-
 # Load font
 font_path = Path("assets/fonts/arial.ttf")
 if not font_path.exists():
-
     print(f"Font file {font_path} not found. Using default font.")
     font = ImageFont.load_default()
 else:
@@ -62,6 +61,7 @@ translations = {
         "threshold_label": "Detection Threshold",
         "button": "Detect Objects",
         "info_label": "Detection Info",
+        "error_label": "Error Messages",
         "model_fast": "General Objects (fast)",
         "model_precision": "General Objects (high precision)",
         "model_small": "Small Objects/Details (slow)",
@@ -76,6 +76,7 @@ translations = {
         "threshold_label": "Umbral de detección",
         "button": "Detectar objetos",
         "info_label": "Información de detección",
+        "error_label": "Mensajes de error",
         "model_fast": "Objetos generales (rápido)",
         "model_precision": "Objetos generales (precisión alta)",
         "model_small": "Objetos pequeños/detalles (lento)",
@@ -90,6 +91,7 @@ translations = {
         "threshold_label": "Seuil de détection",
         "button": "Détecter les objets",
         "info_label": "Information de détection",
+        "error_label": "Messages d'erreur",
         "model_fast": "Objets généraux (rapide)",
         "model_precision": "Objets généraux (haute précision)",
         "model_small": "Petits objets/détails (lent)",
@@ -162,7 +164,6 @@ def get_helsinki_model(language_label):
 translation_cache = {}
 
 
-
 def translate_label(language_label, label):
     """Translates the given label to the target language."""
     # Check cache first
@@ -188,95 +189,102 @@ 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 = []
+    try:
+        # 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
+            try:
+                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
+                text_width, text_height = draw.textsize(display_text, font=font)
+
+            # 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."
 
-    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-        confidence = score.item()
-        box = [round(x, 2) for x in box.tolist()]
+        return image_with_boxes, detection_info, ""  # Empty error message
 
-        # 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
-        try:
-            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
-            text_width, text_height = draw.textsize(display_text, font=font)
-
-        # 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
+    except Exception as e:
+        error_message = f"Error in object detection:\n{str(e)}\n\nStack trace:\n{traceback.format_exc()}"
+        print(error_message)
+        # Return original image, error info, and error message
+        return image if image else None, "Detection failed. See error panel below.", error_message
 
 
 def build_app():
@@ -318,53 +326,100 @@ def build_app():
                     max_lines=15
                 )
 
+        # Error panel - only visible when there are errors
+        with gr.Row():
+            error_panel = gr.Textbox(
+                label=t("English", "error_label"),
+                lines=8,
+                max_lines=20,
+                visible=False,
+                elem_classes=["error-panel"]
+            )
+
         # Function to update interface when language changes
         def update_interface(selected_language):
-            translated_choices = get_translated_model_choices(selected_language)
-            default_model = t(selected_language, "model_fast")
-
-            return [
-                gr.update(value=t(selected_language, "title")),
-                gr.update(label=t(selected_language, "dropdown_label")),
-                gr.update(
-                    choices=translated_choices,
-                    value=default_model,
-                    label=t(selected_language, "dropdown_detection_model_label")
-                ),
-                gr.update(label=t(selected_language, "threshold_label")),
-                gr.update(label=t(selected_language, "input_label")),
-                gr.update(value=t(selected_language, "button")),
-                gr.update(label=t(selected_language, "output_label")),
-                gr.update(label=t(selected_language, "info_label"))
-            ]
+            try:
+                translated_choices = get_translated_model_choices(selected_language)
+                default_model = t(selected_language, "model_fast")
+
+                return [
+                    gr.update(value=t(selected_language, "title")),
+                    gr.update(label=t(selected_language, "dropdown_label")),
+                    gr.update(
+                        choices=translated_choices,
+                        value=default_model,
+                        label=t(selected_language, "dropdown_detection_model_label")
+                    ),
+                    gr.update(label=t(selected_language, "threshold_label")),
+                    gr.update(label=t(selected_language, "input_label")),
+                    gr.update(value=t(selected_language, "button")),
+                    gr.update(label=t(selected_language, "output_label")),
+                    gr.update(label=t(selected_language, "info_label")),
+                    gr.update(label=t(selected_language, "error_label"), value="", visible=False)  # Clear errors
+                ]
+            except Exception as e:
+                error_message = f"Error updating interface language:\n{str(e)}\n\nStack trace:\n{traceback.format_exc()}"
+                print(error_message)
+
+                # Return safe defaults
+                return [
+                    gr.update(),  # Keep current title
+                    gr.update(),  # Keep current language selector
+                    gr.update(),  # Keep current model selector
+                    gr.update(),  # Keep current threshold
+                    gr.update(),  # Keep current input label
+                    gr.update(),  # Keep current button
+                    gr.update(),  # Keep current output label
+                    gr.update(),  # Keep current info label
+                    gr.update(label="Error Messages", value=error_message, visible=True)  # Show error
+                ]
+
+        # Enhanced detection function with error handling
+        def safe_detect_objects(image, language_selector, translated_model_selector, threshold):
+            if image is None:
+                return None, "Please upload an image first.", ""
+
+            try:
+                result_image, info, error = detect_objects(image, language_selector, translated_model_selector,
+                                                           threshold)
+
+                # Update error panel visibility based on whether there's an error
+                error_visible = bool(error.strip())
+
+                return (
+                    result_image,
+                    info,
+                    gr.update(value=error, visible=error_visible)
+                )
+
+            except Exception as e:
+                error_message = f"Unexpected error in detection:\n{str(e)}\n\nStack trace:\n{traceback.format_exc()}"
+                print(error_message)
+                return (
+                    image,  # Return original image
+                    "Detection failed due to unexpected error. See error panel below.",
+                    gr.update(value=error_message, visible=True)
+                )
 
         # Connect language change event
         language_selector.change(
             fn=update_interface,
             inputs=language_selector,
             outputs=[title, language_selector, model_selector, threshold_slider,
-                     input_image, button, output_image, detection_info],
+                     input_image, button, output_image, detection_info, error_panel],
             queue=False
         )
 
         # Connect detection button click event
         button.click(
-            fn=detect_objects,
+            fn=safe_detect_objects,
             inputs=[input_image, language_selector, model_selector, threshold_slider],
-            outputs=[output_image, detection_info]
+            outputs=[output_image, detection_info, error_panel]
         )
 
     return app
 
 
-
-
-
-
-
-
-
-
 # Initialize with default model
 load_model("DETR ResNet-50")