Add cat/dog detector app

app.py

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+import gradio as gr
+import torch
+from transformers import DetrImageProcessor, DetrForObjectDetection
+from PIL import Image, ImageDraw, ImageFont
+import requests # To handle image URLs if needed, but we focus on uploads
+
+# Load the model and processor
+# Using revision="no_timm" to potentially avoid the timm dependency if not installed,
+# but it's safer to include timm in requirements.txt
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-101-dc5")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-101-dc5")
+
+# Define class names for filtering (check model.config.id2label for exact mapping)
+# Common COCO IDs: cat=16, dog=17 (0-indexed) but let's use labels
+# We need to get the actual labels the model uses
+id2label = model.config.id2label
+target_labels = ["cat", "dog"]
+target_ids = [label_id for label_id, label in id2label.items() if label in target_labels]
+
+# Colors for bounding boxes (simple example)
+colors = {"cat": "red", "dog": "blue"}
+
+def detect_objects(image_input):
+    """
+    Detects cats and dogs in the input image using DETR.
+
+    Args:
+        image_input (PIL.Image.Image): Input image.
+
+    Returns:
+        PIL.Image.Image: Image with bounding boxes drawn around detected cats/dogs.
+    """
+    if image_input is None:
+        return None
+
+    # Convert Gradio input (if numpy) to PIL Image, although type="pil" should handle this
+    if not isinstance(image_input, Image.Image):
+         image = Image.fromarray(image_input)
+    else:
+         image = image_input.copy() # Work on a copy
+
+    # Preprocess the image
+    inputs = processor(images=image, return_tensors="pt")
+
+    # Perform inference
+    outputs = model(**inputs)
+
+    # Post-process the results
+    # Convert outputs (bounding boxes and class logits) to COCO API format
+    target_sizes = torch.tensor([image.size[::-1]]) # (height, width)
+    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0] # Lower threshold (e.g., 0.5) might find more objects
+
+    # Draw bounding boxes for cats and dogs
+    draw = ImageDraw.Draw(image)
+    try:
+        # Use a default font or specify a path to a .ttf file if available in the Space
+        font = ImageFont.load_default()
+    except IOError:
+        print("Default font not found. Using basic drawing without text.")
+        font = None
+
+    detections_found = False
+    for score, label_id, box in zip(results["scores"], results["labels"], results["boxes"]):
+        label_id = label_id.item()
+        if label_id in target_ids:
+            detections_found = True
+            box = [round(i, 2) for i in box.tolist()]
+            label = id2label[label_id]
+            box_color = colors.get(label, "green") # Default to green if label not in colors dict
+
+            print(f"Detected {label} with confidence {round(score.item(), 3)} at {box}")
+
+            # Draw rectangle
+            draw.rectangle(box, outline=box_color, width=3)
+
+            # Draw label text
+            if font:
+                text = f"{label}: {score.item():.2f}"
+                text_width, text_height = font.getsize(text) if hasattr(font, 'getsize') else (50, 10) # Estimate size if getsize not available
+                text_bg_coords = [(box[0], box[1]), (box[0] + text_width + 4, box[1] + text_height + 4)]
+                draw.rectangle(text_bg_coords, fill=box_color)
+                draw.text((box[0] + 2, box[1] + 2), text, fill="white", font=font)
+
+    if not detections_found:
+        print("No cats or dogs detected with the current threshold.")
+        # Optionally add text to the image saying nothing was found
+        # draw.text((10, 10), "No cats or dogs detected", fill="black", font=font)
+
+
+    return image
+
+# Create the Gradio interface
+title = "Cat & Dog Detector (using DETR ResNet-101)"
+description = ("Upload an image and the model will draw bounding boxes "
+               "around detected cats and dogs. Uses the facebook/detr-resnet-101-dc5 model from Hugging Face.")
+
+iface = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="pil", label="Upload Image"),
+    outputs=gr.Image(type="pil", label="Output Image with Detections"),
+    title=title,
+    description=description,
+    examples=[
+        # You can add paths to example images if you upload them to your space
+        # Or provide URLs
+         ["http://images.cocodataset.org/val2017/000000039769.jpg"], # Example image URL with cats
+         ["https://storage.googleapis.com/petbacker/images/blog/2017/dog-and-cat-cover.jpg"] # Example image with dog and cat
+    ],
+    allow_flagging="never" # You can change flagging options if needed
+)
+
+# Launch the app
+iface.launch()

requirements.txt

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+torch>=1.9.0
+transformers>=4.15.0
+Pillow>=9.0.0
+gradio>=3.0.0
+requests>=2.25.0
+timm>=0.5.4