Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+import keras_cv
+import keras
+
+# COCO class labels (80 classes)
+COCO_CLASSES = [
+    "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train",
+    "truck", "boat", "traffic light", "fire hydrant", "stop sign",
+    "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep",
+    "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella",
+    "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard",
+    "sports ball", "kite", "baseball bat", "baseball glove", "skateboard",
+    "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork",
+    "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange",
+    "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair",
+    "couch", "potted plant", "bed", "dining table", "toilet", "tv",
+    "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave",
+    "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
+    "scissors", "teddy bear", "hair drier", "toothbrush",
+]
+
+# Color palette for bounding boxes
+COLORS = [
+    "#FF6B6B", "#4ECDC4", "#45B7D1", "#96CEB4", "#FFEAA7",
+    "#DDA0DD", "#98D8C8", "#F7DC6F", "#BB8FCE", "#85C1E9",
+    "#F8C471", "#82E0AA", "#F1948A", "#AED6F1", "#D7BDE2",
+]
+
+
+def load_model():
+    """Load pretrained YOLOv8 model from KerasCV."""
+    model = keras_cv.models.YOLOV8Detector.from_preset(
+        "yolo_v8_m_pascalvoc",
+        bounding_box_format="xyxy",
+    )
+    return model
+
+
+print("Loading model...")
+model = load_model()
+print("Model loaded!")
+
+
+def detect_objects(image, confidence_threshold=0.5):
+    """Run object detection on a single image."""
+    if image is None:
+        return None
+
+    orig_image = Image.fromarray(image)
+    orig_w, orig_h = orig_image.size
+
+    # Resize for model input
+    input_size = 640
+    resized = orig_image.resize((input_size, input_size))
+    img_array = np.array(resized, dtype="float32")
+    input_batch = np.expand_dims(img_array, axis=0)
+
+    # Run prediction
+    predictions = model.predict(input_batch)
+
+    boxes = predictions["boxes"][0]
+    classes = predictions["classes"][0]
+    confidence = predictions["confidence"][0]
+
+    # Convert to numpy if needed
+    if hasattr(boxes, "numpy"):
+        boxes = boxes.numpy()
+        classes = classes.numpy()
+        confidence = confidence.numpy()
+
+    # Draw results on original image
+    draw = ImageDraw.Draw(orig_image)
+
+    try:
+        font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 16)
+        small_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 13)
+    except OSError:
+        font = ImageFont.load_default()
+        small_font = font
+
+    detections_found = 0
+
+    for i in range(len(boxes)):
+        score = float(confidence[i])
+        if score < confidence_threshold:
+            continue
+
+        cls_id = int(classes[i])
+        if cls_id < 0 or cls_id >= len(COCO_CLASSES):
+            label = f"class_{cls_id}"
+        else:
+            label = COCO_CLASSES[cls_id]
+
+        # Scale boxes from resized coords back to original image
+        x1 = float(boxes[i][0]) * orig_w / input_size
+        y1 = float(boxes[i][1]) * orig_h / input_size
+        x2 = float(boxes[i][2]) * orig_w / input_size
+        y2 = float(boxes[i][3]) * orig_h / input_size
+
+        color = COLORS[cls_id % len(COLORS)]
+
+        # Draw bounding box
+        draw.rectangle([x1, y1, x2, y2], outline=color, width=3)
+
+        # Draw label background + text
+        text = f"{label} {score:.0%}"
+        bbox = draw.textbbox((x1, y1), text, font=font)
+        text_w = bbox[2] - bbox[0]
+        text_h = bbox[3] - bbox[1]
+        draw.rectangle([x1, y1 - text_h - 6, x1 + text_w + 8, y1], fill=color)
+        draw.text((x1 + 4, y1 - text_h - 4), text, fill="white", font=font)
+
+        detections_found += 1
+
+    status = f"Found {detections_found} object(s)" if detections_found else "No objects detected"
+    return orig_image, status
+
+
+# Build the Gradio interface
+with gr.Blocks(title="Keras Object Detection") as demo:
+    gr.Markdown("# Object Detection with KerasCV YOLOv8")
+    gr.Markdown("Upload an image to detect objects using a pretrained YOLOv8 model.")
+
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="Upload Image", type="numpy")
+            threshold = gr.Slider(
+                minimum=0.1,
+                maximum=0.95,
+                value=0.5,
+                step=0.05,
+                label="Confidence Threshold",
+            )
+            run_btn = gr.Button("Detect Objects", variant="primary")
+        with gr.Column():
+            output_image = gr.Image(label="Detections")
+            status_text = gr.Textbox(label="Status", interactive=False)
+
+    run_btn.click(
+        fn=detect_objects,
+        inputs=[input_image, threshold],
+        outputs=[output_image, status_text],
+    )
+
+demo.launch()