app.py

"""
YOLOv8 Object Detection
Courses: 100 ch3, 320 ch7, 321 ch5, 360 ch4
"""

import gradio as gr
import numpy as np
from ultralytics import YOLO

# Load model once at startup (auto-downloads yolov8n.pt ~6MB)
model = YOLO("yolov8n.pt")


def detect(image: np.ndarray, conf_threshold: float, iou_threshold: float):
    if image is None:
        return None, []

    results = model(image, conf=conf_threshold, iou=iou_threshold)
    annotated = results[0].plot()

    detections = []
    for box in results[0].boxes:
        detections.append({
            "class": model.names[int(box.cls)],
            "confidence": round(float(box.conf), 3),
            "bbox": [round(x, 1) for x in box.xyxy[0].tolist()],
        })

    summary = f"Detected **{len(detections)}** objects"
    if detections:
        class_counts = {}
        for d in detections:
            class_counts[d["class"]] = class_counts.get(d["class"], 0) + 1
        summary += ": " + ", ".join(f"{v}x {k}" for k, v in sorted(class_counts.items()))

    return annotated, summary


with gr.Blocks(title="YOLOv8 Object Detection") as demo:
    gr.Markdown(
        "# YOLOv8 Object Detection\n"
        "Upload an image for real-time object detection. Adjust confidence and IoU thresholds.\n"
        "*Courses: 100 Deep Learning, 320 Vacuum Robot, 321 Robot Arm, 360 Autonomous Driving*"
    )

    with gr.Row():
        with gr.Column(scale=1):
            input_image = gr.Image(type="numpy", label="Upload Image")
            conf_slider = gr.Slider(0.1, 0.9, value=0.25, step=0.05, label="Confidence Threshold")
            iou_slider = gr.Slider(0.1, 0.9, value=0.45, step=0.05, label="IoU Threshold")
            run_btn = gr.Button("Detect Objects", variant="primary")

        with gr.Column(scale=2):
            output_image = gr.Image(label="Detection Results")
            output_text = gr.Markdown(label="Summary")

    run_btn.click(
        fn=detect,
        inputs=[input_image, conf_slider, iou_slider],
        outputs=[output_image, output_text],
    )

    gr.Examples(
        examples=[
            ["examples/street.jpg", 0.25, 0.45],
            ["examples/room.jpg", 0.25, 0.45],
            ["examples/traffic.jpg", 0.3, 0.45],
        ],
        inputs=[input_image, conf_slider, iou_slider],
    )

if __name__ == "__main__":
    demo.launch()