import gradio as gr
from ultralytics import YOLO
import cv2

examples=[["photo/a.png","Image1"],["photo/b.png","Image2"],
          ["photo/c.png","Image3"],["photo/d.png","Image4"],
          ["photo/e.png","Image5"],["photo/f.png","Image6"],
          ["photo/g.png","Image7"],["photo/h.png","Image8"]]


def detect_objects_on_image(image_path):
    image = cv2.imread(image_path)
    model = YOLO("best.pt")
    results = model.predict(image_path)
    result = results[0]
    output = []

    class_names_mapping = {
        "DPHF": "Double Person Helmet",
        "DPNH": "Double Person No Helmet",
        "SPHF": "Single Person Helmet",
        "SPNH": "Single Person No Helmet",
        "NP": "Number Plate"
    }

    # Add more space around the text
    text_padding = 1

    for box in result.boxes:
        x1, y1, x2, y2 = [round(x) for x in box.xyxy[0].tolist()]
        class_id = box.cls[0].item()
        prob = round(box.conf[0].item(), 2)

        class_name = class_names_mapping.get(result.names[class_id], result.names[class_id])

        # Adjust the rectangle coordinates to add more space around the text
        x1 -= text_padding
        y1 -= text_padding
        x2 += text_padding
        y2 += text_padding

        output.append([
            x1, y1, x2, y2, class_name, prob
        ])

        cv2.rectangle(
            image,
            (x1, y1),
            (x2, y2),
            color=(0, 0, 255),
            thickness=1,
            lineType=cv2.LINE_AA
        )

        cv2.putText(image, class_name, (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (36, 255, 12), 2)

    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)



inputs_image = [
    gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
    gr.components.Image(type="numpy", label="Output Image"),
]
demo = gr.Interface(
    fn=detect_objects_on_image,
    inputs=inputs_image,
    outputs=outputs_image,
    title="Biker Helmet and Number Plate Detection",
    examples=examples,
    cache_examples=False,
)

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