app.py

import cv2
import gradio as gr
from detection import PearDetectionModel
from classification import predict

# make streaming interface that reads from camera and displays the output with bounding boxes
config = {"model_path": "./weights/best.pt", "classes": ['burn_bbox', 'defected_pear', 'defected_pear_bbox', 'normal_pear', 'normal_pear_bbox']}
model = PearDetectionModel(config)


def classify(image):
    """
    Gradio에서 PIL 이미지를 입력받아 추론 결과를 반환.

    Args:
        image (PIL.Image): 업로드된 이미지.

    Returns:
        str: 모델 예측 결과.
    """
    # 임시 파일 저장 후 처리
    image_path = "temp_image.jpg"
    image.save(image_path)
    return predict(image_path)

def detect(img):
    cls, xyxy, conf = model.inference(img)
    for box, conf in zip(xyxy, conf):
        cv2.rectangle(
            img,
            (int(box[0]), int(box[1])),
            (int(box[2]), int(box[3])),
            (0, 255, 0),
            2,
        )
        cv2.putText(
            img,
            f"{conf:.2f}",
            (int(box[0]), int(box[1])),
            cv2.FONT_HERSHEY_SIMPLEX,
            1,
            (0, 255, 0),
            2,
        )
    cv2.putText(
        img,
        "Class: Normal Pear" if cls == 0 else "Class: Abnormal Pear",
        (0, 50),
        cv2.FONT_HERSHEY_SIMPLEX,
        1,
        (0, 255, 0),
        2,
    )
    return img


css = """.my-group {max-width: 500px !important; max-height: 500px !important;}
            .my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""

with gr.Blocks(css=css) as demo:
    demo.title = "Pear Playground"
    # add markdown
    gr.Markdown("## This is a demo for Pear Playground by AISeed.")
    with gr.Tab(label="Classification"):
        gr.Interface(
            fn=classify,
            inputs=gr.Image(type="pil", label="Upload an image"),
            outputs=gr.Label(num_top_classes=9),
            examples=["examples/1.jpg", "examples/2.jpg"],
            title="비정상 과수 분류기",
            description="경량 모델 ResNet101e 을 활용하여 비정상배 분류"
        )
    with gr.Tab(label="Detection"): 
        with gr.Column(elem_classes=["my-column"]):
            with gr.Group(elem_classes=["my-group"]):

                input_img = gr.Image(sources=["webcam"], type="numpy", streaming=True)
        input_img.stream(
            detect,
            [input_img],
            [input_img],
            time_limit=30,
            stream_every=0.1,
        )

if __name__ == "__main__":

    demo.launch()