Create app.py

app.py

@@ -0,0 +1,88 @@
+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()