remove gradio file

.gitignore

@@ -1 +0,0 @@
-.venv

app.py

@@ -1,43 +0,0 @@
-from ultralytics import YOLO
-import PIL
-from PIL import Image
-import gradio as gr
-import numpy as np
-import os
-
-def predict(input_img) -> tuple[np.ndarray | PIL.Image.Image | str, list[tuple[np.ndarray | tuple[int, int, int, int], str]]]:
-    res = model(input_img)
-    if len(res) == 0:
-        return input_img, "No watermark detected"
-    res = res[0]
-    # convert res.boxes.xyxy to a tuple of (x1, y1, x2, y2)
-    bbox = res.boxes.xyxy[0].tolist()
-    bbox = (int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3]))
-    # convert res.boxes.cls to a string
-    label = res.boxes.cls[0]
-    str_label = "Watermark is a logo" if label == 0 else "Watermark is a text"
-    print(bbox, str_label)
-    return input_img, [(bbox, str_label)]
-
-
-gradio_app = gr.Interface(
-    predict,
-    inputs=gr.Image(label="Update your watermaked image", sources=['upload'], type="pil"),
-    # output displays the image with the bounding boxes
-    outputs=gr.AnnotatedImage(),
-    title="Detect Watermark in Images",
-    examples=[
-        os.path.join(os.path.dirname(__file__), "samples/example_text1.jpg"),
-        os.path.join(os.path.dirname(__file__), "samples/example_text2.jpg"),
-        os.path.join(os.path.dirname(__file__), "samples/example_text3.jpg"),
-        os.path.join(os.path.dirname(__file__), "samples/example_logo1.jpg"),
-        os.path.join(os.path.dirname(__file__), "samples/example_logo2.jpg"),
-        os.path.join(os.path.dirname(__file__), "samples/example_logo3.jpg"),
-    ],
-    allow_flagging="never"
-)
-
-
-if __name__ == "__main__":
-    model = YOLO("best.pt")
-    gradio_app.launch(debug=True)

requirements.txt

@@ -1,2 +0,0 @@
-gradio
-ultralytics