fix: bug

app.py

@@ -1,65 +1,48 @@
 import gradio as gr
-import json
+import torch
+from PIL import Image
+import numpy as np
 
-from ultralyticsplus import YOLO, render_result
+model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')
 
-# Model Heading and Description
-model_heading = "YOLOv8 NDL-DocL Datasets"
-description = """YOLOv8 NDL-DocL Datasets Gradio demo for object detection. Upload an image or click an example image to use."""
+import json
 
-article = "<p style='text-align: center'>YOLOv5 NDL-DocL Datasets is an object detection model trained on the <a href=\"https://github.com/ndl-lab/layout-dataset\">NDL-DocL Datasets</a>.</p>"
+def yolo(im, size=1024):
+    g = (size / max(im.size))  # gain
+    im = im.resize((int(x * g) for x in im.size), Image.LANCZOS)  # resize
 
-image_path= [
-       
-    ['『源氏物語』(東京大学総合図書館所蔵).jpg', 0.25, 0.45],
-     ['『源氏物語』(京都大学所蔵).jpg', 0.25, 0.45],
-      ['『平家物語』(国文学研究資料館提供).jpg', 0.25, 0.45]
-]
+    results = model(im)  # inference
+    if results.imgs:
+        writable_img = np.copy(results.imgs[0])  # Create a writable copy of the image data
+        results.imgs[0] = writable_img
+        results.render()  # updates results.imgs with boxes and labels
 
-# Load YOLO model
-model = YOLO('model_- 19 may 2024 15_13.pt')
+    df = results.pandas().xyxy[0].to_json(orient="records")
+    res = json.loads(df)
 
-def yolov8_img_inference(
-    image: gr.Image = None,
-    conf_threshold: gr.Slider = 0.25,
-    iou_threshold: gr.Slider = 0.45,
-):
-    """
-    YOLOv8 inference function
-    Args:
-        image: Input image
-        conf_threshold: Confidence threshold
-        iou_threshold: IOU threshold
-    Returns:
-        Rendered image
-    """
-    results = model.predict(image, conf=conf_threshold, iou=iou_threshold)
-    render = render_result(model=model, image=image, result=results[0])            
+    return [
+        Image.fromarray(results.imgs[0]),
+        res
+    ]
 
-    json_data = json.loads(results[0].tojson())
-    
-    return render, json_data
 
-    
-inputs_image = [
-    gr.Image(type="filepath", label="Input Image"),
-    gr.Slider(minimum=0.0, maximum=1.0, value=0.25, step=0.05, label="Confidence Threshold"),
-    gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold"),
+inputs = gr.Image(type='pil', label="Original Image")
+outputs = [
+    gr.Image(type="pil", label="Output Image"), 
+    gr.JSON()
 ]
 
-outputs_image =[
-    gr.Image(type="filepath", label="Output Image"),
-    gr.JSON(label="Output JSON")
+title = "YOLOv5 NDL-DocL Datasets"
+description = "YOLOv5 NDL-DocL Datasets Gradio demo for object detection. Upload an image or click an example image to use."
+article = "<p style='text-align: center'>YOLOv5 NDL-DocL Datasets is an object detection model trained on the <a href=\"https://github.com/ndl-lab/layout-dataset\">NDL-DocL Datasets</a>.</p>"
+
+# <a href=\"aaa\"></a>
+
+examples = [
+    ['『源氏物語』(東京大学総合図書館所蔵).jpg'],
+    ['『源氏物語』(京都大学所蔵).jpg'],
+    ['『平家物語』(国文学研究資料館提供).jpg']
 ]
-demo = gr.Interface(
-    fn=yolov8_img_inference,
-    inputs=inputs_image,
-    outputs=outputs_image,
-    title=model_heading,
-    description=description,
-    examples=image_path,
-    article=article,
-    cache_examples=False
-)
+demo = gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples)
 
 demo.launch(share=False)

model_- 19 may 2024 15_13.pt → best.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8acb10e585c68b31142f2f4470606da52ce054ab467368b6bfe22ca547620e12
-size 136737193
+oid sha256:497dd54a738a54abfc938507c16b77e4e46930372e6e189ae87895aa5bba0b7c
+size 173348189