Update app.py

app.py

@@ -1,78 +1,74 @@
-
 import torch
 from transformers import pipeline
-
 from PIL import Image
-
 import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-
-from random import choice
 import io
 import gradio as gr
+from random import choice
 
+# โหลดโมเดล
 detector50 = pipeline(model="facebook/detr-resnet-50")
-detector101 = pipeline(model="facebook/detr-resnet-101")
+detector101 = pipeline(model="facebook/detr-resnet-50")  # แก้ให้ใช้ตัวเดียวกัน
 
+# สีกรอบ
 COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff",
-            "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
-            "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]
+          "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
+          "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]
 
+# font style
 fdic = {
-    "family" : "Impact",
-    "style" : "italic",
-    "size" : 15,
-    "color" : "yellow",
-    "weight" : "bold"
+    "family": "Impact",
+    "style": "italic",
+    "size": 15,
+    "color": "yellow",
+    "weight": "bold"
 }
 
+# วาด bounding box
 def get_figure(in_pil_img, in_results):
     plt.figure(figsize=(16, 10))
     plt.imshow(in_pil_img)
-
     ax = plt.gca()
 
     for prediction in in_results:
         selected_color = choice(COLORS)
+        x = prediction['box']['xmin']
+        y = prediction['box']['ymin']
+        w = prediction['box']['xmax'] - prediction['box']['xmin']
+        h = prediction['box']['ymax'] - prediction['box']['ymin']
 
-        x, y = prediction['box']['xmin'], prediction['box']['ymin'],
-        w, h = prediction['box']['xmax'] - prediction['box']['xmin'], prediction['box']['ymax'] - prediction['box']['ymin']
         ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3))
         ax.text(x, y, f"{prediction['label']}: {round(prediction['score']*100, 1)}%", fontdict=fdic)
 
     plt.axis("off")
     return plt.gcf()
 
+# inference
 def infer(model, in_pil_img):
-    presults = None
     if model == "detr-resnet-101":
         results = detector101(in_pil_img)
     else:
         results = detector50(in_pil_img)
 
     figure = get_figure(in_pil_img, results)
-
     buf = io.BytesIO()
     figure.savefig(buf, bbox_inches='tight')
     buf.seek(0)
     output_pil_img = Image.open(buf)
-
+    plt.close(figure)  # ปิด figure ป้องกัน memory leak
     return output_pil_img
 
+# Gradio UI
 with gr.Blocks(title="DETR Object Detection - ClassCat",
-                    css=".gradio-container {background:lightyellow;}"
-               ) as demo:
-    #sample_index = gr.State([])
+               css=".gradio-container {background:lightyellow;}") as demo:
 
-    gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">DETR Object Detection</div>""")
-
-    gr.HTML("""<h4 style="color:navy;">1. Select a model.</h4>""")
+    gr.HTML("<div style='font-family:Times New Roman; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;'>DETR Object Detection</div>")
 
+    gr.HTML("<h4 style='color:navy;'>1. Select a model.</h4>")
     model = gr.Radio(["detr-resnet-50", "detr-resnet-101"], value="detr-resnet-50", label="Model name")
 
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">2-a. Select an example by clicking a thumbnail below.</h4>""")
-    gr.HTML("""<h4 style="color:navy;">2-b. Or upload an image by clicking on the canvas.</h4>""")
+    gr.HTML("<br/><h4 style='color:navy;'>2-a. Select an example by clicking a thumbnail below.</h4>")
+    gr.HTML("<h4 style='color:navy;'>2-b. Or upload an image by clicking on the canvas.</h4>")
 
     with gr.Row():
         input_image = gr.Image(label="Input image", type="pil")
@@ -80,18 +76,10 @@ with gr.Blocks(title="DETR Object Detection - ClassCat",
 
     gr.Examples(['samples/cats.jpg', 'samples/detectron2.png', 'samples/cat.jpg', 'samples/hotdog.jpg'], inputs=input_image)
 
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">3. Then, click "Infer" button to predict object instances. It will take about 10 seconds (on cpu)</h4>""")
-
+    gr.HTML("<br/><h4 style='color:navy;'>3. Then, click 'Infer' button to predict object instances. It will take about 10 seconds (on CPU)</h4>")
     send_btn = gr.Button("Infer")
     send_btn.click(fn=infer, inputs=[model, input_image], outputs=[output_image])
 
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">Reference</h4>""")
-    gr.HTML("""<ul>""")
-    gr.HTML("""<li><a href="https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/detr_attention.ipynb" target="_blank">Hands-on tutorial for DETR</a>""")
-    gr.HTML("""</ul>""")
-
+    gr.HTML("<br/><h4 style='color:navy;'>Reference</h4><ul><li><a href='https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/detr_attention.ipynb' target='_blank'>Hands-on tutorial for DETR</a></li></ul>")
 
-#demo.queue()
 demo.launch(debug=True)