Update app.py

app.py

@@ -1,3 +1,4 @@
+
 import torch
 from transformers import pipeline
 
@@ -8,11 +9,14 @@ import matplotlib.patches as patches
 
 from random import choice
 import io
-import gradio as gr
 
 detector50 = pipeline(model="facebook/detr-resnet-50")
+
 detector101 = pipeline(model="facebook/detr-resnet-101")
 
+
+import gradio as gr
+
 COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff",
             "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
             "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]
@@ -25,10 +29,11 @@ fdic = {
     "weight" : "bold"
 }
 
+
 def get_figure(in_pil_img, in_results):
     plt.figure(figsize=(16, 10))
     plt.imshow(in_pil_img)
-
+    #pyplot.gcf()
     ax = plt.gca()
 
     for prediction in in_results:
@@ -36,13 +41,17 @@ def get_figure(in_pil_img, in_results):
 
         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()
 
+
 def infer(model, in_pil_img):
+
     results = None
     if model == "detr-resnet-101":
         results = detector101(in_pil_img)
@@ -57,7 +66,8 @@ def infer(model, in_pil_img):
     output_pil_img = Image.open(buf)
 
     return output_pil_img
-    
+
+
 with gr.Blocks(title="DETR Object Detection - ClassCat",
                     css=".gradio-container {background:lightyellow;}"
                ) as demo:
@@ -93,5 +103,5 @@ with gr.Blocks(title="DETR Object Detection - ClassCat",
 
 
 #demo.queue()
-demo.launch(debug=True) 
-
+demo.launch(debug=True)el == "detr-resnet-101":
+