Update app.py

app.py

@@ -1,18 +1,21 @@
 import torch
 from transformers import pipeline
 
-from PIT import Image
+from PIL import Image
 
-import matplotlib.pyplot as plt 
+import matplotlib.pyplot as plt
 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"]
@@ -25,40 +28,46 @@ fdic = {
     "weight" : "bold"
 }
 
+
 def get_figure(in_pil_img, in_results):
-    plt.figure(figsize=(16,10))
+    plt.figure(figsize=(16, 10))
     plt.imshow(in_pil_img)
-
+    #pyplot.gcf()
     ax = plt.gca()
 
     for prediction in in_results:
-        select_color = choice(COLORS)
+        selected_color = choice(COLORS)
 
-        x, y = prediction['box']['xmin'], predictiion['box']['ymin'],
-        w, h = prediction['box']['xmax'] - prediction['box']['xmax'], prediction['box']['ymax'] - prediction['box']['ymin']
-        ax.add_patch(plt.Rectangle((x,y), w, h, fill=False, color=selected_color, linewidth=3))
+        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)
     else:
         results = detector50(in_pil_img)
-        
-    figure = get_figure(in_pil_img)
 
-    buf = io.BytesIO():
+    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)
 
     return output_pil_img
 
- with gr.Blocks(title="DETR Object Detection - ClassCat",
+
+with gr.Blocks(title="DETR Object Detection - ClassCat",
                     css=".gradio-container {background:lightyellow;}"
                ) as demo:
     #sample_index = gr.State([])
@@ -94,5 +103,6 @@ def infer(model, in_pil_img):
 
 #demo.queue()
 demo.launch(debug=True)
-   
-    
+
+
+### EOF ###