Update app.py

app.py

@@ -1,43 +1,32 @@
 import torch
 from transformers import pipeline
-
 from PIL import Image
-
 import matplotlib.pyplot as plt
-
-from random import choice
 import io
 
 detector50 = pipeline(model="TuningAI/DETR-BASE_Marine")
 
 import gradio as gr
 
-COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff",
-            "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
-            "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]
-
 fdic = {
-    "family" : "Impact",
     "style" : "italic",
     "size" : 10,
     "color" : "red",
     "weight" : "bold"
 }
-
-
+labels_ = { "LABEL_0":"None" , "LABEL_1": "Boat" ,"LABEL_2": "Car" ,"LABEL_3" :  "Dock" , "LABEL_4" : "Jetski" ,"LABEL_5" : "Lift"}
 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)
+        selected_color ="#008000"
 
         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)
+        ax.text(x, y, f"{labels_[prediction['label']]}: {round(prediction['score']*100, 1)}%", fontdict=fdic)
 
     plt.axis("off")
 
@@ -61,6 +50,6 @@ with gr.Blocks(title="DETR Object Detection") as demo:
     with gr.Row():
         input_image = gr.Image(label="Input image", type="pil")
         output_image = gr.Image(label="Output image with predicted instances", type="pil")
-    send_btn = gr.Button("Infer")
+    send_btn = gr.Button("start")
     send_btn.click(fn=infer, inputs=input_image, outputs=[output_image])
 demo.launch(debug=True)