nnn111111

app.py

@@ -1,31 +1,21 @@
-
 from fastai.vision.all import *
-from fastai.vision.all import load_learner
 import gradio as gr
 
-
-
-
-"""import pathlib
-plt = platform.system()
-if plt == 'Windows': pathlib.WindowsPath = pathlib.PosixPath """
-
-"""import pathlib
-temp=pathlib.PosixPath
-pathlib.PosixPath=pathlib.WindowsPath"""
+# import pathlib
+# temp = pathlib.PosixPath
+# pathlib.PosixPath = pathlib.WindowsPath
 
 cap_labels = ['AIDC F-CK-1 Ching-kuo', 'Boeing F-15EX Eagle II', 'Chengdu J-10 (China)', 'Chengdu J-20 (China)', 'Dassault Rafale', 'English Electric Lightning', 'Eurofighter Typhoon', 'Focke-Wulf Fw 190', 'General Dynamics F-16 Fighting Falcon aircraft', 'Grumman F-14 Tomcat', 'KAI KF-21 Boramae', 'Lockheed Martin F-22 Raptor', 'Lockheed Martin F-35 Lightning II', 'Lockheed P-80 Shooting Star', 'Lockheed YF-12', 'McDonnell Douglas F-4 Phantom II', 'Mikoyan MiG-29', 'Mikoyan Mig-31', 'Mikoyan-Gurevich MiG-25', 'Shenyang FC-31 Gyrfalcon', 'Sukhoi Su-27', 'Sukhoi Su-35 (Russia)', 'Sukhoi Su-57']
 
-model = load_learner(f'latest.pkl')
+
+model = load_learner('latest.pkl')
 
 def recognize_image(image):
     pred, idx, probs = model.predict(image)
     return dict(zip(cap_labels, map(float, probs)))
 
-
-#!export
 image = gr.inputs.Image(shape=(192,192))
-label = gr.outputs.Label()
+label = gr.outputs.Label(num_top_classes=5)
 examples = [
     'images/euro.jpg',
     'images/F-16.jpg',