fix

app.py

@@ -1,27 +1,34 @@
 import gradio as gr
 import requests
 
-def greet(image):
-    with open(image, 'rb') as f:
-        r = requests.post(
-            'https://6a051cv20250210-prediction.cognitiveservices.azure.com/customvision/v3.0/Prediction/29f565b7-4710-47a5-8a47-723048ff7ec9/classify/iterations/Iteration2/image',
-            headers={
-                'Prediction-Key': '8uyKSiqRNbG2JLdMjI8AeOzADtORP3jRh5klqQr0JsJrBBt7x7iPJQQJ99BBACYeBjFXJ3w3AAAIACOGHg4K',
-                'Content-Type': 'application/octet-stream',
-            },
-            data=f.read(),
-        )
-    if r.status_code != 200:
-        return {'확인불가': 1.0}
+def abnormal(image):
+    if (image is None) or (image == ''):
+        return {'이미지가 제공되지 않았습니다.': 1.0}
+    try:
+        with open(image, 'rb') as f:
+            r = requests.post(
+                'https://6a051cv20250210-prediction.cognitiveservices.azure.com/customvision/v3.0/Prediction/29f565b7-4710-47a5-8a47-723048ff7ec9/classify/iterations/Iteration2/image',
+                headers={
+                    'Prediction-Key': '8uyKSiqRNbG2JLdMjI8AeOzADtORP3jRh5klqQr0JsJrBBt7x7iPJQQJ99BBACYeBjFXJ3w3AAAIACOGHg4K',
+                    'Content-Type': 'application/octet-stream',
+                },
+                data=f.read(),
+            )
 
-    output_dict = {}
+        if r.status_code != 200:
+            return {'확인불가': 1.0}
 
-    for item in r.json()['predictions']:
-        tag_name = item['tagName']
-        probability = item['probability']
-        output_dict[tag_name] = probability
+        output_dict = {}
 
-    return output_dict
+        for item in r.json()['predictions']:
+            tag_name = item['tagName']
+            probability = item['probability']
+            output_dict[tag_name] = probability
 
-demo = gr.Interface(fn=greet, inputs=gr.Image(label="Input Image Component", type="filepath"), outputs="label")
+        return output_dict
+
+    except Exception as e:
+        return {[str(e)]: 1.0}
+
+demo = gr.Interface(fn=abnormal, inputs=gr.Image(label="Input Image Component", type="filepath"), outputs="label")
 demo.launch()