to streaming

app.py

@@ -1,9 +1,42 @@
 import gradio as gr
 import requests
 
-def abnormal(image):
+# 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(),
+#             )
+
+#         if r.status_code != 200:
+#             return {'확인불가': 1.0}
+
+#         output_dict = {}
+
+#         for item in r.json()['predictions']:
+#             tag_name = item['tagName']
+#             probability = item['probability']
+#             output_dict[tag_name] = probability
+
+#         return output_dict
+
+#     except Exception as e:
+#         return {[str(e)]: 1.0}
+
+# demo = gr.Interface(abnormal, gr.Image(label="Input Image Component", type="filepath", sources=["webcam"]), "label")
+
+def abnormal_stream(image):
     if (image is None) or (image == ''):
         return {'이미지가 제공되지 않았습니다.': 1.0}
+
     try:
         with open(image, 'rb') as f:
             r = requests.post(
@@ -30,5 +63,12 @@ def abnormal(image):
     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")
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            input_img = gr.Image(sources=["webcam"], type="filepath")
+        with gr.Column():
+            output_img = gr.Label()
+        dep = input_img.stream(abnormal_stream, [input_img], [output_img], every=1)
+
 demo.launch()

flagged/log.csv

@@ -0,0 +1,2 @@
+Input Image Component,output,flag,username,timestamp
+flagged/Input Image Component/51dc4ea78146ecbd3ec2/wine.png,"{""label"": ""\ubab8 \ubabb \uac00\ub204\uae30"", ""confidences"": [{""label"": ""\ubab8 \ubabb \uac00\ub204\uae30"", ""confidence"": 0.51231885}, {""label"": ""\ubaa9\ub9cc\uc9c0\ub2e4"", ""confidence"": 0.29413196}, {""label"": ""\uc5b4\uae68 \ub450\ub4dc\ub9ac\uae30"", ""confidence"": 0.09920414}, {""label"": ""\ub208\ube44\ube44\uae30"", ""confidence"": 0.05283867}, {""label"": ""\uc6b4\uc804\ud558\ub2e4"", ""confidence"": 0.022258094}, {""label"": ""\ud558\ud488"", ""confidence"": 0.015104012}, {""label"": ""\ubea8\ub54c\ub9ac\uae30"", ""confidence"": 0.002993021}, {""label"": ""\uafb8\ubc85\uafb8\ubc85\uc878\ub2e4"", ""confidence"": 0.001151227}]}",,,2025-02-20 14:54:55.603445