update audio and image

app.py

@@ -1,51 +1,101 @@
 import gradio as gr
 import requests
 from io import BytesIO
+import random
 
-def abnormal_stream(image):
+# 행동별 KSS 점수 설정 (제곱 값 적용)
+kss_mapping = {
+    "운전하다": 1,
+    "눈비비기": 21,
+    "어깨를두드리다": 25,
+    "목을만지다": 25,
+    "하품": 36,
+    "뺨을때리다": 64,
+    "꾸벅꾸벅졸다": 81,
+    "몸못가누기": 100,
+}
+
+risk_images = {
+    "매우 안전": "image1.png",
+    "안전": "image2.png",
+    "주의": "image3.png",
+    "위험": "image4.png",
+    "매우 위험": "image5.png",
+}
+
+def get_risk_status(avg_kss, slope):
+    """
+    윈도우 내 평균 KSS에 따라 5단계 위험 상태를 반환.
+    """
+    if avg_kss < 9:
+        return "매우 안전", "image1.png", None
+    elif avg_kss < 20:
+        return "안전", "image2.png", None
+    elif avg_kss < 40:
+        return "주의", "image3.png", "약간 피로해보여요.잠시 쉬었다 가시는걸 추천드려요.mp3"
+    elif avg_kss < 75:
+        return "위험", "image4.png", random.choice([
+            "졸음이 온다면 차라리 관속에 누우세요.mp3",
+            "미치셨습니까 휴먼.mp3",
+            "지금자면 병원에서 깨실걸요 ㅋㅋ.mp3"
+        ])
+    else:
+        return "매우 위험", "image5.png", "사이렌.mp3"
+
+def analyze_frame(image):
     try:
         byte_io = BytesIO()
         image.save(byte_io, 'png')
         byte_io.seek(0)
 
         r = requests.post(
-            'https://6a051cv20250210-prediction.cognitiveservices.azure.com/customvision/v3.0/Prediction/29f565b7-4710-47a5-8a47-723048ff7ec9/classify/iterations/Iteration2/image',
+            'https://6b003cv20250210-prediction.cognitiveservices.azure.com/customvision/v3.0/Prediction/03fa2862-cb54-4344-b484-630379edffaa/classify/iterations/Iteration4/image',
             headers={
-                'Prediction-Key': '8uyKSiqRNbG2JLdMjI8AeOzADtORP3jRh5klqQr0JsJrBBt7x7iPJQQJ99BBACYeBjFXJ3w3AAAIACOGHg4K',
+                'Prediction-Key': '8ypy2B3ZECnRG0PaYKzpSNvOz8yAhfF7MY2z2wQxSzkweNlhgI4SJQQJ99BBACYeBjFXJ3w3AAAIACOG0WmE',
                 'Content-Type': 'application/octet-stream',
             },
             data=byte_io,
         )
 
         if r.status_code != 200:
-            return {'확인불가': 1.0, r.status_code: 0.0, r.text: 0.0}, None
+            return "결과 없음", "image1.png", None
 
-        output_dict = {}
+        top_predictions_3 = sorted(r.json()['predictions'], key=lambda x: x['probability'], reverse=True)[:3]
+        top_prediction_1 = top_predictions_3[0]
+        action_name = top_prediction_1['tagName']
+        kss_score = kss_mapping.get(action_name, 0)
+        avg_kss = kss_score
 
-        for item in r.json()['predictions']:
-            tag_name = item['tagName']
-            probability = item['probability']
-            output_dict[tag_name] = probability
+        # 위험 상태 및 이미지/음성 정보 가져오기
+        risk_state, risk_image, audio_file = get_risk_status(avg_kss, None)
 
-        with open('alert.wav', 'rb') as f:
-            output_audio = f.read()
-            return output_dict, output_audio
+        results_text = f"🎬 실시간 행동 분석\n\n"
+        for prediction in top_predictions_3:
+            results_text += f"{prediction['tagName']}: {prediction['probability'] * 100:.2f}%\n"
+        results_text += f"🔹 가장 유사한 행동: {action_name} | KSS 점수: {kss_score}\n"
+        results_text += f"📊 현재 위험 수준: {risk_state}\n"
+
+        # 자동 재생을 위한 Audio 컴포넌트
+        if audio_file:
+            audio_output = gr.Audio(value=audio_file, autoplay=True)
+        else:
+            audio_output = None
+
+        return results_text, risk_image, audio_output
 
     except Exception as e:
-        return {str(e): 1.0}
+        return f"오류 발생: {str(e)}", "image1.png", None
 
-with gr.Blocks(
-    analytics_enabled=False,
-    title='졸음운전 알리미',
-    head='''<meta name="theme-color" content="#0f0f11">''',
-) as demo:
+with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
             input_img = gr.Image(sources=["webcam"], type="pil")
         with gr.Column():
-            output_label = gr.Label()
-            output_audio = gr.Audio(autoplay=True)
-        dep = input_img.stream(abnormal_stream, [input_img], [output_label, output_audio])
+            output_label = gr.Textbox(label="🔍 분석 결과")
+            output_image = gr.Image(label="📊 위험 수준", height=200, width=200)
+            output_audio = gr.Audio(label="🔊 음성 경고")
+
+    input_img.stream(analyze_frame, [input_img], [output_label, output_image, output_audio])
 
 if __name__ == "__main__":
-    demo.launch(favicon_path='./favicon.png', show_api=False)
+    demo.launch(debug=True)

app2.py

@@ -0,0 +1,51 @@
+import gradio as gr
+import requests
+from io import BytesIO
+
+def abnormal_stream(image):
+    try:
+        byte_io = BytesIO()
+        image.save(byte_io, 'png')
+        byte_io.seek(0)
+
+        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=byte_io,
+        )
+
+        if r.status_code != 200:
+            return {'확인불가': 1.0, r.status_code: 0.0, r.text: 0.0}, None
+
+        output_dict = {}
+
+        for item in r.json()['predictions']:
+            tag_name = item['tagName']
+            probability = item['probability']
+            output_dict[tag_name] = probability
+
+        with open('alert.wav', 'rb') as f:
+            output_audio = f.read()
+            return output_dict, output_audio
+
+    except Exception as e:
+        return {str(e): 1.0}
+
+with gr.Blocks(
+    analytics_enabled=False,
+    title='졸음운전 알리미',
+    head='''<meta name="theme-color" content="#0f0f11">''',
+) as demo:
+    with gr.Row():
+        with gr.Column():
+            input_img = gr.Image(sources=["webcam"], type="pil")
+        with gr.Column():
+            output_label = gr.Label()
+            output_audio = gr.Audio(autoplay=True)
+        dep = input_img.stream(abnormal_stream, [input_img], [output_label, output_audio])
+
+if __name__ == "__main__":
+    demo.launch(favicon_path='./favicon.png', show_api=False)