Create app.py

app.py

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+# app.py
+import os
+# —— 把 DeepFace 缓存目录指向可写的 /tmp/.deepface
+os.environ["DEEPFACE_HOME"] = "/tmp/.deepface"
+os.makedirs(os.environ["DEEPFACE_HOME"], exist_ok=True)
+
+import gradio as gr
+import cv2
+import numpy as np
+from deepface import DeepFace
+
+def face_emotion(frame: np.ndarray) -> str:
+    """
+    接收 gr.Camera 给出的 RGB ndarray，
+    转成 BGR 后交给 DeepFace 分析情绪。
+    """
+    # RGB → BGR
+    bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+    res = DeepFace.analyze(
+        bgr,
+        actions=['emotion'],
+        enforce_detection=False
+    )
+    # DeepFace 支持 list 或 dict 返回
+    if isinstance(res, list):
+        emo = res[0].get('dominant_emotion', 'unknown')
+    else:
+        emo = res.get('dominant_emotion', 'unknown')
+    return emo
+
+# —— Gradio 前端 —— 
+with gr.Blocks() as demo:
+    gr.Markdown("## 📱 多模態即時情緒分析（示範：即時人臉情緒）")
+    camera = gr.Camera(label="請對準鏡頭", type="numpy")
+    output = gr.Textbox(label="偵測到的情緒")
+    # 实时流：fps 可以调低一点，减轻服务器压力
+    camera.stream(face_emotion, camera, output, fps=5)
+
+if __name__ == "__main__":
+    demo.launch()