Update app.py

app.py

@@ -134,58 +134,51 @@ def predict_voice(audio_path: str):
         return {}
 
 # --- 4. 人脸情绪预测 ---
+import gradio as gr
+
 def predict_face(img: np.ndarray):
-    if not has_deepface or img is None:
-        return {}
-    try:
-        res = DeepFace.analyze(img, actions=["emotion"], detector_backend="opencv")
-        if isinstance(res, list):
-            first = res[0] if res else {}
-            emo = first.get("emotion", {}) if isinstance(first, dict) else {}
-        else:
-            emo = res.get("emotion", {}) if isinstance(res, dict) else {}
-        return {k: float(v) for k,v in emo.items()}
-    except Exception as e:
-        print("DeepFace.analyze error:", e)
+    # 你的 DeepFace 分析逻辑
+    if img is None:
         return {}
+    # ...
+    return {"happy": 0.5, "sad": 0.5}  # 举例
 
-# --- 5. Gradio 界面：用 gr.components.Camera ---
 def build_interface():
     with gr.Blocks() as demo:
         gr.Markdown("## 多模態情緒分析示例")
         with gr.Tabs():
-            # 臉部 Tab，仅当 has_deepface=True 时显示
-            if has_deepface:
-                with gr.TabItem("臉部情緒"):
-                    gr.Markdown("### 臉部情緒 (即時 Webcam Streaming 分析)")
-                    with gr.Row():
-                        # 用 Video 捕获 Webcam
-                        webcam = gr.Video(source="webcam", streaming=True, label="攝像頭畫面")
-                        face_out = gr.Label(label="情緒分布")
-                    webcam.stream(fn=predict_face, inputs=webcam, outputs=face_out)
-            else:
-                # 如果本地缺少 deepface，可给用户提示
-                with gr.TabItem("臉部情緒 (跳過)"):
-                    gr.Markdown
-
-
-            # 語音 Tab
+            # 臉部情緒 Tab
+            with gr.TabItem("臉部情緒"):
+                gr.Markdown("### 臉部情緒 (即時 Webcam Streaming 分析)")
+                with gr.Row():
+                    # 这里用 gr.Image(sources="webcam", streaming=True, type="numpy")
+                    webcam = gr.Image(sources="webcam", streaming=True, type="numpy", label="攝像頭畫面")
+                    face_out = gr.Label(label="情緒分佈")
+                # 每帧送到 predict_face
+                webcam.stream(fn=predict_face, inputs=webcam, outputs=face_out)
+
+            # 語音情緒 Tab
             with gr.TabItem("語音情緒"):
                 gr.Markdown("### 語音情緒 分析")
                 with gr.Row():
+                    # 浏览器录音用 source="microphone"
                     audio = gr.Audio(source="microphone", streaming=False, type="filepath", label="錄音")
                     voice_out = gr.Label(label="語音情緒結果")
                 audio.change(fn=predict_voice, inputs=audio, outputs=voice_out)
 
-            # 文字 Tab
+            # 文字情緒 Tab
             with gr.TabItem("文字情緒"):
-                gr.Markdown("### 文字情緒 分析 (规则+Inference API)")
+                gr.Markdown("### 文字情緒 分析 (規則+Inference API)")
                 with gr.Row():
                     text = gr.Textbox(lines=3, placeholder="請輸入中文文字…")
                     text_out = gr.Label(label="文字情緒結果")
+                # 使用 submit 触发
                 text.submit(fn=predict_text_mixed, inputs=text, outputs=text_out)
+
         return demo
 
+
+
 if __name__ == "__main__":
     demo = build_interface()
     # share=True 可在本地测试时生成临时公网链接