Update app.py

app.py

@@ -1,36 +1,33 @@
-# app.py
-import os
-# ─── 解決 DeepFace 無法寫入預設路徑的問題 ─────────────────────────────────────────
-# 將 DeepFace 的快取目錄指向可寫入的 /tmp 之下
-os.environ["DEEPFACE_HOME"] = "/tmp/.deepface"
-
 import gradio as gr
-import numpy as np
-import joblib, io
-import librosa
+import numpy as np, cv2, io, base64
+import librosa, joblib
 from deepface import DeepFace
 
-# ─── 1. 載入模型 ─────────────────────────────────────────────────────────────
-# 我們把模型檔放在跟 app.py 同一層的 voice_model.joblib
-MODEL_PATH = os.path.join(os.path.dirname(__file__), "voice_model.joblib")
-audio_model = joblib.load(MODEL_PATH)
+# 预加载模型
+audio_model = joblib.load("voice_model.joblib")
 
-# ─── 2. 定義各種分析函數 ────────────────────────────────────────────────────
-def analyze_face(frame: np.ndarray):
-    # DeepFace 回傳 dict，裡面有 'dominant_emotion'
-    res = DeepFace.analyze(frame, actions=["emotion"], enforce_detection=False)
-    return frame, res["dominant_emotion"]
+# 人脸情绪分析函数
+def analyze_face(frame):
+    # frame: H×W×3 numpy array, RGB
+    try:
+        res = DeepFace.analyze(frame, actions=['emotion'], enforce_detection=False)
+        return res['dominant_emotion']
+    except Exception as e:
+        return f"Error: {e}"
 
+# 语音情绪分析函数
 def analyze_audio(wav_file):
-    wav_bytes = wav_file.read()
-    # 用 librosa 讀入
-    y, sr = librosa.load(io.BytesIO(wav_bytes), sr=None)
+    if wav_file is None: return "no audio"
+    data = wav_file.read()
+    y, sr = librosa.load(io.BytesIO(data), sr=None)
     mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
-    mf = np.mean(mfccs.T, axis=0)
-    return audio_model.predict([mf])[0]
+    feat = np.mean(mfccs.T, axis=0)
+    pred = audio_model.predict([feat])[0]
+    return pred
 
+# 文本情绪分析函数
 def analyze_text(txt):
-    # 簡單關鍵字 mapping
+    if not txt: return "no text"
     mapping = {
         "😊 happy":   ["開心","快樂","愉快","喜悅","歡喜","興奮","高興","歡"],
         "😠 angry":   ["生氣","憤怒","不爽","發火","火大","氣憤"],
@@ -38,40 +35,33 @@ def analyze_text(txt):
         "😲 surprise":["驚訝","意外","嚇","好奇","驚詫","詫異","訝異"],
         "😨 fear":    ["怕","恐懼","緊張","懼","膽怯","畏"],
     }
-    for emo, kws in mapping.items():
-        if any(w in txt for w in kws):
+    for emo, kw in mapping.items():
+        if any(w in txt for w in kw):
             return emo
-    return "😐 neutral"
+    return "neutral"
 
-# ─── 3. 建立 Gradio 介面 ────────────────────────────────────────────────────
-with gr.Blocks(title="多模態即時情緒分析") as demo:
-    gr.Markdown("## 🤖 多模態即時情緒分析")
+# Gradio 界面
+with gr.Blocks() as demo:
+    gr.Markdown("# 多模態即時情緒分析")
     with gr.Tabs():
-        with gr.TabItem("📷 Live Face"):
-            # 注意要用 gr.components.Camera 或 gr.components…
-            camera = gr.components.Camera(label="請對準鏡頭 (Live)")
-            out_img = gr.Image(label="擷取畫面")
-            out_lbl = gr.Label(label="檢測到的情緒")
-            camera.change(
-                fn=analyze_face,
-                inputs=camera,
-                outputs=[out_img, out_lbl],
-                live=True
-            )
-
-        with gr.TabItem("🎤 上傳語音檔"):
-            wav = gr.File(label="選擇 .wav 檔案", file_types=[".wav"])
-            wav_btn = gr.Button("開始分析")
-            wav_out = gr.Textbox(label="語音偵測到的情緒")
-            wav_btn.click(fn=analyze_audio, inputs=wav, outputs=wav_out)
-
+        with gr.TabItem("📷 即時人臉"):
+            camera = gr.Camera(label="請對準鏡頭")
+            face_out = gr.Textbox(label="偵測到的情緒")
+            camera.change(analyze_face, inputs=camera, outputs=face_out)
+        with gr.TabItem("🎤 上傳語音"):
+            audio = gr.Audio(source="upload", type="file", label="上傳 WAV")
+            audio_out = gr.Textbox(label="語音情緒")
+            audio.change(analyze_audio, inputs=audio, outputs=audio_out)
         with gr.TabItem("⌨️ 輸入文字"):
-            txt = gr.Textbox(label="在此輸入文字", lines=3)
-            txt_btn = gr.Button("開始分析")
-            txt_out = gr.Textbox(label="文字偵測到的情緒")
-            txt_btn.click(fn=analyze_text, inputs=txt, outputs=txt_out)
+            txt_input = gr.Textbox(label="輸入文字")
+            txt_btn = gr.Button("分析文字")
+            txt_out = gr.Textbox(label="文字情緒")
+            txt_btn.click(analyze_text, inputs=txt_input, outputs=txt_out)
+
+    gr.Markdown(
+        "⚠️ Hugging Face Spaces 無法直接呼叫本機攝影機；"
+        "請在手機/電腦瀏覽器使用，或拉到最下方打開 Camera 頁籤測試。"
+    )
 
-# 啟動
 if __name__ == "__main__":
-    # Hugging Face Spaces 上不需要傳 host/port，直接 .launch() 即可
-    demo.launch()
+    demo.launch()