Create src\streamlit_app.py

src//streamlit_app.py

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+# ─────────────────────────────────────────────────────────────────────────────
+# src/streamlit_app.py
+# ─────────────────────────────────────────────────────────────────────────────
+import os
+os.environ["STREAMLIT_HOME"] = "/tmp/.streamlit"
+# 關閉 CORS、關掉使用者統計（可選）
+os.environ["STREAMLIT_SERVER_ENABLE_CORS"] = "false"
+os.environ["STREAMLIT_GATHER_USAGE_STATS"]  = "false"
+# 建目錄
+os.makedirs("/tmp/.streamlit", exist_ok=True)
+
+# ─────────────── ２. 把 DeepFace 的 HOME 指到 /tmp/.deepface ───────────────
+os.environ["DEEPFACE_HOME"] = "/tmp/.deepface"
+# 建好 weights 子目錄，DeepFace 下載權重就不會再 mkdir /
+os.makedirs("/tmp/.deepface/weights", exist_ok=True)
+
+
+
+import streamlit as st
+import cv2, numpy as np, base64, io
+import librosa, joblib
+from deepface import DeepFace
+
+
+# ── 1️⃣ 載入所有模型（DeepFace + 你的語音模型）──
+@st.cache_resource(show_spinner=False)
+def load_models():
+    # a) DeepFace 預熱
+    DeepFace.analyze(
+        img_path = np.zeros((224,224,3), dtype=np.uint8),
+        actions  = ['emotion'],
+        enforce_detection=False
+    )
+    # b) 載入你 commit 到 repo 的語音模型檔案
+   # 取得目前檔案 (streamlit_app.py) 的資料夾路徑
+    root = os.path.dirname(__file__)  
+# 然後從 src/ 底下讀模型檔
+    model_path = os.path.join(root, "voice_model.joblib")
+    audio_model = joblib.load(model_path)
+
+    return audio_model
+
+audio_model = load_models()
+
+
+# ── 2️⃣ 文本情緒分析函式 ─────────────────────────────────────────────────────
+def analyze_text_fn(text: str) -> str:
+    if any(w in text for w in ["開心","快樂","愉快","喜悦","喜悅","歡喜","興奮","高興"]):
+        return "happy"
+    if any(w in text for w in ["生氣","憤怒","不爽","發火","火大","氣憤"]):
+        return "angry"
+    if any(w in text for w in ["傷心","難過","哭","難受","心酸","憂","悲","哀","痛苦","慘","愁"]):
+        return "sad"
+    if any(w in text for w in ["驚訝","意外","嚇","驚詫","詫異","訝異","好奇"]):
+        return "surprise"
+    if any(w in text for w in ["怕","恐懼","緊張","懼","膽怯","畏"]):
+        return "fear"
+    return "neutral"
+
+
+# ── 3️⃣ 語音情緒分析函式 ─────────────────────────────────────────────────────
+def analyze_audio_fn(wav_bytes: bytes) -> str:
+    # 讀 wav bytes
+    y, sr = librosa.load(io.BytesIO(wav_bytes), 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]
+
+
+# ── 4️⃣ Streamlit 介面 ───────────────────────────────────────────────────────
+st.set_page_config(page_title="多模態即時情緒分析", layout="wide")
+st.title("📱 多模態即時情緒分析")
+
+tabs = st.tabs(["🔴 Face（本地測試）", "🎤 上傳語音檔", "⌨️ 輸入文字"])
+with tabs[0]:
+    st.header("Live Face（僅限本地瀏覽器測試）")
+    st.info("⚠️ Hugging Face Spaces 無法直接開啟攝影機，請在本機使用 `streamlit run app.py` 測試。")
+
+    # 這邊如果用 streamlit-webrtc 才能在本地呼叫攝影機
+    # 省略示範，或改成 gradio demo
+
+with tabs[1]:
+    st.header("🎤 上傳 WAV 檔進行分析")
+    # 支援 .wav 上傳
+    wav_file = st.file_uploader("請選擇 .wav 音檔", type=["wav"])
+    if wav_file is not None:
+        # 讀 bytes，呼叫分析函式
+        wav_bytes = wav_file.read()
+        emo = analyze_audio_fn(wav_bytes)
+        st.success(f"🎤 語音偵測到的情緒：**{emo}**")
+
+
+with tabs[2]:
+    st.header("輸入文字進行情緒分析")
+    txt = st.text_area("請在此輸入文字")
+    if st.button("開始分析"):
+        emo = analyze_text_fn(txt)
+        st.success(f"📝 文本偵測到的情緒：**{emo}**")