Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,44 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+import cv2, numpy as np, base64, io, os
+import librosa, joblib
+from deepface import DeepFace
+
+# 1) 加载所有模型
+@st.cache_resource
+def load_models():
+    DeepFace.analyze(img_path=np.zeros((224,224,3),dtype=np.uint8),
+                     actions=['emotion'], enforce_detection=False)
+    voice_clf = joblib.load("voice_model.joblib")
+    return voice_clf
+
+voice_clf = load_models()
+
+st.title("📱 即時多模態情緒分析")
+
+# 2) 即时人脸
+st.header("🖼 實時人臉情緒")
+img_data = st.camera_input("對準鏡頭")
+if img_data is not None:
+    arr = np.frombuffer(img_data.read(), np.uint8)
+    img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+    res = DeepFace.analyze(img, actions=["emotion"], enforce_detection=False)
+    emo = (res[0] if isinstance(res,list) else res).get("dominant_emotion","unknown")
+    st.write("情緒：", emo)
+
+# 3) 語音上傳
+st.header("🎤 上傳語音情緒")
+audio = st.file_uploader("請上傳 WAV 音檔", type=["wav"])
+if audio is not None:
+    with open("tmp.wav","wb") as f: f.write(audio.getbuffer())
+    y, sr = librosa.load("tmp.wav", sr=None)
+    mf = np.mean(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13).T,axis=0)
+    emo = voice_clf.predict([mf])[0]
+    st.write("情緒：", emo)
+
+# 4) 文字輸入
+st.header("📝 輸入文字情緒")
+txt = st.text_input("打些文字…")
+if txt:
+    # copy 你的 analyze_text_fn
+    emo = analyze_text_fn(txt)
+    st.write("情緒：", emo)