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# app.py
import gradio as gr
import numpy as np
import joblib, io
import librosa
from deepface import DeepFace
# —— 1. 预加载模型 ——
# DeepFace 热身 + 载入语音模型
audio_model = joblib.load("src/voice_model.joblib")
# 你也可以包一层 try/except
def analyze_face(frame: np.ndarray):
"""
输入：摄像头采到的 RGB numpy 数组
输出：DeepFace 分析出的 dominant_emotion（字符串）
"""
# DeepFace.analyze 接受 RGB np.array
res = DeepFace.analyze(
img_path=frame,
actions=['emotion'],
enforce_detection=False
)
# 兼容 dict / list 返回
if isinstance(res, list):
emo = res[0].get('dominant_emotion', 'unknown')
else:
emo = res.get('dominant_emotion', 'unknown')
return emo
def analyze_audio(wav_file):
"""
输入：上传的 wav（二进制）
输出：语音情绪分类标签
"""
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]
def analyze_text(txt):
"""
简单的基于关键词的中文情绪分析
"""
if any(w in text for w in ["開心","快樂","愉快","喜悅","歡喜","興奮","歡","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"
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")
tabs = gr.Tabs()
with tabs:
with gr.TabItem("🔴 Face（即時）"):
gr.Markdown("⚠️ Spaces 無法直接打開攝像頭，請本機 `python app.py` 測試；手機／電腦瀏覽器可用以下方式：")
camera = gr.Image(
source="webcam",
type="numpy",
label="請對準鏡頭"
)
face_out = gr.Textbox(label="偵測結果")
camera.change(fn=analyze_face, inputs=camera, outputs=face_out)
with gr.TabItem("🎤 上傳語音"):
wav = gr.File(
label="請選擇 .wav 音檔",
file_types=["wav"]
)
audio_out = gr.Textbox(label="偵測結果")
wav.upload(fn=analyze_audio, inputs=wav, outputs=audio_out)
with gr.TabItem("⌨️ 文本輸入"):
txt = gr.Textbox(
label="在此輸入文字",
placeholder="輸入想要分析的句子…"
)
text_btn = gr.Button("開始分析")
text_out = gr.Textbox(label="偵測結果")
text_btn.click(fn=analyze_text, inputs=txt, outputs=text_out)
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False
)
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