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import gradio as gr
import whisper
import torch
import numpy as np
from feature import (
AudioTextEmotionModel,
extract_audio_features,
extract_text_features
)
# 設定設備
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 載入模型
emotion_model = AudioTextEmotionModel(audio_input_dim=180, text_input_dim=768, hidden_dim=128, output_dim=3)
emotion_model.load_state_dict(torch.load("model_weights.pth", map_location=device))
emotion_model.to(device)
emotion_model.eval()
# Whisper 模型
whisper_model = whisper.load_model("base")
EMOTION_LABELS = {0: '正面', 1: '中性', 2: '負面'}
# 情緒預測主函式（支援語音 / 文字 / 雙模）
def analyze_input(audio, text_input):
audio_feat = None
text_feat = None
result_text = ""
# 若有語音輸入
if audio:
result = whisper_model.transcribe(audio, language="zh")
transcribed_text = result["text"]
result_text += f"🎧 語音轉文字：「{transcribed_text}」\n"
audio_feat = extract_audio_features(audio)
else:
transcribed_text = None
# 若有文字輸入（用戶輸入或語音轉出）
text = text_input or transcribed_text
if text:
text_feat = extract_text_features(text)
result_text += f"✏️ 文字內容：「{text}」\n"
if audio_feat is None and text_feat is None:
return "請提供語音或文字輸入進行情緒辨識。"
# 製作 tensor 輸入
audio_tensor = (
torch.tensor(audio_feat, dtype=torch.float32).unsqueeze(0).unsqueeze(0).to(device)
if audio_feat is not None else
torch.zeros(1, 1, 180).to(device)
)
text_tensor = (
torch.tensor(text_feat, dtype=torch.float32).unsqueeze(0).unsqueeze(0).to(device)
if text_feat is not None else
torch.zeros(1, 1, 768).to(device)
)
with torch.no_grad():
output = emotion_model(audio_tensor, text_tensor)
pred = torch.argmax(output, dim=1).item()
result_text += f"📊 預測情緒：{EMOTION_LABELS[pred]}"
return result_text
# Gradio Chat UI
with gr.Blocks() as demo:
gr.Markdown("## 🎧 中文語音情緒辨識聊天機器人\n支援語音輸入、文字輸入，或兩者結合分析")
chatbot = gr.Chatbot()
with gr.Row():
audio_input = gr.Audio(sources=["microphone", "upload"], type="filepath", label="語音")
text_input = gr.Textbox(lines=2, placeholder="輸入文字內容...", label="文字")
send_btn = gr.Button("送出分析")
def chat_handler(audio, text, history):
response = analyze_input(audio, text)
history = history or []
history.append(("👤", response))
return history, None, ""
send_btn.click(fn=chat_handler,
inputs=[audio_input, text_input, chatbot],
outputs=[chatbot, audio_input, text_input])
demo.launch(share=True)
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