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import torch
import torch.nn as nn
import torch.nn.functional as F
import librosa
import numpy as np
import gradio as gr
import openai
import os
# Emotion categories
emotions = ["Neutral", "Happy", "Angry", "Sad", "Surprise"]
# CNN model definition
class CNN(nn.Module):
def __init__(self, num_classes):
super(CNN, self).__init__()
self.name = "CNN"
self.conv1 = nn.Conv1d(in_channels=768, out_channels=256, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm1d(256)
self.pool = nn.AdaptiveMaxPool1d(output_size=96)
self.conv2 = nn.Conv1d(in_channels=256, out_channels=128, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm1d(128)
self.conv3 = nn.Conv1d(in_channels=128, out_channels=64, kernel_size=3, padding=1)
self.bn3 = nn.BatchNorm1d(64)
self.fc1 = nn.Linear(64 * 96, 128)
self.dropout = nn.Dropout(0.5)
self.fc2 = nn.Linear(128, num_classes)
def forward(self, x):
x = x.unsqueeze(1)
x = x.permute(0, 2, 1)
x = F.relu(self.bn1(self.conv1(x)))
x = self.pool(x)
x = F.relu(self.bn2(self.conv2(x)))
x = self.pool(x)
x = F.relu(self.bn3(self.conv3(x)))
x = self.pool(x)
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
# Load the trained model
model = CNN(num_classes=5)
model.load_state_dict(torch.load("best_model.pth", map_location="cpu"))
model.eval()
# Extract features from audio file
def extract_feature(audio_path):
y, sr = librosa.load(audio_path, sr=16000)
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40)
max_len = 200
if mfcc.shape[1] > max_len:
mfcc = mfcc[:, :max_len]
else:
pad_width = max_len - mfcc.shape[1]
mfcc = np.pad(mfcc, ((0, 0), (0, pad_width)), mode='constant')
feature = np.tile(mfcc, (int(768 / 40), 1))
feature = torch.tensor(feature, dtype=torch.float32).unsqueeze(0)
return feature
# Full pipeline: emotion detection + GPT response
def predict_and_reply(audio_path):
feature = extract_feature(audio_path)
with torch.no_grad():
output = model(feature)
pred = torch.argmax(output, dim=1).item()
emotion = emotions[pred]
prompt = f"The user sounds {emotion.lower()}. What would you like to say to them?"
try:
openai.api_key = os.getenv("OPENAI_API_KEY", "your-openai-api-key") # Replace with real key or env var
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are an empathetic AI assistant."},
{"role": "user", "content": prompt}
]
)
reply = response['choices'][0]['message']['content']
except Exception as e:
reply = f"❌ GPT Error: {str(e)}"
return f"🎧 Detected Emotion: **{emotion}**\n\n💬 GPT Says:\n{reply}"
#️ Gradio app layout
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("## 🎙️ 情绪检测 + 聊天机器人")
gr.Markdown("上传或录制一段简短的语音片段，我会识别你的情绪，并请求 GPT 做出共情的回应。")
with gr.Row():
with gr.Column():
audio_input = gr.Audio(label="🎧 语音输入", type="filepath", format="wav")
submit_btn = gr.Button("🚀 提交")
with gr.Column():
output_text = gr.Markdown(label="💬 GPT 回复")
submit_btn.click(fn=predict_and_reply, inputs=audio_input, outputs=output_text)
demo.launch()
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