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app.py

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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()

best_model_CNN_bs32_lr0.0005_epoch9_acc0.9238.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c1ff55da3574c0126d1ed0970a0e0584d4a3f0aa9e668562e95043b33bdbf946
+size 6017753

requirements.txt

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+torch
+torchaudio
+torchvision
+transformers
+librosa
+matplotlib
+numpy
+openai
+pandas
+tqdm
+scikit-learn
+gradio