Create app.py

app.py

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+import os
+import io
+import PIL
+import torch
+import librosa
+import gradio as gr
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from transformers import BertConfig, BertTokenizer, XLMRobertaForSequenceClassification
+from keras.models import load_model
+
+
+def text_clf(text):
+    # os.environ['CUDA_VISIBLE_DEVICES'] = '1'
+    # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    vocab_file = "vocab.txt"  # 词汇表
+    tokenizer = BertTokenizer(vocab_file)
+    # 加载模型
+    config = BertConfig.from_pretrained("nanaaaa/emotion_chinese_english")
+    model = XLMRobertaForSequenceClassification.from_pretrained("nanaaaa/emotion_chinese_english", config=config)
+    # model.to(device)
+    inputs = tokenizer(text, return_tensors="pt")
+    # inputs.to(device)
+    # 模型推断
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+    # 创建标签和概率列表
+    labels = ["害怕", "高兴喵", "惊喜", "伤心", "生气"]
+    probabilities = probs.detach().cpu().numpy()[0].tolist()
+    # 返回标签和概率列表
+    return {labels[i]: float(probabilities[i]) for i in range(len(labels))}
+
+
+def audio_clf(aud):
+    my_model = load_model('D://speech_mfcc_model.h5')
+
+    def normalizeVoiceLen(y, normalizedLen):
+        nframes = len(y)
+        y = np.reshape(y, [nframes, 1]).T
+        # 归一化音频长度为2s,32000数据点
+        if (nframes < normalizedLen):
+            res = normalizedLen - nframes
+            res_data = np.zeros([1, res], dtype=np.float32)
+            y = np.reshape(y, [nframes, 1]).T
+            y = np.c_[y, res_data]
+        else:
+            y = y[:, 0:normalizedLen]
+        return y[0]
+
+    def getNearestLen(framelength, sr):
+        framesize = framelength * sr
+        # 找到与当前framesize最接近的2的正整数次方
+        nfftdict = {}
+        lists = [32, 64, 128, 256, 512, 1024]
+        for i in lists:
+            nfftdict[i] = abs(framesize - i)
+        print(nfftdict)
+        sortlist = sorted(nfftdict.items(), key=lambda x: x[1])
+        print(sortlist)
+        framesize = int(sortlist[0][0])  # 取最接近当前framesize的那个2的正整数次方值为新的framesize
+        return framesize
+
+    VOICE_LEN = 35000
+    sr, y = aud
+    N_FFT = getNearestLen(0.5, sr)
+    y = normalizeVoiceLen(y, VOICE_LEN)  # 归一化长度
+    mfcc_data = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13, n_fft=N_FFT, hop_length=int(N_FFT / 4))
+    feature = np.mean(mfcc_data, axis=0)
+    # 数据标准化
+    data = feature.tolist()
+    DATA_MEAN = np.mean(feature.tolist(), axis=0)
+    DATA_STD = np.std(feature.tolist(), axis=0)
+    data -= DATA_MEAN
+    data /= DATA_STD
+    data = np.array(data)
+    data = data.reshape((1, data.shape[0], 1))
+    pred = my_model.predict(data)
+    labels1 = ["angry", "fear", "joy", "neutral", "sadness", "surprise"]
+    probabilities1 = pred[0].tolist()
+    return {labels1[i]: float(probabilities1[i]) for i in range(len(labels1))}
+
+
+def cir_clf(L, R):
+    df_4 = pd.read_csv(r'../df_4.csv', encoding="gbk")
+    fig, ax = plt.subplots()
+    r = df_4["R_nor"][int(L):int(R)]
+    theta = (2 * np.pi * df_4["Theta_nor"])[int(L):int(R)]
+
+    def clf_col(x):
+        if -1.5 * np.pi > x > -2 * np.pi:
+            return 5
+        if -1.5 * np.pi < x < -1.1 * np.pi:
+            return 2
+        if -1.1 * np.pi < x < -1 * np.pi:
+            return 3
+        if 1.04 * np.pi > x > 1 * np.pi:
+            return 3
+        if 1.1 * np.pi < x < 1.375 * np.pi:
+            return 4
+        if 1.625 * np.pi > x > 1.375 * np.pi:
+            return 1
+        if 1.625 * np.pi < x < 2 * np.pi:
+            return 0
+
+    theta1 = theta.copy()
+    colors = theta1.apply(lambda x: clf_col(x))
+    ax = plt.subplot(111, projection="polar")
+    c = ax.scatter(theta, r, c=colors, cmap="hsv", alpha=0.6)
+    fig.set_size_inches(10, 10)
+
+    def fig2data(fig):
+        import PIL.Image as Image
+        fig.canvas.draw()
+        w, h = fig.canvas.get_width_height()
+        buf = np.fromstring(fig.canvas.tostring_argb(), dtype=np.uint8)
+        buf.shape = (w, h, 4)
+        buf = np.roll(buf, 3, axis=2)
+        image = Image.frombytes("RGBA", (w, h), buf.tostring())
+        image = np.asarray(image)
+        return image
+
+    return fig2data(fig)
+
+
+with gr.Blocks() as demo:
+    with gr.Tab("Flip Text"):
+        text = gr.Textbox(label="文本哟")
+        text_output = gr.outputs.Label(label="情感呢")
+        text_button = gr.Button("确认")
+
+    with gr.Tab("Flip Audio"):
+        audio = gr.Audio(label="音频捏")
+        audio_output = gr.outputs.Label(label="情感哟")
+        audio_button = gr.Button("确认")
+
+    with gr.Tab("Flip Circle"):
+        cir_l = gr.Slider(0, 30000, step=1)
+        cir_r = gr.Slider(0, 30000, step=1)
+        cir_output = gr.outputs.Image(type='numpy', label="情感圈")
+        cir_button = gr.Button("确认")
+
+    text_button.click(fn=text_clf, inputs=text, outputs=text_output)
+    audio_button.click(fn=audio_clf, inputs=audio, outputs=audio_output)
+    cir_button.click(fn=cir_clf, inputs=[cir_l, cir_r], outputs=cir_output)
+
+demo.launch(share=True)