initial commit

app.py

@@ -0,0 +1,97 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
+import gradio as gr
+from torch.nn import functional as F
+import seaborn
+
+import matplotlib
+import platform
+
+if platform.system() == "Darwin":
+    print("MacOS")
+    matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import io
+from PIL import Image
+
+import matplotlib.font_manager as fm
+
+
+
+
+import util
+
+font_path = r'NanumGothicCoding.ttf'
+fontprop = fm.FontProperties(fname=font_path, size=18)
+
+plt.rcParams["font.family"] = 'NanumGothic'
+
+
+def visualize_attention(sent, attention_matrix, n_words=10):
+    def draw(data, x, y, ax):
+        seaborn.heatmap(data, 
+                        xticklabels=x, square=True, yticklabels=y, vmin=0.0, vmax=1.0, 
+                        cbar=False, ax=ax)
+        
+    # make plt figure with 1x6 subplots
+    fig = plt.figure(figsize=(16, 8))
+    # fig.subplots_adjust(hspace=0.7, wspace=0.2)
+    for i, layer in enumerate(range(1, 12, 2)):
+        ax = fig.add_subplot(2, 3, i+1)
+        ax.set_title("Layer {}".format(layer))
+        draw(attention_matrix[layer], sent if layer > 6 else [], sent if layer in [1,7] else [], ax=ax)
+ 
+    fig.tight_layout()
+    plt.close()
+
+    return fig
+
+
+
+def predict(model_name, text):
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    config = AutoConfig.from_pretrained(model_name)
+    print(config.id2label)
+
+    tokenized_text = tokenizer([text], return_tensors='pt')
+
+    input_tokens = tokenizer.convert_ids_to_tokens(tokenized_text.input_ids[0])
+    print(input_tokens)
+    input_tokens = util.bytetokens_to_unicdode(input_tokens) if config.model_type in ['roberta', 'gpt', 'gpt2'] else input_tokens
+
+    model.eval()
+    output, attention = model(**tokenized_text, output_attentions=True, return_dict=False)
+    output = F.softmax(output, dim=-1)
+    result = {}
+    
+    for idx, label in enumerate(output[0].detach().numpy()):
+        result[config.id2label[idx]] = float(label)
+
+    fig = visualize_attention(input_tokens, attention[0][0].detach().numpy())
+    return result, fig#.logits.detach()#.numpy()#, output.attentions.detach().numpy()
+
+
+if __name__ == '__main__':
+
+    model_name = 'jason9693/SoongsilBERT-beep-base'
+    text = '읿딴걸 홍볿글 읿랉곭 쌑젩낄고 앉앟있냩'
+    # output = predict(model_name, text)
+    
+    # print(output)
+
+    model_name_list = [
+        'jason9693/SoongsilBERT-beep-base'
+    ]
+
+    #Create a gradio app with a button that calls predict()
+    app = gr.Interface(
+        fn=predict, 
+        server_port=26899, 
+        server_name='0.0.0.0', 
+        inputs=[gr.inputs.Dropdown(model_name_list, label="Model Name"), 'text'], outputs=['label', 'plot'], 
+        examples = [[model_name, text]],
+        title="한국어 혐오성 발화 분류기 (Korean Hate Speech Classifier)",
+        description="Korean Hate Speech Classifier with Several Pretrained LM\nCurrent Supported Model:\n1. SoongsilBERT"
+        )
+    app.launch(inline=False)