Add application file

app.py

@@ -0,0 +1,66 @@
+
+import gradio as gr
+import numpy as np
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
+
+emotion_labels = ['喜び', '悲しみ', '期待', '驚き', '怒り', '信頼', '悲しみ', '嫌悪']
+sentiment_labels = ['ポジティブ', 'ニュートラル', 'ネガティブ']
+
+def np_softmax(x):
+    x_exp = torch.exp(torch.tensor(x) - torch.max(torch.tensor(x)))
+    f_x = x_exp / x_exp.sum()
+    return f_x
+
+def emotion_classifier(text):
+    model.eval()
+    tokens = tokenizer(text, truncation=True, return_tensors="pt")
+    tokens.to(model.device)
+    preds = model(**tokens)
+    prob = np_softmax(preds.logits.cpu().detach().numpy()[0])
+    out_dict = {n: p.item() for n, p in zip(emotion_labels, prob)}
+    return out_dict
+
+tokenizer = AutoTokenizer.from_pretrained("cl-tohoku/bert-base-japanese-whole-word-masking")
+model = AutoModelForSequenceClassification.from_pretrained("jingwora/language-emotion-classification-ja", num_labels=8)
+
+def sentiment_classifier(text):
+  clf = pipeline(model="lxyuan/distilbert-base-multilingual-cased-sentiments-student", return_all_scores=True)
+  sentiment = clf(text)
+  sentiment = {item['label']: item['score'] for item in sentiment[0]}
+  sentiment = {sentiment_labels[i]: sentiment[label] for i, label in enumerate(sentiment)} # 日本語ラベル
+  return sentiment
+
+examples = [
+    ["このお店は本当に素晴らしいです！サービスも料理も満足できるものばかりでした。"],
+    ["料理の味が期待外れでした。改善が必要ですね。"],
+    ["サービスは普通ですが、特に不満もありません。"],
+    ["価格と品質のバランスが取れていると思います。"],
+]
+
+
+demo = gr.Blocks(
+    theme="freddyaboulton/dracula_revamped",
+)
+
+with demo:
+    gr.Markdown(
+        """
+    # Emotion and Sentiment Classification
+    Enter Japanese text and get the emotion probabilities and sentiment probablilities.
+    """
+    )
+    text = gr.Textbox(lines=2)
+    with gr.Row():
+        gr.Examples(examples=examples, inputs=text)
+
+    b1 = gr.Button("Emotion Classification")
+    label1 = gr.Label(num_top_classes=8)
+    b1.click(emotion_classifier, inputs=text, outputs=label1)
+
+    b2 = gr.Button("Sentiment Analysis")
+    label2 = gr.Label(num_top_classes=3)
+    b2.click(sentiment_classifier, inputs=text, outputs=label2)
+
+demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+
+gradio==3.36.1
+transformers==4.30.2
+torch==2.0.1