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first edit app.py
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app.py
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import gradio as gr
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import torch
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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import gradio as gr
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import matplotlib.pyplot as plt
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import io
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import re
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import os
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from datetime import datetime
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import spaces
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@spaces.GPU
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def load_model():
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model_id = "oshizo/japanese-sexual-moderation-v2"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForSequenceClassification.from_pretrained(
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model_id,
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problem_type="regression"
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)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = model.to(device)
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return tokenizer, model, device
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@spaces.GPU
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def analyze_text(text, tokenizer, model, device):
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with torch.no_grad():
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encoding = tokenizer([text], padding='max_length', truncation=True, max_length=64, return_tensors="pt")
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encoding = {k: v.to(device) for k, v in encoding.items()}
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score = model(**encoding).logits.item()
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return score
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@spaces.GPU
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def split_text(text, split_by='sentence'):
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if split_by == 'sentence':
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return [sent.strip() for sent in re.split('。|!|?', text) if sent.strip()]
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else: # split by line
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return [line.strip() for line in text.split('\n') if line.strip()]
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@spaces.GPU
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def create_graph(texts, scores):
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fig, ax = plt.subplots(figsize=(12, 6))
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ax.bar(range(len(scores)), scores)
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ax.set_xlabel('テキスト番号')
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ax.set_ylabel('スコア')
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ax.set_title("分析結果")
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ax.set_xticks(range(len(scores)))
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ax.set_xticklabels(range(1, len(scores) + 1))
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plt.tight_layout()
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return fig
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@spaces.GPU
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def create_not_r18_text(texts, scores):
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not_r18_texts = []
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for text, score in zip(texts, scores):
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if score < 0.4:
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not_r18_texts.append(text)
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else:
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not_r18_texts.append('') # 除外された行の位置に空行を挿入
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return '\n'.join(not_r18_texts)
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tokenizer, model, device = load_model()
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@spaces.GPU
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def process_text(text, split_by):
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texts = split_text(text, split_by)
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scores = [analyze_text(t, tokenizer, model, device) for t in texts]
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graph = create_graph(texts, scores)
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not_r18_text = create_not_r18_text(texts, scores)
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result = {
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"texts": texts,
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"scores": scores,
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}
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return result, graph, not_r18_text
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# Gradio インターフェースの定義
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iface = gr.Interface(
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fn=process_text,
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inputs=[
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gr.Textbox(label="テキスト入力"),
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gr.Radio(["sentence", "line"], label="分割方法", value="sentence")
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],
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outputs=[
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gr.JSON(label="分析結果"),
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gr.Plot(label="スコアグラフ"),
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gr.Textbox(label="R18判定除外テキスト")
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],
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title="テキスト分析API",
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description="テキストを入力し、R18判定と分析を行います。"
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)
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# サーバーの起動
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if __name__ == "__main__":
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iface.launch(server_name="0.0.0.0", server_port=7860)
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