app.py

import torch
from transformers import BertTokenizerFast, BertForSequenceClassification
import pandas as pd
import gradio as gr

# 模型和分词器都在根目录
model = BertForSequenceClassification.from_pretrained(".")
tokenizer = BertTokenizerFast.from_pretrained(".")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# 载入人物和著作CSV
figures_df = pd.read_csv("figures.csv")
figure_info = {}
for _, row in figures_df.iterrows():
    figure_info[row['流派']] = (
        f"代表人物：{row['代表人物']}\n"
        f"著作：{row['代表著作']}\n"
        f"身份/简介：{row['身份/简介']}"
    )

id2label = {
    0: "交叉性女性主义",
    1: "差异女性主义",
    2: "激进女性主义",
    3: "自由女性主义"
}

def analyze_paragraph(paragraph):
    import re
    from collections import Counter

    sentences = re.split(r'[。！？]', paragraph)
    sentences = [s.strip() for s in sentences if s.strip()]

    predictions = []
    for s in sentences:
        inputs = tokenizer(s, return_tensors="pt", truncation=True, padding=True, max_length=128).to(device)
        with torch.no_grad():
            outputs = model(**inputs)
            pred_id = torch.argmax(outputs.logits, dim=1).item()
            predictions.append(pred_id)

    count = Counter(predictions)
    total = len(predictions)

    ratio_str = "\n".join([f"{id2label[k]}: {v/total:.1%}" for k, v in count.items()])
    main_label_id = count.most_common(1)[0][0]
    main_label = id2label[main_label_id]
    figures = figure_info.get(main_label, "无对应人物资料")

    return f"主导流派：{main_label}\n\n流派占比：\n{ratio_str}\n\n{figures}"

interface = gr.Interface(
    fn=analyze_paragraph,
    inputs=gr.Textbox(lines=5, placeholder="请输入文本..."),
    outputs="text",
    title="女性主义流派辨析模型",
    description="分析输入文本的女性主义流派占比和主导流派，并给出对应人物著作。"
)

# 🚫 不要加 if __name__ == "__main__"
# ✅ 直接运行 interface
interface.launch()