app.py

# app.py
import gradio as gr
from transformers import pipeline
import pandas as pd
import tempfile
import os

# 初始化情感分析模型
model_name = "uer/roberta-base-finetuned-jd-binary-chinese"
classifier = pipeline("text-classification", model=model_name)

def predict_single(text):
    """单句预测并格式化结果为PDF要求的样式"""
    if not text.strip():
        return {"negative (stars 1, 2 and 3)": "0.0000", "positive (stars 4 and 5)": "0.0000"}
    
    result = classifier(text)[0]
    return {
        "negative (stars 1, 2 and 3)": f"{result['score']:.6f}" if result['label'] == 'positive' else f"{1 - result['score']:.6f}",
        "positive (stars 4 and 5)": f"{1 - result['score']:.6f}" if result['label'] == 'positive' else f"{result['score']:.6f}"
    }

def process_batch(file):
    """处理批量文本并生成带完整结果的CSV"""
    if not file:
        raise gr.Error("请先上传文件")
    
    with open(file.name, "r", encoding="utf-8") as f:
        texts = [line.strip() for line in f if line.strip()]
    
    results = []
    for text in texts:
        pred = classifier(text)[0]
        results.append({
            "文本": text,
            "negative (stars 1, 2 and 3)": f"{pred['score']:.6f}" if pred['label'] == 'positive' else f"{1 - pred['score']:.6f}",
            "positive (stars 4 and 5)": f"{1 - pred['score']:.6f}" if pred['label'] == 'positive' else f"{pred['score']:.6f}"
        })
    
    df = pd.DataFrame(results)
    # 创建临时CSV文件
    temp_dir = tempfile.mkdtemp()
    csv_path = os.path.join(temp_dir, "情感分析结果.csv")
    df.to_csv(csv_path, index=False, encoding="utf-8-sig")
    return df, csv_path

# =============== 界面布局 ===============
with gr.Blocks(title="基于RoBERTa模型的中文情感分析系统", css="""
    .gradio-container {max-width: 900px;
    margin:0 auto !important;
    padding:20px;
    }
    .df-table {width: 100%;
    }
    hi, .markdown-text{
    text-align:center !important;
    }
    """
) as demo:
    # 标题和说明
    gr.Markdown("""
    # 基于RoBERTa模型的中文情感分析系统
    本项目使用RoBERTa中文情感分类模型，实现积极与消极情绪的二分类分析  
    **标签说明**：  
    positive：积极情绪（相当于评分4星和5星）  
    negative：消极情绪（相当于评分1星、2星和3星）
    """)

    # ===== 单句分析标签页 =====
    with gr.Tab("单句情感分析"):
        gr.Markdown("### 请输入一句话进行情感分析")
        text_input = gr.Textbox(label="", placeholder="请输入要分析的文本...", lines=3)
        analyze_btn = gr.Button("分析", variant="primary")
        
        # 结果展示表格
        gr.Markdown("**分析结果**")
        single_result = gr.Dataframe(
            headers=["negative (stars 1, 2 and 3)", "positive (stars 4 and 5)"],
            datatype=["str", "str"],
            elem_classes=["df-table"],
            interactive=False
        )

    # ===== 批量分析标签页 =====
    with gr.Tab("批量情感分析"):
        gr.Markdown("### 上传.txt文件（每行一句）")
        file_input = gr.File(label="", file_types=[".txt"])
        process_btn = gr.Button("上传分析", variant="primary")
        
        # 结果展示
        gr.Markdown("**分析结果：**")
        batch_result = gr.Dataframe(
            headers=["文本", "negative (stars 1, 2 and 3)", "positive (stars 4 and 5)"],
            elem_classes=["df-table"],
            interactive=False
        )
        download_btn = gr.DownloadButton("下载分析结果（CSV）", visible=False)
        temp_file = gr.State()

    # ===== 事件处理 =====
    # 单句分析（修复卡死的关键修改）
    @analyze_btn.click(inputs=text_input, outputs=single_result)
    def handle_single_analysis(text):
        if not text.strip():
            return pd.DataFrame([{
                "negative (stars 1, 2 and 3)": "N/A",
                "positive (stars 4 and 5)": "N/A"
            }])
        result = predict_single(text)
        return pd.DataFrame([result])

    # 批量分析
    @process_btn.click(inputs=file_input, outputs=[batch_result, temp_file, download_btn])
    def handle_batch_analysis(file):
        df, csv_path = process_batch(file)
        return df, csv_path, gr.update(visible=True)
    
    # 文件下载
    download_btn.click(
        fn=lambda x: x,
        inputs=temp_file,
        outputs=download_btn
    )

    # 页脚（居中）
    gr.Markdown("---")
    gr.Markdown("燕山大学文法学院 姜，联系方式: myhopejob@163.com")

# 启动应用
demo.launch(share=True)