app.py

import gradio as gr
from transformers import pipeline

# 直接引用 Hugging Face Hub 上的模型
MODEL_REPO = "tabularisai/multilingual-sentiment-analysis"

# 初始化情感分析 pipeline
classifier = pipeline("sentiment-analysis", model=MODEL_REPO)

def analyze_sentiment(text):
    try:
        results = classifier(text)
        # 处理单条和多条输入
        if isinstance(results, list):
            output_lines = []
            for result in results:
                label = result.get('label', '')
                score = result.get('score', 0)
                output_lines.append(f"Label: {label}, Confidence: {score:.2%}")
            return "\n".join(output_lines)
        else:
            label = results.get('label', '')
            score = results.get('score', 0)
            return f"Label: {label}, Confidence: {score:.2%}"
    except Exception as e:
        return f"Error: {str(e)}"

# Gradio Web 界面
with gr.Blocks(title="Multilingual Sentiment Analysis") as app:
    gr.Markdown("## 🌍 Multilingual Sentiment Analysis\nAnalyze sentiment for texts in multiple languages using [tabularisai/multilingual-sentiment-analysis](https://huggingface.co/tabularisai/multilingual-sentiment-analysis).")

    prompt_input = gr.Textbox(label="Enter your text here", lines=3, placeholder="Type or paste your sentence...")
    output = gr.Textbox(label="Sentiment Result", lines=2)
    btn = gr.Button("Analyze Sentiment")
    btn.click(
        fn=analyze_sentiment,
        inputs=prompt_input,
        outputs=output
    )

if __name__ == "__main__":
    app.launch()