import gradio as gr
from textblob import TextBlob

# 此段函数接受文本输入并返回一个字典，包含三项信息：极性（polarity）、主观性（subjectivity）、评价（assessment）
def sentiment_analysis(text: str) -> dict:
    #以下字符串非常重要，该字符串协助Gradio生成MCP工具模式
    """
    Perform sentiment analysis on input text using TextBlob library.
    
    The function calculates polarity (sentiment score from -1 to 1) and subjectivity 
    (how opinionated the text is from 0 to 1), then provides a human-readable assessment.
    
    Args:
        text (str): Input text to be analyzed for sentiment. Should be in English for best results.
        
    Returns:
        dict: Dictionary containing three keys:
            - polarity (float): Sentiment score between -1.0 (negative) and 1.0 (positive)
            - subjectivity (float): Score between 0.0 (objective) and 1.0 (subjective)
            - assessment (str): Categorical classification ('positive', 'negative', or 'neutral')
    """
    # 使用TextBlob分析文本情感
    blob = TextBlob(text)
    sentiment = blob.sentiment
    
    # Format and return sentiment analysis results
    return {
        "polarity": round(sentiment.polarity, 2),  # -1 (negative) to 1 (positive)
        "subjectivity": round(sentiment.subjectivity, 2),  # 0 (objective) to 1 (subjective)
        "assessment": "positive" if sentiment.polarity > 0 else "negative" if sentiment.polarity < 0 else "neutral"
    }

# gr.Interface 同时创建网页用户界面和 MCP 服务器
demo = gr.Interface(
    fn=sentiment_analysis,  # 调用前面定义的sentiment_analysis方法
    inputs=gr.Textbox(placeholder="Enter text to analyze..."),  # 输入和输出组件定义工具的模式
    outputs=gr.JSON(),  # JSON 输出组件确保正确的序列化
    title="Text Sentiment Analysis",  # Interface title
    description="Analyze the sentiment of text using TextBlob"  # Description below title
)

# Launch the web interface when script is run directly
if __name__ == "__main__":
    # 置 mcp_server=True 启用 MCP 服务器
    demo.launch(mcp_server=True)