import json
import os
import gradio as gr
from textblob import TextBlob
from snownlp import SnowNLP

def sentiment_analysis(text: str) -> str:
    '''
    Analyse the sentiment of the given text

    Args:
        text (str): The text to analyse

    Returns:
        str: A JSON string containing polarity, subjectivity, and assessment
    '''
    blob = TextBlob(text)
    sentiment = blob.sentiment
    
    result = {
        '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'
    }

    return json.dumps(result)


def chinese_sentiment_analysis(text: str) -> str:
    '''
    Analyse the sentiment of the given Chinese text

    Args:
        text (str): The text to analyse

    Returns:
        str: A JSON string containing polarity, subjectivity, and assessment
    '''
    s = SnowNLP(text)
    
    # SnowNLP 的情感分析返回值範圍是 0 到 1，0 表示負面，1 表示正面
    polarity = s.sentiments
    subjectivity = None # SnowNLP 不提供主觀性評估，可設為 None 或其他值
    
    result = {
        'polarity': round(polarity, 2), # 0 (negative) to 1 (positive)
        'subjectivity': subjectivity, # SnowNLP 不提供主觀性評估
        'assessment': 'positive' if polarity > 0.5 else 'negative' if polarity < 0.5 else 'neutral'
    }

    return json.dumps(result)


def batch_sentiment_analysis(file_path: str) -> str:
    '''
    Batch process sentiment analysis from JSON file
    
    Args:
        file_path (str): Path to JSON file with {text: ''} format
    
    Returns:
        str: JSON string with analysis results in values
    '''
    with open(file_path, 'r', encoding = 'utf-8') as f:
        data = json.load(f)
    
    for key in data:
        analysis_result = json.loads( sentiment_analysis(key) )
        data[key] = analysis_result
    
    dir_name = os.path.dirname(file_path)
    base_name = os.path.basename(file_path)
    output_path = os.path.join(dir_name, f'processed_{base_name}')
    with open(output_path, 'w', encoding = 'utf-8') as f:
        json.dump(data, f, ensure_ascii = False, indent = 2)
    
    return output_path


def batch_chinese_sentiment_analysis(file_path: str) -> str:
    '''
    Batch process Chinese sentiment analysis from JSON file
    
    Args:
        file_path (str): Path to JSON file with {text: ''} format
    
    Returns:
        str: JSON string with analysis results in values
    '''
    with open(file_path, 'r', encoding = 'utf-8') as f:
        data = json.load(f)
    
    for key in data:
        analysis_result = json.loads( chinese_sentiment_analysis(key) )
        data[key] = analysis_result
    
    dir_name = os.path.dirname(file_path)
    base_name = os.path.basename(file_path)
    output_path = os.path.join(dir_name, f'processed_{base_name}')
    with open(output_path, 'w', encoding = 'utf-8') as f:
        json.dump(data, f, ensure_ascii = False, indent = 2)
    
    return output_path

# gradio interface
demo = gr.TabbedInterface(
    [
        gr.Interface(
            fn = sentiment_analysis, 
            inputs = gr.Textbox(placeholder = 'Enter text to analyse...'), 
            outputs = gr.Textbox(), 
            title = 'Text Sentiment Analysis', 
            description = 'Analyse the sentiment of text using TextBlob', 
            api_name = 'sentiment_analysis'
        ),
        gr.Interface(
            fn = chinese_sentiment_analysis, 
            inputs = gr.Textbox(placeholder = '要分析的中文...'), 
            outputs = gr.Textbox(), 
            title = '中文情感分析', 
            description = 'Analyse the sentiment of Chinese text using SnowNLP', 
            api_name = 'chinese_sentiment_analysis'
        ),
        gr.Interface(
            fn = batch_sentiment_analysis,
            inputs = gr.File(label = 'Upload JSON File'),
            outputs = gr.File(label = 'Download Results'),
            title = 'Batch Sentiment Analysis',
            description = 'Process JSON file with multiple texts (English)',
            api_name = 'batch_sentiment_analysis'
        ),
        gr.Interface(
            fn = batch_chinese_sentiment_analysis,
            inputs = gr.File(label = '上傳JSON文件'),
            outputs = gr.File(label = '下載分析結果'),
            title = '批量中文情感分析',
            description = 'Batch process Chinese sentiment analysis from JSON file',
            api_name = 'batch_chinese_sentiment_analysis'
        )
    ],
    [
        'sentiment analysis',
        '中文情感分析',
        'batch processing',
        '批次中文情感分析'
    ]
)

# Launch the interface and MCP server
if __name__ == '__main__':
    demo.launch(mcp_server = True)