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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)
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