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| # V3 | |
| # Improve speed and user experience | |
| import streamlit as st | |
| from transformers import pipeline | |
| from langdetect import detect | |
| # 加载翻译 pipeline | |
| def load_translation_pipeline(): | |
| return pipeline("translation", model="facebook/m2m100_418M", max_length=256) | |
| # 加载情感分析 pipeline | |
| def load_sentiment_pipeline(): | |
| return pipeline("sentiment-analysis", model="Rocky080808/finetuned-distilbert-base-uncased-finetuned-sst-2-english", max_length=256) | |
| # 定义语言映射 | |
| language_name_map = { | |
| 'en': "English", | |
| 'zh-cn': "Chinese (Simplified)", | |
| 'zh-tw': "Chinese (Traditional)", | |
| 'ja': "Japanese", | |
| 'de': "German", | |
| 'es': "Spanish", | |
| 'fr': "French" | |
| } | |
| # 翻译到英语的函数 | |
| def translate_to_english(text, translation_pipeline): | |
| detected_language = detect(text) | |
| # 语言映射 | |
| language_map = { | |
| 'en': "en", # 英语直接通过 | |
| 'zh-cn': "zh", # Simplified Chinese | |
| 'zh-tw': "zh", # Traditional Chinese | |
| 'ja': "ja", # Japanese | |
| 'de': "de", # German | |
| 'es': "es", # Spanish | |
| 'fr': "fr" # French | |
| } | |
| if detected_language not in language_map: | |
| return None, "Unsupported language" | |
| # 如果检测到是英语，直接返回原文本 | |
| if detected_language == 'en': | |
| return text, "en" | |
| # 翻译为英语 | |
| translated_text = translation_pipeline(text, src_lang=language_map[detected_language], tgt_lang="en") | |
| return translated_text[0]['translation_text'], language_name_map.get(detected_language, detected_language) | |
| # 主程序逻辑 | |
| def main(): | |
| # 加载翻译和情感分析模型 | |
| translation_pipeline = load_translation_pipeline() | |
| sentiment_pipeline = load_sentiment_pipeline() | |
| st.title("Global Customer Reviews Sentiment Analyzer") | |
| st.write("Analyze customer sentiment by their reviews. Please input the customer reviews to get the sentiment analysis result.") | |
| st.write("Support 6 languages: English, Chinese, Japanese, German, Spanish and French.") | |
| st.write("For example, by inputting: I like the product very much!") | |
| st.write("The application will tell you: Very satisfied, the customer is very likely to return and recommend.") | |
| user_input = st.text_input("Enter customer reviews here and press Analyze:") | |
| # 用户点击分析按钮后触发 | |
| if st.button("Analyze"): | |
| if user_input: | |
| # 翻译或直接处理英语 | |
| translated_text, detected_language = translate_to_english(user_input, translation_pipeline) | |
| if detected_language == "Unsupported language": | |
| st.write("The input language is not supported. Please use Chinese, Japanese, German, Spanish, or French.") | |
| else: | |
| # 显示检测语言和翻译结果（如果需要翻译） | |
| st.write(f"Detected language: {language_name_map.get(detected_language, detected_language)}") | |
| st.write(f"Translated Text: {translated_text}" if detected_language != "English" else f"Original Text: {translated_text}") | |
| # 情感分析 | |
| result = sentiment_pipeline(translated_text) | |
| label_str = result[0]["label"] | |
| label = int(label_str.split("_")[-1]) | |
| confidence = result[0]["score"] | |
| # 情感结果映射 | |
| label_to_text = { | |
| 0: "Very dissatisfied, immediate follow-up is required.", | |
| 1: "Dissatisfied, please arrange follow-up.", | |
| 2: "Neutral sentiment, further case analysis is needed.", | |
| 3: "Satisfied, the customer may return for a purchase.", | |
| 4: "Very satisfied, the customer is very likely to return and recommend." | |
| } | |
| sentiment_text = label_to_text.get(label, "Unrecognized sentiment") | |
| st.write(f"Sentiment Analysis Result: {sentiment_text}") | |
| # st.write(f"Confidence Score: {confidence:.2f}") | |
| if __name__ == "__main__": | |
| main() | |