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| import streamlit as st | |
| from transformers import pipeline | |
| from translate import Translator | |
| import re | |
| # Load models | |
| def load_models(): | |
| """Loads the sentiment analysis and summarization models.""" | |
| sentiment_analyzer = pipeline("text-classification", model="miltonc/distilbert-base-uncased_ft_5") | |
| summarizer = pipeline("summarization", model="FelixChao/T5-Chinese-Summarization") | |
| return sentiment_analyzer, summarizer | |
| # Max length for models | |
| SENTIMENT_MAX_LENGTH = 512 # Maximum input length for DistilBERT sentiment analysis | |
| SUMMARIZER_MAX_LENGTH = 512 # Maximum input length for T5 summarization (approximate) | |
| def sentiment_analysis(text, sentiment_analyzer): | |
| """Analyzes the sentiment of the input text.""" | |
| try: | |
| result = sentiment_analyzer(text)[0] | |
| return result | |
| except Exception as e: | |
| print(f"sentiment_analysis error for '{text}': {e}. Returning 'sentiment_analysis Failed'") | |
| return {"label": "sentiment_analysis Failed", "score": 0.0} | |
| def summarize_news(text, summarizer): | |
| """Summarizes the input news text.""" | |
| try: | |
| summary = summarizer(text, max_length=30, min_length=10)[0]['summary_text'] | |
| return summary | |
| except Exception as e: | |
| print(f"Summarization error for '{text}': {e}. Returning 'Summarization Failed'") | |
| return "Summarization Failed" | |
| def translate_text(text, target_language='en', source_language='zh-TW'): | |
| """Translates Chinese text to English.""" | |
| translator = Translator(to_lang=target_language, from_lang=source_language) | |
| try: | |
| translation = translator.translate(text) | |
| return translation | |
| except Exception as e: | |
| print(f"Translation error for '{text}': {e}. Returning 'Translation Failed'") | |
| return "Translation Failed" | |
| # Check if the input text is Chinese | |
| def is_chinese(text): | |
| """Checks if the input text contains Chinese characters.""" | |
| return bool(re.search(r'[\u4e00-\u9fff]', text)) | |
| # Main Streamlit app | |
| def main(): | |
| """Main Streamlit application.""" | |
| st.title("AI-Powered Sentiment Analysis and Summarization") | |
| sentiment_analyzer, summarizer = load_models() | |
| text = st.text_area(f"Enter the Chinese text here (Max {max(SENTIMENT_MAX_LENGTH, SUMMARIZER_MAX_LENGTH)} characters).....", height=200) | |
| if st.button("Analyze"): | |
| if text: | |
| if not is_chinese(text): | |
| st.error("Error: Please enter Chinese text.") | |
| return | |
| if len(text) > SENTIMENT_MAX_LENGTH: | |
| st.error(f"Error: Input text for sentiment analysis exceeds the maximum length of {SENTIMENT_MAX_LENGTH} characters. Please shorten your text.") | |
| return | |
| if len(text) > SUMMARIZER_MAX_LENGTH: | |
| st.error(f"Error: Input text for summarization exceeds the maximum length of {SUMMARIZER_MAX_LENGTH} characters. Please shorten your text.") | |
| return | |
| with st.spinner("Analyzing sentiment..."): | |
| text_en = translate_text(text, target_language='en', source_language='zh-TW') | |
| sentiment_result = sentiment_analysis(text_en, sentiment_analyzer) | |
| label_map = {"LABEL_0": 'negative', "LABEL_1": 'neutral', "LABEL_2": 'positive'} | |
| try: | |
| sentiment_label = label_map.get(sentiment_result['label'], 'Unknown') | |
| sentiment_score = sentiment_result['score'] | |
| st.write(f"### Sentiment: {sentiment_label}") | |
| st.write(f"Score: {sentiment_score:.4f}") | |
| except (KeyError, ValueError): | |
| st.write("Error processing sentiment result.") | |
| st.write(sentiment_result) | |
| with st.spinner("Summarizing News..."): | |
| story = summarize_news(text, summarizer) | |
| st.write("### Summarized News:") | |
| st.write(story) | |
| if __name__ == "__main__": | |
| main() |