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| import streamlit as st | |
| from transformers import pipeline | |
| # 加载模型 | |
| def load_pipelines(): | |
| sentiment_pipeline = pipeline(model="KeviniveK/CustomModel_IMDB") | |
| translation_pipeline = pipeline( | |
| "translation_en_to_zh", | |
| model="Helsinki-NLP/opus-mt-en-zh" | |
| ) | |
| return sentiment_pipeline, translation_pipeline | |
| sentiment_pipeline, translation_pipeline = load_pipelines() | |
| # 标签映射 | |
| label_map = { | |
| "LABEL_0": "差评 | Negative", | |
| "LABEL_1": "好评 | Positive" | |
| } | |
| # 页面标题 | |
| st.title("影评分析与翻译 | Sentiment Analysis & Translation") | |
| st.write("请输入一段英文影评，我们将分析其情感并翻译成中文。") | |
| st.write("Enter an English movie review below. The app will analyze its sentiment and translate it into Chinese.") | |
| # 用户输入 | |
| user_input = st.text_area("英文影评输入 | Enter English Movie Review", height=150) | |
| if user_input: | |
| # 情感分析 | |
| result = sentiment_pipeline(user_input) | |
| sentiment_raw = result[0]["label"] | |
| sentiment = label_map.get(sentiment_raw, sentiment_raw) | |
| confidence = result[0]["score"] | |
| # 翻译 | |
| translation = translation_pipeline(user_input) | |
| translated_text = translation[0]["translation_text"] | |
| # 显示结果 | |
| st.subheader("情感分析结果 | Sentiment Analysis Result") | |
| st.write(f"**情感 (Sentiment):** {sentiment}") | |
| st.write(f"**置信度 (Confidence):** {confidence:.2f}") | |
| st.subheader("中文翻译结果 | Chinese Translation") | |
| st.write(translated_text) | |
| if __name__ == "__main__": | |
| pass |