Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+def main():
+    # 加载情感分析模型和tokenizer
+    model = AutoModelForSequenceClassification.from_pretrained("WRX020510/CustomModel_twitter", num_labels=3)
+    tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
+    sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+
+    # 加载文本摘要模型
+    summarizer = pipeline("summarization", model="human-centered-summarization/financial-summarization-pegasus")
+
+    # Streamlit 应用界面设计
+    st.title("Financial Text Analysis with Hugging Face Spaces")
+    st.write("Enter a financial text to analyze its sentiment and generate a summary:")
+
+    # 用户输入
+    user_input = st.text_area("Text Input", height=150)
+    
+    if user_input:
+        # 情感分析
+        sentiment_result = sentiment_pipeline(user_input)
+        sentiment = sentiment_result[0]['label']
+        st.write(f"Sentiment (0-2): {sentiment}")
+
+        # 文本摘要
+        summary_result = summarizer(user_input, max_length=130, min_length=30, do_sample=False)
+        summary = summary_result[0]['summary_text']
+        st.write("Summary:")
+        st.write(summary)
+
+if __name__ == "__main__":
+    main()