Update app.py

app.py

@@ -1,11 +1,11 @@
 !pip install huggingface_hub
-!pip install --upgrade transformers
+!pip install transformers
 !pip install datasets soundfile
 
 ##########################################
 # Step 0: 导入必需的库
 ##########################################
-
+import streamlit as st
 from transformers import pipeline, SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan, AutoModelForCausalLM, AutoTokenizer, pipeline
 from datasets import load_dataset
 from IPython.display import Audio, display
@@ -16,10 +16,19 @@ from google.colab import drive
 
 from huggingface_hub import login
 
+# Streamlit application title
+st.title("Comment reply for you")
+st.write("automative reply")
+
+# Text input for user to enter the comment
+text = st.text_area("Enter your comment", "")
+
 
 ##########################################
 # Step 1：情感分析 - 分析用户评论的情感倾向
 ##########################################
+# Perform tasks when the user clicks the "Comment" button
+if st.button("Comment"):
 
 pipe = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
 
@@ -37,10 +46,15 @@ user_review = "I love the fast delivery, but the product quality could be better
 emotion_results = emotion_classifier(user_review)[0]  # 返回列表中的第一个结果（单条输入）
 
 # 打印所有情感维度及其分数
+
 print("情感分析结果（多维度）：")
 for emotion in emotion_results:
     print(f"{emotion['label']}: {emotion['score']:.4f}")
-
+    
+    st.write("Text:", text)
+    st.write("Label:", max_label)
+    st.write("Score:", max_score)
+    
 # 提取置信度最高的情感标签（可选）
 dominant_emotion = max(emotion_results, key=lambda x: x['score'])
 print("\n主导情感：", dominant_emotion['label'], f"(置信度: {dominant_emotion['score']:.2f})")