Update app.py

app.py

@@ -1,84 +1,45 @@
+# import part
 import streamlit as st
-import numpy as np
 from transformers import pipeline
+import numpy as np  # Import numpy
 
-# 环境稳定化配置
-@st.cache_resource
-def load_pipelines():
-    try:
-        sentiment_pipe = pipeline(
-            "text-classification",
-            model="EmmaL1/CustomModel_amazon",
-            return_all_scores=True,
-            device="cpu",
-            torch_dtype="float32"
-        )
-        qa_pipe = pipeline(
-            "question-answering",
-            model="distilbert/distilbert-base-cased-distilled-squad",
-            device="cpu"
-        )
-        return sentiment_pipe, qa_pipe
-    except Exception as e:
-        st.error(f"Initialization Error: {str(e)}")
-        st.stop()
+# Initialize sentiment analysis pipeline
+sentiment_pipeline = pipeline(model="EmmaL1/CustomModel_amazon")
 
-sentiment_pipeline, qa_pipeline = load_pipelines()
+# Initialize question-answering pipeline
+qa_pipeline = pipeline("question-answering", model="distilbert/distilbert-base-cased-distilled-squad")
 
-def get_balanced_rating(prediction):    probs = np.array([score['score'] for score in prediction[0][:5]])
-    probs = probs / (probs.sum() + 1e-8)  # 安全标准化
-    
-    # 优化后的阈值设置（可调整）
-    rating_rules = [
-        (0.95, 1),   # 概率>95%才判为1星
-        (0.85, 2),
-        (0.70, 3), 
-        (0.50, 4),
-        (0.00, 5)     # 默认5星
-    ]
-    
-    for threshold, rating in rating_rules:
-        if probs[rating-1] > threshold:
-            return rating, probs[rating-1]
-    
-    return 5, probs[4]  # 默认返回5星
+# function part
+# text classification
+def textclassification():
+    st.title("Amazon Customer Sentiment Analysis：Ratings and Reasons")
+    st.write("Enter a sentence to analyze its rating and reason:")
 
-def main():
-    st.title("Amazon Review Rating System")
-    
-    user_input = st.text_area("Input Review Text:", height=150)
-    
-    if st.button("Analyze"):
-        with st.spinner("Processing..."):
-            try:
-                # 获取预测
-                prediction = sentiment_pipeline(user_input)
-                
-                # 计算优化后的评分
-                rating, confidence = get_balanced_rating(prediction)
-                
-                # 显示结果
-                st.subheader("Analysis Result")
-                col1, col2 = st.columns(2)
-                with col1:
-                    st.metric("Predicted Rating", f"{rating} stars")
-                with col2:
-                    st.metric("Confidence", f"{confidence:.1%}")
-                
-                # 生成解释
-                qa_result = qa_pipeline({
-                    'question': f'What justifies this {rating}-star rating?',
-                    'context': user_input
-                })
-                st.subheader("Key Factors")
-                st.write(qa_result['answer'])
-                
-                # 调试信息（可选）
-                if st.checkbox("Show debug info"):
-                    st.write("Raw probabilities:", [f"{p:.4f}" for p in prediction[0]])
-                
-            except Exception as e:
-                st.error(f"Error: {str(e)}")
+    user_input = st.text_input("Input your text:")
+    if user_input:
+        # Sentiment Analysis
+        sentiment_result = sentiment_pipeline(user_input)
+        sentiment = sentiment_result[0]["label"]
+        confidence = sentiment_result[0]["score"]
 
-if __name__ == "__main__":
-    main()
+        st.write(f"Sentiment: {sentiment}")
+        st.write(f"Confidence: {confidence:.2f}")
+
+        # Determine the rating based on confidence
+        if sentiment == "POSITIVE":
+            # Scale confidence to a rating of 1 to 5
+            rating = int(confidence * 4) + 1 
+        else:
+            # For negative sentiment, invert the confidence score
+            rating = int((1 - confidence) * 4) + 1  
+
+        # Display the rating
+        st.write(f"The rating is {rating} stars")
+
+        # Question Answering
+        qa_input = {
+            'question': f'Why is the rating {rating} star?',
+            'context': user_input  # Use user input as context
+        }
+        qa_result = qa_pipeline(qa_input)
+        st.write(f"Reasons: {qa_result['answer']}")