Update app.py

app.py

@@ -1,25 +1,65 @@
-# Use a pipeline as a high-level helper
+import streamlit as st
 from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import matplotlib.pyplot as plt
+from sklearn.metrics import precision_recall_curve, auc
 
+# 初始化情感分析模型
 pipe = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
-# Load model directly
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
-model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
-import streamlit as st
-from transformers import pipeline
+# 定义页面功能
+st.sidebar.title("App Navigation")
+page = st.sidebar.radio("Choose a feature", ["Sentiment Analysis", "Model Evaluation"])
 
-# 加载情感分析模型
-pipe = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
+if page == "Sentiment Analysis":
+    # 设置页面标题
+    st.title("Twitter Sentiment Analysis App")
+
+    # 输入框让用户输入文本
+    user_input = st.text_input("Enter a tweet to analyze:")
+
+    if user_input:
+        # 调用模型分析用户输入
+        result = pipe(user_input)
+        st.write("Sentiment Analysis Result:", result)
+
+elif page == "Model Evaluation":
+    # 设置评估页面标题
+    st.title("Model Precision-Recall Evaluation")
+
+    # 上传真实标签和预测概率
+    st.write("### 输入数据")
+    y_true_input = st.text_area("输入真实标签 (用逗号分隔)", "1,0,1,1,0,1")
+    y_score_input = st.text_area("输入预测概率 (用逗号分隔)", "0.95,0.1,0.85,0.75,0.2,0.9")
+
+    if y_true_input and y_score_input:
+        try:
+            # 解析输入
+            y_true = list(map(int, y_true_input.split(",")))
+            y_score = list(map(float, y_score_input.split(",")))
+
+            # 检查长度是否一致
+            if len(y_true) != len(y_score):
+                st.error("真实标签和预测概率的长度不一致！请重新输入。")
+            else:
+                # 计算 Precision 和 Recall
+                precision, recall, _ = precision_recall_curve(y_true, y_score)
+                pr_auc = auc(recall, precision)
 
-# 设置页面标题
-st.title("Twitter Sentiment Analysis App")
+                # 绘制 PR 曲线
+                fig, ax = plt.subplots()
+                ax.plot(recall, precision, label=f"PR Curve (AUC = {pr_auc:.2f})")
+                ax.set_xlabel("Recall")
+                ax.set_ylabel("Precision")
+                ax.set_title("Precision-Recall Curve")
+                ax.legend(loc="best")
+                ax.grid()
 
-# 输入框让用户输入文本
-user_input = st.text_input("Enter a tweet to analyze:")
+                # 显示图像
+                st.pyplot(fig)
+                st.success(f"PR Curve AUC: {pr_auc:.2f}")
+        except Exception as e:
+            st.error(f"发生错误: {e}")
+    else:
+        st.info("请输入真实标签和预测概率以生成 PR 曲线。")
 
-if user_input:
-    # 调用模型分析用户输入
-    result = pipe(user_input)
-    st.write("Sentiment Analysis Result:", result)