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")

# 定义页面功能
st.sidebar.title("App Navigation")
page = st.sidebar.radio("Choose a feature", ["Sentiment Analysis", "Model Evaluation"])

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)

                # 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()

                # 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。")