my_pages/page1.py

from typing import Dict
import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import hypernetx as hnx
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from io import BytesIO
import time
from utils.data_processor import load_data, process_data, build_hyperedges
from utils.visualizer import visualize_gmm, visualize_ratings

from utils.streamlit_hypergraph import hypergraph_visualization_component


def main():
    st.title("NeurIPS 2024 Bench Paper 高斯混合聚类分析")

    # 自动播放
    slider_max = 10
    if 'play_state' not in st.session_state:
        st.session_state.play_state = False
    if 'iteration' not in st.session_state:
        st.session_state.iteration = 0
    # 定义回调函数来切换播放状态
    def toggle_play():
        if not st.session_state.play_state and st.session_state.iteration == slider_max:
            st.session_state.iteration = 0 # 重置迭代次数
        st.session_state.play_state = not st.session_state.play_state

    # 创建播放/暂停按钮
    if st.session_state.play_state:
        button_label = "暂停"
    else:
        button_label = "开始拟合"
    st.button(button_label, on_click=toggle_play, key="play_button")
    # 播放速度
    # speed = st.slider("播放速度", min_value=0.1, max_value=2.0, value=1.0, step=0.1, key="speed_slider")
    # 主页面布局

    # 显示迭代次数滑条
    iteration = st.slider("迭代步骤", min_value=1, max_value=slider_max, 
                            value=st.session_state.iteration, step=1, 
                            key="iteration_slider")
    # st.write(f"当前迭代次数: {iteration}")
    # print(st.session_state.iteration)


    # 动态限制采样数量的最大值
    df = load_data()

    
    # 使用 sidebar 控制参数
    with st.sidebar:
        st.header("控制面板")
        speed = st.slider("拟合速度", min_value=0.1, max_value=2.0, value=1.0, step=0.1, key="speed_slider")
        draw_width = st.slider("绘图宽度", min_value=3, max_value=20, value=6, step=1, key="draw_width")
        draw_height = st.slider("绘图高度",min_value=3, max_value=20, value=6, step=1, key="draw_height")

        max_samples = len(df)
        num_samples = st.slider("选择采样论文数量", min_value=1, 
                                max_value=min(100, max_samples), value=min(10, max_samples), step=1)
                                
        
        # 添加复选框选择显示 paper 的属性
        display_attribute = st.selectbox(
            "选择显示 paper 的属性",
            ["order", "index", "id", "title", "keywords", "author"]
        )
        # 选择是 top k 还是 top p
        display_option = st.selectbox(
            "选择显示的选项",
            ["Top K Clusters", "Clusters Up To Probability P"]
        )
        # Top K Clusters
        if display_option == "Top K Clusters":
            max_k = 5
            top_k = st.slider("选择 K 值", min_value=1, max_value=max_k, 
                              value=1, step=1)
            top_p = None
        else:
            top_k = None
            top_p = st.slider("选择 P 值", min_value=0.0, max_value=1.0, value=0.5, step=0.01)

        # 处理数据
        sampled_df, probabilities, paper_attributes = process_data(df, iteration, num_samples)
        # print(display_attribute) # 字符串
        hyperedges = build_hyperedges(probabilities, paper_attributes, display_attribute, top_k=top_k, top_p=top_p)
        hypergraph = hnx.Hypergraph(hyperedges)

        # print(hyperedges)

    show_hypergraph = st.checkbox("显示超图", value=True, key="show_hyperedges")
    show_gaussian = st.checkbox("显示高斯分布", value=False, key="show_gaussian")

    if show_hypergraph:
        hypergraph_visualization_component(hypergraph, draw_width, draw_height)
    
    if show_gaussian:
        st.header("高斯混合分布聚类结果")
        fig_gmm = visualize_gmm(sampled_df, iteration)
        st.plotly_chart(fig_gmm, use_container_width=True)

    # 显示采样论文的详细信息
    st.header("采样论文详细信息")
    st.dataframe(sampled_df[["index", "title", "keywords", "rating_avg", "confidence_avg", "site"]
                            ]
                            # .style.highlight_max(axis=0)
                            )

    # 增加第二种可视化方式
    # st.header("论文评分分布")
    # fig_bar = visualize_ratings(sampled_df)
    # st.plotly_chart(fig_bar, use_container_width=True)


    
    # 自动播放功能
    # print(st.session_state.play_state)
    if st.session_state.play_state:
        # 使用空容器来显示进度
        progress_container = st.empty()
        
        with st.spinner("正在播放..."):
            if st.session_state.iteration < slider_max:
                # 增加滑动条值
                st.session_state.iteration += 1
                st.write(f"当前迭代次数: {st.session_state.iteration}")
                # print(st.session_state.iteration)
                # 等待一小段时间模拟滑动过程
                time.sleep(1/speed)  # 根据速度调整等待时间
                # 使用rerun来更新页面
                st.rerun()
            else:
                # 到达最大值时停止播放
                st.session_state.play_state = False


    
# if __name__ == "__main__":
# # 设置页面布局
#     st.set_page_config(layout="wide")
#     # 运行主函数
main()