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277 lines
8.9 KiB
277 lines
8.9 KiB
5 months ago
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# import pandas as pd
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# import pandas_profiling
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# import streamlit as st
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# # from pydantic_settings import BaseSettings
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#
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# from streamlit_pandas_profiling import st_profile_report
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#
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# df = pd.read_csv("https://storage.googleapis.com/tf-datasets/titanic/train.csv")
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# pr = df.profile_report()
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#
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# st_profile_report(pr)
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import os
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import django
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from django.conf import settings
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os.chdir('D:/python/djangoProject/test_Bootstrap')
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# 设置 Django 环境变量
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os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'test_Bootstrap.settings')
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print('开始初始化')
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# 强制初始化 Django
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django.setup()
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print("Django configured.")
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print("Starting Streamlit...")
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import streamlit as st
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from streamlit_vertical_slider import vertical_slider
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from st_pages import Page, Section, show_pages, add_page_title, add_indentation
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add_page_title()
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add_indentation()
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import streamlit as st
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import streamlit as st
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# 定义点击回调函数
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def reset_weights():
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st.session_state.slider_values = [32, 12, 43, 12, 12]
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st.session_state.reset_trigger += 1
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# 初始化 session state 中的键
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if 'slider_values' not in st.session_state:
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st.session_state.slider_values = [32, 12, 43, 12, 12]
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if 'reset_trigger' not in st.session_state:
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st.session_state.reset_trigger = 0
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if 'fund_code' not in st.session_state:
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st.session_state['fund_code'] = ''
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col1, col2 = st.columns([0.8, 0.2])
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with col1:
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# 使用 HTML 和内联CSS来增加字体大小
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st.markdown("""
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<div style='font-size: 34px; font-weight: bold;'>
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数据加权
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</div>
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""", unsafe_allow_html=True)
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with col2:
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st.button("使用默认权重", key="hidden_button", on_click=reset_weights)
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col1, col2 = st.columns([0.8, 0.2])
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with col1:
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st.markdown('######')
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# 创建滑块
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columns = st.columns(5)
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labels = [
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"国家流感中心周报数据",
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"北京疾控中心数据",
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"百度流感指数数据",
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"药品相关股票数据",
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"流感相关基金数据"
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]
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descriptions = [
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"详细数据来自国家流感中心的周报。",
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"来自北京市疾控中心的相关数据。",
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"基于百度搜索指数的流感数据。",
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"涉及流感药品的股票数据。",
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"投资于流感相关领域的基金数据。"
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]
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# 使用循环生成滑块
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for i, col in enumerate(columns):
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with col:
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st.markdown(f"##### {labels[i]}")
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st.markdown(descriptions[i]) # 使用 Markdown 来提供一致的文本框高度
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st.slider(
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label="b", # 使用空标签，因为标题和描述已包含足够信息
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min_value=0,
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max_value=100,
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value=st.session_state.slider_values[i],
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step=1,
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key=f"slider_{i}_{st.session_state.reset_trigger}"
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)
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# 定义点击回调函数
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def reset_model_weights():
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st.session_state.model_slider_values = [2, 12, 43, 12, 12]
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st.session_state.reset_trigger += 1
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# 初始化 session state 中的键
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if 'model_slider_values' not in st.session_state:
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st.session_state.model_slider_values = [2, 12, 43, 12, 12]
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if 'reset_trigger' not in st.session_state:
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st.session_state.reset_trigger = 0
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col1, col2 = st.columns([0.8, 0.2])
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with col1:
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# 使用 HTML 和内联CSS来增加字体大小
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st.markdown("""
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<div style='font-size: 34px; font-weight: bold;'>
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模型加权
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</div>
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""", unsafe_allow_html=True)
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with col2:
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st.button("使用默认权重", key="hidden_button2", on_click=reset_model_weights)
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col1, col2 = st.columns([0.8, 0.2])
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with col1:
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st.markdown('######')
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# 创建滑块
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columns = st.columns(5)
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labels = [
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"ARIMA",
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"随机森林",
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"VAR",
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"SARIMA",
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"LSTM"
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]
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descriptions = [
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"自回归积分滑动平均模型。",
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"适用于高维特征空间的预测问题。",
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"向量自回归模型，适用多元数据。",
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"季节性自回归积分滑动平均模型。",
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"长期记忆网络模型。"
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]
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# 使用循环生成滑块
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for i, col in enumerate(columns):
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with col:
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st.markdown(f"##### {labels[i]}")
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st.markdown(descriptions[i]) # 使用 Markdown 来提供一致的文本框高度
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value = st.slider(
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label="none", # 使用空标签，因为标题和描述已包含足够信息
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min_value=0,
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max_value=100,
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value=st.session_state.model_slider_values[i],
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step=1,
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key=f"model_slider_{i}_{st.session_state.reset_trigger}",
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label_visibility = 'collapsed' # 隐藏标签
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)
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st.session_state.model_slider_values[i] = value
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#从滑块获取模型权重
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model_values_list = st.session_state.model_slider_values
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# st.write('滑块数值：',model_values_list)
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# print(model_values_list)
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#基金预测函数
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# 添加项目根目录到sys.path
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import os
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import sys
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project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), r'D:\python\djangoProject\\test_Bootstrap'))
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sys.path.append(project_root)
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from app_test.add_fund_data import add_fund_data
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from app_test.VAR import VAR_run
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from app_test.RF import RF_run
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from app_test.ARIMA import ARIMA_run
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import pandas as pd
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import json
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from pyecharts import options as opts
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import streamlit.components.v1 as components
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def fund_predect():
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fund_code = st.session_state.fund_code
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print(f'开始预测')
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data = add_fund_data(fund_code)
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VAR_result = VAR_run(data,'fund_data','')
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power_var = model_values_list[2]/(model_values_list[1]+model_values_list[2])
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power_rf = model_values_list[1] / (model_values_list[1] + model_values_list[2])
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VAR_result = VAR_result.to_frame(name='fund_data')
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print(VAR_result,type(VAR_result))
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RF_result = RF_run(data,'fund_data',['liugan_index','infection_number_x', 'infection_number_y', 'jijin_data', 'shoupan'])
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# print(ARIMA_run(data,'fund_data',['liugan_index','infection_number_x', 'infection_number_y', 'jijin_data', 'shoupan']))
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print(RF_result,type(RF_result))
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VAR = [item[0] for item in VAR_result.values.tolist()]
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RF = [item[0] for item in RF_result.values.tolist()]
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pre = [VAR[i]*power_var+RF[i]*power_rf for i in range(len(VAR))]
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# 找到列表中的最小值和最大值
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min_val = min(pre)
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max_val = max(pre)
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# 计算每个值相对于最小值的差异比例
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pre = [(x - min_val) / (max_val - min_val) for x in pre]
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# 将差异比例放大
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pre= [x * 100 for x in pre]
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print("放大差异后的列表：", pre)
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print(pre,type(pre))
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date_column = VAR_result.iloc[:, 0]
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date = date_column.index.tolist()
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date = [str(i)[:10] for i in date]
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print('这是预测结果')
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result = pd.DataFrame({
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'date': date,
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'prediction': pre
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})
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print(result,type(result))
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#可视化预测结果
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date_js = json.dumps(date)
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data_js = json.dumps(pre)
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print('预测结果可视化')
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col = columns = st.columns(1)[0]
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with col:
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st.markdown(f"##### ")
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st.markdown("基金编号：") # 使用 Markdown 来提供一致的文本框高度
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# st.markdown()
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result_visualization(date_js, data_js)
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print('结束')
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# 基金代码输入框和反馈信息
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col1, col2 = st.columns([4, 1])
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with col1:
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# 使用 HTML 和 CSS 来定制标题样式
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st.markdown("""
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<style>
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.big-bold {
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font-size: 34px; /* 设置字体大小 */
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font-weight: bold; /* 字体加粗 */
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}
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</style>
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<p class='big-bold'>基金预测</p>
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""", unsafe_allow_html=True)
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# 创建一个输入框，使用 placeholder 参数设置提示语
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fund_code = st.text_input('基金代码', placeholder='请输入基金代码', key='fund_code', on_change=fund_predect,
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label_visibility='collapsed')
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print(fund_code)
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def result_visualization(date_js, data_js):
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html_content = f"""
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<style>
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#echarts-container {{
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position: fixed;
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bottom: 0;
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left: 50%;
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transform: translateX(-50%);
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width: 700px;
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height: 300px;
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background: #f5f5f5;
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z-index: 100;
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}}
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</style>
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<div id="echarts-container"></div>
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<script src="https://cdn.bootcdn.net/ajax/libs/echarts/5.5.0/echarts.min.js"></script>
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<script>
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var main = echarts.init(document.getElementById("echarts-container"));
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option = {{
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backgroundColor: '#f5f5f5', // 设置背景颜色为浅灰色
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legend: {{
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data: ['基金预测结果']
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}},
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xAxis: {{
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type: 'category',
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data: {date_js}
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}},
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yAxis: {{
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type: 'value'
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}},
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series: [{{
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name: '国家流感中心周报数据',
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data: {data_js},
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type: 'line'
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}}]
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}};
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main.setOption(option);
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</script>
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"""
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# 使用 Streamlit 的 HTML 函数将 HTML 内容嵌入页面中
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components.html(html_content, height=350)
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