| import streamlit as st
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| from datetime import datetime
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| import pandas as pd
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| import streamlit as st
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| from streamlit_elements import elements, mui
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| import plotly.express as px
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| import numpy as np
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| import os
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| st.set_page_config(layout='wide')
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| st.markdown("""
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| <style>
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| .st-emotion-cache-rci5h9 {
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| display: inline-flex
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| ;
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| -webkit-box-align: center;
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| border: none !important;
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| background: transparent;
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| align-items: center;
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| -webkit-box-pack: center;
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| justify-content: center;
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| font-weight: 400;
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| padding: 0.25rem 0.75rem;
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| border-radius: 0.5rem;
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| min-height: 2.5rem;
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| margin: 0px;
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| line-height: 1.6;
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| color: inherit;
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| width: auto;
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| cursor: pointer;
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| user-select: none;
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| /* background-color: rgb(43, 44, 54); */
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| border: 1px solid rgba(250, 250, 250, 0.2);}
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|
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| .st-emotion-cache-13na8ym {
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| margin-bottom: 0px;
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| margin-top: 0px;
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| width: 100%;
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| border-style: solid;
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| border-width: 1px;
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| /* border-color: rgba(250, 250, 250, 0.2); */
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| /* border-radius: 0.5rem; */
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| border: none;
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| }
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| summary.st-emotion-cache-17sglzk.eqpbllx1:hover {
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| background-color:gray;
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| border-radius:5px;
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| font-size: 200px;
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| }
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| .streamlit-expanderHeader {
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| font-size: 50px; /* Change this to your desired font size */
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| font-weight: bold; /* Optional: Make the text bold */
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| color: #4CAF50; /* Optional: Customize the text color */
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| }
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| .st-emotion-cache-ysk9xe p {
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| word-break: break-word;
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| margin-bottom: 0px;
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| font-size: 18px;
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| }
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| .st-emotion-cache-16oe73j {
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| margin-bottom: 0px;
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| margin-top: 0px;
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| width: 100%;
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| border: none !important;
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| }
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|
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| .st-emotion-cache-ior94r {
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| display: inline-flex
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| ;
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| -webkit-box-align: center;
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| align-items: center;
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| -webkit-box-pack: center;
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| justify-content: center;
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| font-weight: 400;
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| padding: 0.25rem 0.75rem;
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| border-radius: 0.5rem;
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| min-height: 2.5rem;
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| margin: 0px;
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| line-height: 1.6;
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| color: inherit;
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| width: auto;
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| cursor: pointer;
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| user-select: none;
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| border: none !important;
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| background: transparent;
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| }
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| button.st-emotion-cache-18hpnz2.ef3psqc19 {
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| border: none;
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| background: transparent;
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| }
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| button.st-emotion-cache-18hpnz2.ef3psqc19 {
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| display: inline-flex;
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| align-items: center;
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| justify-content: center;
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| font-weight: 400;
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| padding: 0.25rem 0.75rem;
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| border-radius: 0.5rem;
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| min-height: 2.5rem;
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| margin: 0px;
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| line-height: 1.6;
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| color: inherit;
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| width: auto;
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| cursor: pointer;
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| user-select: none;
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| border: none !important;
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| background: transparent; /* Initial background */
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| transition: background 0.3s ease; /* Smooth transition for background */
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| }
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| /* Hover effect: Change background and add slight scale */
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| button.st-emotion-cache-18hpnz2.ef3psqc19:hover {
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| background: rgba(54, 117, 175, 0.3); /* Change background on hover */
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| transform: scale(1.05); /* Slight scale effect on hover */
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| }
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| .st-emotion-cache-1dn3z2w {
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| position: fixed;
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| top: 0px;
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| left: 0px;
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| right: 0px;
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| height: 5rem;
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| background: rgb(0 0 0);
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| outline: none;
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| z-index: 999990;
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| display: block;
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| }
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| </style>
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| """, unsafe_allow_html=True)
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| st.logo(r'assets\logo.svg', icon_image='assets\logo-sm.svg',size='large')
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| if "current_page" not in st.session_state:
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| st.session_state.current_page = "home"
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| def home():
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| st.header('this is header')
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| def price_and_fundamental():
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| st.header('Price and Fundamentals')
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| if "data" not in st.session_state:
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| np.random.seed(42)
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| dates = pd.date_range(start='2024-01-01', end='2024-08-31', freq='D')
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| jobs = ['Job_A', 'Job_B', 'Job_C', 'Job_D']
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| scenarios = ['Scenario_A', 'Scenario_B', 'Scenario_C']
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| areas = ['TKO', 'SYS', 'HKD', 'CHB', 'HKR', 'KNS', 'CGK']
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| data = pd.DataFrame({
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| 'Date': np.random.choice(dates, 1000),
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| 'Job': np.random.choice(jobs, 1000),
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| 'Scenario': np.random.choice(scenarios, 1000),
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| 'Area': np.random.choice(areas, 1000),
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| 'Value': np.random.randint(100, 1000, 1000)
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| })
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| st.session_state.data = data
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| data = st.session_state.data
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| data['Date'] = pd.to_datetime(data['Date']).dt.date
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| col1, col2 = st.columns(2)
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| st.header('Select Data')
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| st.write('Use the widget below to explore the generation model')
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| with col1:
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| col1_1, col1_2, col1_3 = st.columns(3)
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| with col1_1:
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| selected_job = st.selectbox(
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| 'Select a job',
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| sorted(data['Job'].unique())
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| )
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| compare_jobs = st.checkbox('Compare two jobs', help='Compare jobs here')
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| with col1_2:
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| selected_scenario = st.selectbox(
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| 'Select Scenario',
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| sorted(data['Scenario'].unique())
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| )
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| select_all_areas = st.checkbox('Select all areas', help='Select all areas here')
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| st.button('Load Data')
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| with col1_3:
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| selected_granularity = st.selectbox(
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| 'Select granularity',
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| ['Daily', 'Weekly', 'Monthly', 'Quarterly', 'Yearly']
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| )
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| show_inputs_only = st.checkbox(
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| 'Show inputs only',
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| help="Check to show only the inputs for the run. Useful if the run hasn't been solved and inserted yet.",
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| key="Show_inputs_only"
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| )
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| if st.button('Refresh job list'):
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| st.experimental_rerun()
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| col1_col1, col1_col2, col1_col3 = st.columns(3)
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| with col1_col1:
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| pass
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| with col1_col2:
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| pass
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| with col1_col3:
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| pass
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| with col2:
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| if select_all_areas:
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| selected_areas = data['Area'].unique().tolist()
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| else:
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| selected_areas = st.multiselect(
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| 'Select areas',
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| sorted(data['Area'].unique()),
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| default=['TKO']
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| )
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| selected_date_range = st.slider(
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| 'Select date range',
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| min_value=data['Date'].min(),
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| max_value=data['Date'].max(),
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| value=(data['Date'].min(), data['Date'].max()),
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| format="YYYY-MM-DD", key='this'
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| )
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| filtered_data = data[
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| (data['Job'] == selected_job) &
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| (data['Scenario'] == selected_scenario) &
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| (data['Area'].isin(selected_areas)) &
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| (data['Date'] >= selected_date_range[0]) &
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| (data['Date'] <= selected_date_range[1])
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| ]
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| if not filtered_data.empty:
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| if selected_granularity != 'Daily':
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| filtered_data['Date'] = pd.to_datetime(filtered_data['Date'])
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| if selected_granularity == 'Weekly':
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| filtered_data['Date'] = filtered_data['Date'].dt.to_period('W').apply(lambda r: r.start_time)
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| elif selected_granularity == 'Monthly':
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| filtered_data['Date'] = filtered_data['Date'].dt.to_period('M').apply(lambda r: r.start_time)
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| elif selected_granularity == 'Quarterly':
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| filtered_data['Date'] = filtered_data['Date'].dt.to_period('Q').apply(lambda r: r.start_time)
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| elif selected_granularity == 'Yearly':
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| filtered_data['Date'] = filtered_data['Date'].dt.to_period('Y').apply(lambda r: r.start_time)
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| filtered_data = filtered_data.groupby(['Date', 'Area'], as_index=False)['Value'].mean()
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| st.subheader('Interactive Plot')
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| if not filtered_data.empty:
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| fig = px.line(
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| filtered_data,
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| x='Date',
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| y='Value',
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| color='Area',
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| title=f'Filtered Data Over Time ({selected_granularity})'
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| )
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| fig.update_layout(
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| plot_bgcolor='rgb(30,30,30)',
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| paper_bgcolor='rgb(20,20,20)',
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| font=dict(
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| color='white',
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| size=14,
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| ),
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| title=dict(
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| text=f'Filtered Data Over Time ({selected_granularity})',
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| font=dict(
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| size=20,
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| color='white'
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| )
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| ),
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| xaxis=dict(
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| showgrid=False,
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| color='white',
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| title=dict(
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| text='Date',
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| font=dict(color='white')
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| )
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| ),
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| yaxis=dict(
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| showgrid=True,
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| gridcolor='gray',
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| color='white',
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| title=dict(
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| text='Value',
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| font=dict(color='white')
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| )
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| ),
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| legend=dict(
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| title=dict(text='Area', font=dict(color='white')),
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| font=dict(color='white')
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| )
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| )
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| st.plotly_chart(fig, use_container_width=True)
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| st.subheader('Original Data')
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| st.dataframe(data, use_container_width=True)
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| st.header('Filtered dataframe')
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| if not filtered_data.empty:
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| st.dataframe(filtered_data, use_container_width=True)
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| else:
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| st.write("No data available for the selected filters.")
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| def generation():
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| st.title("🏭 Generation")
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| st.write("Details about generation.")
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| def load_factors():
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| st.title("🌩️ Load Factors")
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| st.write("Details about load factors.")
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| def calendar_creation():
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| st.title("📆 Calendar Creation")
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| st.write("Create and manage calendars.")
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| def calibration():
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| st.title("🔌 Calibration")
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| st.write("Calibrate your models.")
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| def weather_paths():
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| st.title("🌦️ Weather Paths")
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| st.write("Weather-related data.")
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| def app_page():
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| st.title("📊 PyGWalker Integration")
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| st.write("Interactive visualizations with PyGWalker.")
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| with st.sidebar:
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| if st.button("🏚️ Home"):
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| st.session_state.current_page = "home"
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| with st.expander(" Generation Model"):
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| if st.button("🗾Price & Fun"):
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| st.session_state.current_page = "price_and_fundamental"
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| if st.button("🏭 Generation"):
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| st.session_state.current_page = "generation"
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| if st.button("🌩️ Load Factors"):
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| st.session_state.current_page = "load_factors"
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| with st.expander("Load Scenarios"):
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| if st.button("📆 Calendar Creation"):
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| st.session_state.current_page = "calendar_creation"
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| if st.button("🔌 Calibration"):
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| st.session_state.current_page = "calibration"
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| if st.button("🌦️ Weather Paths"):
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| st.session_state.current_page = "weather_paths"
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| with st.expander("Commodities Scenarios"):
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| if st.button("📊 PyGWalker", key='rajratan'):
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| st.session_state.current_page = "app_page"
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| if st.session_state.current_page == "home":
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| home()
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| elif st.session_state.current_page == "price_and_fundamental":
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| price_and_fundamental()
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| elif st.session_state.current_page == "generation":
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| generation()
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| elif st.session_state.current_page == "load_factors":
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| load_factors()
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| elif st.session_state.current_page == "calendar_creation":
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| calendar_creation()
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| elif st.session_state.current_page == "calibration":
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| calibration()
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| elif st.session_state.current_page == "weather_paths":
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| weather_paths()
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| elif st.session_state.current_page == "app_page":
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| app_page()
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|
