Code Editor for app.py

import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import numpy as np
from datetime import datetime, timedelta

# Set page config
st.set_page_config(
    page_title="Chemical Production Dashboard",
    page_icon="🏭",
    layout="wide"
)

# Generate sample data
@st.cache_data
def generate_sample_data():
    dates = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D')
    chemicals = ['Corrosion Inhibitor', 'Scale Inhibitor', 'Biocide', 'Demulsifier']
    
    data = []
    for chemical in chemicals:
        base_consumption = np.random.uniform(100, 500)
        for date in dates:
            # Add some randomness and seasonal variation
            seasonal_factor = 1 + 0.2 * np.sin(2 * np.pi * date.dayofyear / 365)
            daily_consumption = base_consumption * seasonal_factor * np.random.uniform(0.8, 1.2)
            
            data.append({
                'Date': date,
                'Chemical': chemical,
                'Consumption (L)': daily_consumption,
                'Stock Level (L)': base_consumption * 30 * np.random.uniform(0.5, 0.9),
                'Cost ($)': daily_consumption * np.random.uniform(2, 5)
            })
    
    return pd.DataFrame(data)

# Main layout
st.title("⚗️ Chemical Production Dashboard")

# Load data
df = generate_sample_data()

# Sidebar filters
st.sidebar.header("Filters")

# Date range selector
date_range = st.sidebar.date_input(
    "Select Date Range",
    value=(df['Date'].min(), df['Date'].max()),
    min_value=df['Date'].min(),
    max_value=df['Date'].max()
)

# Chemical selector
selected_chemicals = st.sidebar.multiselect(
    "Select Chemicals",
    options=df['Chemical'].unique(),
    default=df['Chemical'].unique()
)

# Filter data based on selections
mask = (df['Date'].dt.date >= date_range[0]) & (df['Date'].dt.date <= date_range[1])
filtered_df = df[mask & df['Chemical'].isin(selected_chemicals)]

# Create three columns for KPIs
col1, col2, col3 = st.columns(3)

with col1:
    total_consumption = filtered_df['Consumption (L)'].sum()
    st.metric("Total Consumption", f"{total_consumption:,.0f} L")

with col2:
    total_cost = filtered_df['Cost ($)'].sum()
    st.metric("Total Cost", f"${total_cost:,.2f}")

with col3:
    avg_daily_consumption = filtered_df.groupby('Date')['Consumption (L)'].sum().mean()
    st.metric("Avg Daily Consumption", f"{avg_daily_consumption:,.0f} L")

# Create two columns for charts
col1, col2 = st.columns(2)

with col1:
    st.subheader("Daily Consumption by Chemical")
    fig_consumption = px.line(
        filtered_df,
        x='Date',
        y='Consumption (L)',
        color='Chemical',
        title='Daily Chemical Consumption'
    )
    st.plotly_chart(fig_consumption, use_container_width=True)

with col2:
    st.subheader("Current Stock Levels")
    current_stock = filtered_df.groupby('Chemical')['Stock Level (L)'].last().reset_index()
    fig_stock = px.bar(
        current_stock,
        x='Chemical',
        y='Stock Level (L)',
        title='Current Stock Levels'
    )
    st.plotly_chart(fig_stock, use_container_width=True)

# Cost analysis
st.subheader("Cost Analysis")
cost_by_chemical = filtered_df.groupby('Chemical')['Cost ($)'].sum().reset_index()
fig_cost = px.pie(
    cost_by_chemical,
    values='Cost ($)',
    names='Chemical',
    title='Cost Distribution by Chemical'
)
st.plotly_chart(fig_cost, use_container_width=True)

# Detailed data table
st.subheader("Detailed Data")
st.dataframe(
    filtered_df.sort_values('Date', ascending=False)
    .round(2)
    .style.format({
        'Consumption (L)': '{:,.0f}',
        'Stock Level (L)': '{:,.0f}',
        'Cost ($)': '${:,.2f}'
    })
)