Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

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

# Generate sample data
def generate_sample_data():
    dates = pd.date_range(end=datetime.now(), periods=100, freq='D')
    chemicals = ['Ethanol', 'Methanol', 'Acetone', 'Benzene']
    
    data = {
        'Date': [],
        'Chemical': [],
        'Production_Volume': [],
        'Quality_Score': [],
        'Batch_Number': [],
        'Temperature': []
    }
    
    batch = 1000
    for date in dates:
        for chemical in chemicals:
            data['Date'].append(date)
            data['Chemical'].append(chemical)
            data['Production_Volume'].append(np.random.normal(1000, 100))
            data['Quality_Score'].append(np.random.uniform(90, 100))
            data['Batch_Number'].append(batch)
            data['Temperature'].append(np.random.normal(25, 5))
            batch += 1
            
    return pd.DataFrame(data)

# Create sample data
df = generate_sample_data()

# Dashboard title
st.title("Chemical Production Dashboard")

# Date filter
st.sidebar.header("Filters")
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_chemical = st.sidebar.multiselect(
    "Select Chemicals",
    options=df['Chemical'].unique(),
    default=df['Chemical'].unique()[0]
)

# 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_chemical)]

# Layout with columns
col1, col2 = st.columns(2)

with col1:
    st.subheader("Production Volume Over Time")
    production_data = filtered_df.pivot(index='Date', columns='Chemical', values='Production_Volume')
    st.line_chart(production_data)

with col2:
    st.subheader("Quality Score Trends")
    quality_data = filtered_df.pivot(index='Date', columns='Chemical', values='Quality_Score')
    st.line_chart(quality_data)

# Second row
col3, col4 = st.columns(2)

with col3:
    st.subheader("Temperature Distribution")
    temp_data = filtered_df.pivot(index='Date', columns='Chemical', values='Temperature')
    st.area_chart(temp_data)

with col4:
    st.subheader("Recent Production Statistics")
    recent_stats = filtered_df.groupby('Chemical').agg({
        'Production_Volume': ['mean', 'min', 'max'],
        'Quality_Score': 'mean',
        'Temperature': 'mean'
    }).round(2)
    recent_stats.columns = ['Avg Volume', 'Min Volume', 'Max Volume', 'Avg Quality', 'Avg Temp']
    st.dataframe(recent_stats)

# Key metrics in a row
st.subheader("Key Metrics")
metric1, metric2, metric3, metric4 = st.columns(4)

with metric1:
    total_production = filtered_df['Production_Volume'].sum()
    st.metric("Total Production", f"{total_production:,.0f} L")

with metric2:
    avg_quality = filtered_df['Quality_Score'].mean()
    st.metric("Average Quality Score", f"{avg_quality:.1f}%")

with metric3:
    total_batches = filtered_df['Batch_Number'].nunique()
    st.metric("Total Batches", total_batches)

with metric4:
    avg_temp = filtered_df['Temperature'].mean()
    st.metric("Average Temperature", f"{avg_temp:.1f}°C")

# Show raw data if requested
if st.checkbox("Show Raw Data"):
    st.subheader("Raw Data")
    st.dataframe(filtered_df)