Code Editor for app.py

import streamlit as st
import pandas as pd
import altair as alt
from datetime import datetime, timedelta

st.set_page_config(layout="wide")
st.title("Chemical Treatment Dashboard")

# Initializing the data
data = {
    'CHEM_DATE': pd.date_range(start='2023-01-01', periods=100),
    'INJECTION_POINT': ['Point A', 'Point B', 'Point C'] * 34,
    'CHEM_PRODUCT_NAME': ['Product X', 'Product Y', 'Product Z'] * 34,
    'TREATED_FLUID': ['Water', 'Oil', 'Gas'] * 34,
    'TREATED_FLOWRATE': [100 + x for x in range(100)],
    'TARGET_INJECTION_RATE': [10 + x/2 for x in range(100)],
    'TARGET_PPM': [50 + x for x in range(100)],
    'IS_FIELD_TEST': [True, False] * 50,
    'IS_BATCH': [True, False] * 50,
    'BSW_TREATED_FLUID': [x/100 for x in range(100)],
    'TEMP_TREATED_FLUID': [60 + x/2 for x in range(100)],
    'PRESSURE_TREATED_FLUID': [1000 + x*10 for x in range(100)],
    'TDS_TREATED_FLUID': [5000 + x*100 for x in range(100)],
    'PH_TREATED_FLUID': [7 + x/100 for x in range(100)],
    'SUBSTITUTE_PRODUCT': ['Sub A', 'Sub B', 'None'] * 34
}

df = pd.DataFrame(data)

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

# Date filter
date_min = df['CHEM_DATE'].min()
date_max = df['CHEM_DATE'].max()
selected_dates = st.sidebar.date_input(
    "Select Date Range",
    value=(date_min, date_max),
    min_value=date_min,
    max_value=date_max
)

# Product filter
all_products = list(df['CHEM_PRODUCT_NAME'].unique())
selected_products = st.sidebar.multiselect(
    'Select Chemical Products',
    all_products,
    default=all_products
)

# Injection point filter
all_points = list(df['INJECTION_POINT'].unique())
selected_points = st.sidebar.multiselect(
    'Select Injection Points',
    all_points,
    default=all_points
)

# Filter the dataframe
mask = (
    (df['CHEM_DATE'].dt.date >= selected_dates[0]) &
    (df['CHEM_DATE'].dt.date <= selected_dates[1]) &
    (df['CHEM_PRODUCT_NAME'].isin(selected_products)) &
    (df['INJECTION_POINT'].isin(selected_points))
)
filtered_df = df[mask]

# Display key metrics
col1, col2, col3 = st.columns(3)
with col1:
    st.metric("Total Treatments", len(filtered_df))
with col2:
    st.metric("Average Flow Rate", f"{filtered_df['TREATED_FLOWRATE'].mean():.2f}")
with col3:
    st.metric("Average Target PPM", f"{filtered_df['TARGET_PPM'].mean():.2f}")

# Charts
st.subheader("Treatment Analysis")

# Create treatment timeline chart
timeline_chart = alt.Chart(filtered_df).mark_circle().encode(
    x='CHEM_DATE:T',
    y='TARGET_INJECTION_RATE:Q',
    color='CHEM_PRODUCT_NAME:N',
    tooltip=['CHEM_DATE', 'CHEM_PRODUCT_NAME', 'TARGET_INJECTION_RATE', 'INJECTION_POINT']
).properties(
    height=300
).interactive()

st.altair_chart(timeline_chart, use_container_width=True)

# Create distribution by injection point
injection_chart = alt.Chart(filtered_df).mark_bar().encode(
    x='INJECTION_POINT:N',
    y='count():Q',
    color='CHEM_PRODUCT_NAME:N'
).properties(
    height=300
).interactive()

st.altair_chart(injection_chart, use_container_width=True)

# Display filtered data table
st.subheader("Detailed Data")
st.dataframe(filtered_df)

# Download button for filtered data
csv = filtered_df.to_csv(index=False).encode('utf-8')
st.download_button(
    label="Download filtered data as CSV",
    data=csv,
    file_name="chemical_treatment_data.csv",
    mime="text/csv",
)