Code Editor for app.py

import streamlit as st
import pandas as pd
import requests
import matplotlib.pyplot as plt

@st.cache_data
def fetch_energy_data():
    url = "https://api.eia.gov/v2/seds/data/"
    params = {
        "frequency": "annual",
        "data[0]": "value",
        "facets[seriesId][]": ["BMTCB", "CLTCB", "PATCB", "PPTCB", "RETCB"], 
        "facets[stateId][]": ["US"], 
        "sort[0][column]": "period",
        "sort[0][direction]": "desc",
        "offset": 0,
        "length": 5000,
        "api_key": "XQ6sIdWdcbX3opHOGhFf6mDCyccPkmQ8MZrDwm52"
    }
    response = requests.get(url, params=params)
    if response.status_code == 200:
        return pd.DataFrame(response.json()["response"]["data"])
    else:
        st.error(f"Failed to fetch data. Status code: {response.status_code}")
        return pd.DataFrame()

@st.cache_data
def process_energy_data(data):
    if data.empty:
        return data

    data["period"] = data["period"].astype(int)
    data["value"] = pd.to_numeric(data["value"], errors="coerce")

    category_map = {
        "Biomass total consumption": "Biomass",
        "Coal total consumption": "Coal",
        "All petroleum products total consumption": "Petroleum",
        "Natural gasoline (pentanes plus) total consumption": "Natural Gas",
        "Renewable energy total consumption": "Renewable Energy"
    }
    data["category"] = data["seriesDescription"].map(category_map)

    category_order = ["Biomass", "Coal", "Petroleum", "Natural Gas", "Renewable Energy"]
    data["category"] = pd.Categorical(data["category"], categories=category_order, ordered=True)

    return data

def main():
    st.title("U.S. Annual Energy Consumption by Category")
    st.write("Explore biomass, coal, petroleum, natural gas, and renewable energy consumption by year.")

    raw_data = fetch_energy_data()
    if raw_data.empty:
        st.write("No data available.")
        return

    processed_data = process_energy_data(raw_data)

    st.sidebar.header("Filter Options")
    energy_type = st.sidebar.selectbox(
        "Select Energy Type:",
        ["All"] + list(processed_data["category"].cat.categories)
    )

    if energy_type == "All":
        year = st.sidebar.selectbox(
            "Select a Year:",
            sorted(processed_data["period"].unique(), reverse=True)
        )
        filtered_data = processed_data[processed_data["period"] == year]

        if not filtered_data.empty:
            st.subheader(f"All Energy Consumption Categories for {year}")
            fig, ax = plt.subplots(figsize=(10, 6))
            filtered_data = filtered_data.sort_values(by="category")
            filtered_data.set_index("category")["value"].plot(kind="bar", ax=ax)
            ax.set_title(f"All Energy Consumption Categories in {year}")
            ax.set_ylabel("Energy Consumption (Billion Btu)")
            ax.set_xlabel("Category")
            plt.xticks(rotation=45, ha="right")
            st.pyplot(fig)
        else:
            st.write(f"No data available for {year}.")
    else:
        min_year, max_year = st.sidebar.select_slider(
            "Select Year Range:",
            options=sorted(processed_data["period"].unique()),
            value=(processed_data["period"].min(), processed_data["period"].max())
        )
        filtered_data = processed_data[
            (processed_data["category"] == energy_type) &
            (processed_data["period"] >= min_year) & (processed_data["period"] <= max_year)
        ]

        if not filtered_data.empty:
            st.subheader(f"{energy_type} Consumption from {min_year} to {max_year}")
            fig, ax = plt.subplots(figsize=(10, 6))
            filtered_data.plot(x="period", y="value", kind="line", marker="o", ax=ax)
            ax.set_title(f"{energy_type} Consumption from {min_year} to {max_year}")
            ax.set_ylabel("Energy Consumption (Billion Btu)")
            ax.set_xlabel("Year")
            plt.xticks(rotation=45, ha="right")
            st.pyplot(fig)
        else:
            st.write(f"No data available for {energy_type} from {min_year} to {max_year}.")

    st.markdown("***")
    st.markdown("**Data Source**: U.S. Energy Information Administration (EIA).")

if __name__ == "__main__":
    main()