app.py

import streamlit as st
import pandas as pd
import altair as alt
import json
import os
from datetime import datetime

# -------------------------------
# App Config and Title
# -------------------------------
st.set_page_config(page_title="Climate Clock Observatory", layout="wide")
st.title("🌍 Real-Time Climate Clock Observatory")

# -------------------------------
# Show Last Updated Timestamp
# -------------------------------
try:
    file_stats = os.stat("climate_data.json")
    last_updated = datetime.fromtimestamp(file_stats.st_mtime).strftime("%Y-%m-%d %H:%M:%S")
    st.caption(f"🕒 Data last updated: {last_updated}")
except:
    st.caption("🕒 Last updated time not available")

# -------------------------------
# Refresh Button
# -------------------------------
if st.button("🔁 Refresh Data"):
    with st.spinner("Refreshing data..."):
        st.experimental_rerun()

# -------------------------------
# Load Climate Data from JSON
# -------------------------------
try:
    with open("climate_data.json", "r") as f:
        clock = json.load(f)
except Exception as e:
    st.error("❌ Failed to load climate data.")
    st.stop()

# -------------------------------
# 1️⃣ CO₂ Budget Depletion Projection
# -------------------------------
st.header("1️⃣ CO₂ Budget Depletion Projection")

co2_data = clock.get("co2", {})
co2_remaining = float(co2_data.get("remaining", 0))
co2_rate = float(co2_data.get("rate", 0))

years = list(range(datetime.now().year, datetime.now().year + 10))
remaining_budget = [max(co2_remaining - i * co2_rate, 0) for i in range(10)]
co2_df = pd.DataFrame({"Year": years, "Remaining CO₂ Budget (Gt)": remaining_budget})

co2_chart = alt.Chart(co2_df).mark_line(point=True).encode(
    x='Year:O',
    y='Remaining CO₂ Budget (Gt):Q',
    tooltip=['Year', 'Remaining CO₂ Budget (Gt)']
).properties(width=700)

st.altair_chart(co2_chart, use_container_width=True)

# -------------------------------
# 2️⃣ Global Energy Mix Donut Chart
# -------------------------------
st.header("2️⃣ Global Energy Mix – Renewables vs Others")

renew_data = clock.get("renewables", {})
renew_percent = float(renew_data.get("percentage", 0))
energy_df = pd.DataFrame({
    "Type": ["Renewables", "Other"],
    "Percentage": [renew_percent, 100 - renew_percent]
})

energy_chart = alt.Chart(energy_df).mark_arc(innerRadius=50).encode(
    theta="Percentage:Q",
    color="Type:N",
    tooltip=["Type", "Percentage"]
).properties(width=400, height=400)

st.altair_chart(energy_chart, use_container_width=True)

# -------------------------------
# 3️⃣ Lifeline Metrics Bar Chart
# -------------------------------
st.header("3️⃣ Lifeline Metrics – Positive Contributions")

lifelines = clock.get("lifelines", [])
lifeline_df = pd.DataFrame([
    {"Label": item.get("label", ""), "Value": float(item.get("value", 0))}
    for item in lifelines
])

lifeline_chart = alt.Chart(lifeline_df).mark_bar().encode(
    x=alt.X("Label:N", sort="-y"),
    y="Value:Q",
    tooltip=["Label", "Value"]
).properties(width=700)

st.altair_chart(lifeline_chart, use_container_width=True)

# -------------------------------
# 4️⃣ Time Left to 1.5°C Threshold
# -------------------------------
st.header("4️⃣ Time Left to 1.5°C Threshold")

deadline = clock.get("deadline", {})
time_parts = deadline.get("time_left", "0:0:0:0:0").split(":")
try:
    years, months, days = int(time_parts[0]), int(time_parts[1]), int(time_parts[2])
    st.success(f"⏳ Estimated time before 1.5°C threshold: {years} years, {months} months, {days} days")
except:
    st.warning("⚠️ Could not parse climate deadline.")

# -------------------------------
# 5️⃣ CO₂ Budget Simulator with Slider
# -------------------------------
st.header("5️⃣ CO₂ Budget Simulator – What If We Reduce Emissions?")

st.markdown("📉 Use the slider below to simulate reduced annual emissions and visualize its impact.")

new_rate = st.slider(
    "New Annual CO₂ Emission Rate (Gt/year)",
    min_value=10.0,
    max_value=45.0,
    value=co2_rate,
    step=0.5
)

sim_years = []
sim_budget = []
current_budget = co2_remaining
year = datetime.now().year

while current_budget > 0 and len(sim_years) < 20:
    sim_years.append(year)
    sim_budget.append(current_budget)
    current_budget -= new_rate
    year += 1

sim_df = pd.DataFrame({
    "Year": sim_years,
    "Projected CO₂ Budget (Gt)": sim_budget
})

sim_chart = alt.Chart(sim_df).mark_line(point=True).encode(
    x="Year:O",
    y="Projected CO₂ Budget (Gt):Q",
    tooltip=["Year", "Projected CO₂ Budget (Gt)"]
).properties(width=700)

st.altair_chart(sim_chart, use_container_width=True)

# -------------------------------
# Footer
# -------------------------------
st.markdown("---")
st.caption("Made by Sanidhya and Group Members | Data from Climate Clock API (cached sample)")