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README.md

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+---
+title: "Climate Clock Observatory"
+emoji: 🌍
+colorFrom: green
+colorTo: blue
+sdk: streamlit
+sdk_version: "1.32.0"
+app_file: app.py
+pinned: false
+---
+
+# 🌍 Climate Clock Observatory
+
+This Streamlit app visualizes real-time climate indicators using the [Climate Clock API](https://docs.climateclock.world/climate-clock-docs/climate-clock-api). It is built as part of a final project to demonstrate how data can be transformed into an engaging and informative climate observatory.
+
+## 📊 Visualizations Included
+
+1. **CO₂ Budget Depletion Projection** – 10-year forecast using current depletion rates.
+2. **Global Energy Mix** – A donut chart showing the percentage of global energy from renewables.
+3. **Lifeline Metrics** – A bar chart showing positive contributions like trees planted and energy created.
+4. **Time Until 1.5°C Breach** – Countdown clock showing years, months, and days remaining.
+5. **CO₂ Budget Simulator** – Interactive slider that simulates reduced emissions and updates the budget timeline.
+
+## 🚀 How to Run
+
+```bash
+pip install -r requirements.txt
+streamlit run app.py
+```
+
+## 🛠️ Tech Stack
+
+- Streamlit
+- Altair
+- Pandas
+- Climate Clock API
+
+## 👥 Contributors
+
+Sanidhya and Group Members
+
+## 📝 Notes
+
+- All plots are created using Altair as per project guidelines.
+- The app uses a refresh-button-friendly design and pseudo real-time data.
+
+Enjoy exploring our climate observatory!
+

app.py

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+import streamlit as st
+import requests
+import pandas as pd
+import altair as alt
+from datetime import datetime, timedelta
+
+st.set_page_config(page_title="Climate Clock Observatory", layout="wide")
+st.title("🌍 Real-Time Climate Clock Observatory")
+
+# -- Helper: Fetch and parse Climate Clock API data
+@st.cache_data(ttl=300)
+def fetch_climate_data():
+    url = "https://api.climateclock.world/v2/clock"
+    response = requests.get(url)
+    if response.status_code == 200:
+        return response.json().get("data", {}).get("clocks", {})
+    return {}
+
+clock = fetch_climate_data()
+
+if not clock:
+    st.error("Unable to fetch Climate Clock API data.")
+    st.stop()
+
+# 1️⃣ CO₂ Budget Depletion Line Chart (10-year projection)
+st.header("1️⃣ CO₂ Budget Depletion Projection")
+try:
+    co2_clock = clock.get("co2", {})
+    co2_remaining = float(co2_clock.get("remaining", 0))
+    co2_rate = float(co2_clock.get("rate", 0))
+
+    years = list(range(datetime.now().year, datetime.now().year + 10))
+    values = [max(co2_remaining - i * co2_rate, 0) for i in range(10)]
+    df_proj = pd.DataFrame({"Year": years, "Remaining CO₂ Budget (Gt)": values})
+
+    chart = alt.Chart(df_proj).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(chart, use_container_width=True)
+except Exception as e:
+    st.warning(f"Could not generate CO₂ projection chart: {e}")
+
+# 2️⃣ Renewable Energy Share Donut Chart
+st.header("2️⃣ Global Energy Mix – Renewables vs Others")
+try:
+    renewables_clock = clock.get("renewables", {})
+    percent = float(renewables_clock.get("percentage", 0))
+    energy_df = pd.DataFrame({
+        'Type': ['Renewables', 'Other'],
+        'Percentage': [percent, 100 - percent]
+    })
+
+    pie = 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(pie, use_container_width=True)
+except Exception as e:
+    st.warning(f"Could not load renewable energy chart: {e}")
+
+# 3️⃣ Lifeline Metrics – Positive Climate Interventions
+st.header("3️⃣ Lifeline Metrics – Positive Contributions")
+try:
+    lifelines = clock.get("lifelines", [])
+    lifeline_df = pd.DataFrame([
+        {"Label": l.get("label", ""), "Value": float(l.get("value", 0))}
+        for l in lifelines
+    ])
+    bar = 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(bar, use_container_width=True)
+except Exception as e:
+    st.warning(f"Could not generate lifeline chart: {e}")
+
+# 4️⃣ Climate Deadline Countdown Display
+st.header("4️⃣ Time Left to 1.5°C Threshold")
+try:
+    deadline_clock = clock.get("deadline", {})
+    deadline_parts = list(map(int, deadline_clock.get("time_left", "0:0:0:0:0").split(":")))
+    time_left_str = f"{deadline_parts[0]} years, {deadline_parts[1]} months, {deadline_parts[2]} days"
+    st.success(f"⏳ Estimated Time Remaining Before 1.5°C Limit is Breached: {time_left_str}")
+except Exception as e:
+    st.warning(f"Could not show climate deadline: {e}")
+
+# 5️⃣ Interactive What-If: CO₂ Emission Reduction Simulator
+st.header("5️⃣ CO₂ Budget Simulator – What If We Reduce Emissions?")
+try:
+    st.markdown("📉 Use the slider below to simulate reduced annual emissions and see when we would run out of CO₂ budget.")
+    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 = []
+    budget = co2_remaining
+    year = datetime.now().year
+
+    while budget > 0 and len(sim_years) < 20:
+        sim_years.append(year)
+        sim_budget.append(budget)
+        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)
+except Exception as e:
+    st.warning(f"Could not run simulation: {e}")
+
+# Footer
+st.markdown("---")
+st.caption("Made by Sanidhya and Group Members | Data from Climate Clock API")

requirements.txt

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+streamlit
+pandas
+altair
+requests