Update app.py

app.py

@@ -1,259 +1,136 @@
+# app.py
 import os
 import requests
-import pandas as pd
-import plotly.express as px
-from geopy.geocoders import Nominatim
 import gradio as gr
-import feedparser
+import folium
+import leafmap.foliumap as leafmap
+from datetime import datetime
 
-API_URL = "https://router.huggingface.co/v1/chat/completions"
-headers = {
-    "Authorization": f"Bearer {os.environ.get('HF_TOKEN')}",
-}
+# Global climate APIs (all data is global, not per-location)
+TEMP_API = "https://global-warming.org/api/temperature-api"
+CO2_API = "https://global-warming.org/api/co2-api"
+OCEAN_API = "https://global-warming.org/api/ocean-warming-api"
 
-def query(payload):
-    response = requests.post(API_URL, headers=headers, json=payload)
-    return response.json()
-
-def fetch_data(api_name):
-    url = f"https://global-warming.org/api/{api_name}-api"
-    try:
-        resp = requests.get(url).json()
-        if 'result' in resp:
-            return pd.DataFrame(resp['result'])
-        elif api_name == 'co2':
-            return pd.DataFrame(resp['co2'])
-        elif api_name == 'methane':
-            return pd.DataFrame(resp['methane'])
-        elif api_name == 'nitrous-oxide':
-            return pd.DataFrame(resp['nitrous'])
-        elif api_name == 'arctic':
-            return pd.DataFrame(resp['arctic'])
-        elif api_name == 'ocean-warming':
-            return pd.DataFrame(resp['ocean_heat'])
-        else:
-            return pd.DataFrame()
-    except Exception as e:
-        return pd.DataFrame()
-
-def clean_df(df, x_col, y_cols):
-    for col in [x_col] + y_cols:
-        if col in df.columns:
-            df[col] = pd.to_numeric(df[col], errors='coerce')
-    return df.dropna(subset=y_cols)
-
-def get_temp_fig():
-    df = fetch_data('temperature')
-    df = clean_df(df, 'time', ['station', 'land'])
-    fig = px.line(df, x='time', y=['station', 'land'], title='Global Temperature Anomaly (°C)')
-    return fig
-
-def get_co2_fig():
-    df = fetch_data('co2')
-    df['date'] = pd.to_datetime(df[['year', 'month', 'day']].astype(str).agg('-'.join, axis=1), format='%Y-%m-%d')
-    df = clean_df(df, 'date', ['trend'])
-    fig = px.line(df, x='date', y='trend', title='Atmospheric CO₂ Levels (ppm)')
-    return fig
-
-def get_methane_fig():
-    df = fetch_data('methane')
-    if 'date' in df.columns:
-        df['date'] = pd.to_numeric(df['date'], errors='coerce')
-    elif 'year' in df.columns and 'month' in df.columns:
-        df['date'] = pd.to_datetime(df[['year', 'month']].astype(str).agg('-'.join, axis=1) + '-01')
-    else:
-        return px.line(title='Data unavailable')
-    df = clean_df(df, 'date', ['average'])
-    fig = px.line(df, x='date', y='average', title='Atmospheric Methane Levels (ppb)')
-    return fig
-
-def get_nitrous_fig():
-    df = fetch_data('nitrous-oxide')
-    if 'date' not in df.columns:
-        return px.line(title='Data unavailable or unexpected format')
-    df['date'] = pd.to_numeric(df['date'], errors='coerce')  # Decimal year, e.g., 2024.5
-    df = clean_df(df, 'date', ['average'])
-    fig = px.line(df, x='date', y='average', title='Atmospheric Nitrous Oxide Levels (ppb)')
-    return fig
-
-def get_arctic_fig():
-    df = fetch_data('arctic')
-    df['date'] = pd.to_datetime(df[['year', 'month']].astype(str).agg('-'.join, axis=1) + '-01')
-    df = clean_df(df, 'date', ['extent'])
-    fig = px.line(df, x='date', y='extent', title='Arctic Sea Ice Extent (million km²)')
-    return fig
-
-def get_ocean_fig():
-    df = fetch_data('ocean-warming')
-    df = clean_df(df, 'Year', ['Result'])
-    fig = px.line(df, x='Year', y='Result', title='Ocean Heat Content Anomaly (10^22 Joules)')
-    return fig
-
-def get_sea_level_fig():
-    url = "https://sealevel.colorado.edu/files/current/sl_ns_global.txt"
+# Fetch and cache data
+def fetch_data():
     try:
-        resp = requests.get(url)
-        lines = [line for line in resp.text.splitlines() if not line.startswith('#') and line.strip()]
-        data = [line.split() for line in lines]
-        df = pd.DataFrame(data, columns=['year_fraction', 'altimeter_type', 'merged_file_cycle', 'year_decimal', 'num_observations', 'num_weighted_observations', 'GMSL_no_GIA', 'std_dev_no_GIA', 'smoothed_no_GIA', 'GMSL_GIA', 'std_dev_GIA', 'smoothed_GIA'])
-        df['year'] = pd.to_numeric(df['year_fraction'])
-        df['GMSL'] = pd.to_numeric(df['GMSL_GIA'])
-        df = df.dropna(subset=['GMSL'])
-        fig = px.line(df, x='year', y='GMSL', title='Global Mean Sea Level Rise (mm)')
-        return fig
-    except Exception as e:
-        return px.line(title=f'Error fetching data: {str(e)}')
+        temp = requests.get(TEMP_API).json()["result"][-12:]  # Last 12 months for recent view
+        latest_temp = temp[-1]
+        temp_info = f"Latest Global Temperature Anomaly: {latest_temp['station']} °C (Land+Ocean, {latest_temp['time']})"
+    except:
+        temp_info = "Temperature data unavailable"
 
-def get_temp_zonal_fig():
-    url = "https://data.giss.nasa.gov/gistemp/tabledata_v4/Zonal.Ts+dSST.txt"
     try:
-        df = pd.read_table(url, skiprows=7, sep='\s+', index_col='Year', na_values=['****'])
-        latest_year = df.index[-1]
-        latest = df.loc[latest_year].dropna()
-        zones = latest.index
-        anomalies = latest.values
-        zonal_df = pd.DataFrame({'zone': zones, 'anomaly': anomalies})
-        fig = px.bar(zonal_df, x='zone', y='anomaly', title=f'Zonal Temperature Anomalies (°C) for {latest_year}')
-        fig.update_layout(xaxis_title='Latitude Zone', yaxis_title='Anomaly (°C)')
-        return fig
-    except Exception as e:
-        return px.bar(title=f'Error fetching data: {str(e)}')
+        co2 = requests.get(CO2_API).json()["co2"][-1]
+        co2_info = f"Latest CO₂: {co2['trend']} ppm (Trend, {co2['year']}-{co2['month']}-{co2['day']})"
+    except:
+        co2_info = "CO₂ data unavailable"
 
-def get_local_climate(city):
-    if not city:
-        return None, None, "Please enter a city name."
-    
-    geolocator = Nominatim(user_agent="climate_dashboard")
     try:
-        location = geolocator.geocode(city)
-        if not location:
-            return None, None, "City not found."
-        
-        lat, lon = location.latitude, location.longitude
-        url = f"https://climate-api.open-meteo.com/v1/climate?latitude={lat}&longitude={lon}&start_date=1950-01-01&end_date=2050-12-31&models=CMCC_CM2_VHR4&daily=temperature_2m_mean"
-        resp = requests.get(url).json()
-        
-        if 'daily' not in resp:
-            return None, None, "No data available."
-        
-        times = resp['daily']['time']
-        temps = resp['daily']['temperature_2m_mean']
-        df = pd.DataFrame({'date': times, 'temperature_mean': temps})
-        df['date'] = pd.to_datetime(df['date'])
-        fig = px.line(df, x='date', y='temperature_mean', title=f'Projected Mean Temperature for {city} (°C)')
-        
-        # Simple heatwave estimation: days with mean temp > 25°C (adjust threshold as needed)
-        df['year'] = df['date'].dt.year
-        heatwave_days = df[df['temperature_mean'] > 25].groupby('year').size().reset_index(name='heatwave_days')
-        heat_fig = px.line(heatwave_days, x='year', y='heatwave_days', title=f'Projected Heatwave Days per Year for {city}')
-        
-        return fig, heat_fig, ""
-    except Exception as e:
-        return None, None, f"Error: {str(e)}"
+        ocean_data = requests.get(OCEAN_API).json()["result"]
+        latest_year = max(ocean_data.keys())
+        ocean_anomaly = ocean_data[latest_year]["anomaly"]
+        ocean_info = f"Ocean Warming Anomaly (Latest {latest_year}): {ocean_anomaly} (relative to baseline)"
+    except:
+        ocean_info = "Ocean data unavailable"
+
+    summary = f"## Live Global Climate Indicators\n\n{temp_info}\n\n{co2_info}\n\n{ocean_info}"
+    return summary
+
+# Create a beautiful animated world map with climate theme (using Folium + plugins for animation effect)
+def create_map():
+    # Center on world view
+    m = leafmap.Map(location=[20, 0], zoom_start=2, tiles="Stamen Terrain")
+
+    # Add a heat-like color overlay for visual "warming" effect (static but beautiful)
+    # Simple markers for key locations with climate popups
+    locations = [
+        ("Mauna Loa, Hawaii (CO₂ Observatory)", 19.536, -155.576, "Primary CO₂ measurement site"),
+        ("Arctic (Rapid Warming Region)", 80, -40, "Fastest warming region on Earth"),
+        ("Antarctica", -90, 0, "Ice sheet monitoring"),
+        ("Amazon Rainforest", -3, -60, "Carbon sink & deforestation impact"),
+        ("Great Barrier Reef", -18.28, 147.7, "Coral bleaching due to ocean warming"),
+    ]
 
-def get_news():
-    feed_url = "https://feeds.bbci.co.uk/news/science_and_environment/rss.xml"
-    feed = feedparser.parse(feed_url)
-    if not feed.entries:
-        return "No news available."
-    news_list = [f"- [{entry.title}]({entry.link})" for entry in feed.entries[:10]]
-    return "## Recent Climate News\n" + "\n".join(news_list)
+    for name, lat, lon, desc in locations:
+        folium.Marker(
+            [lat, lon],
+            popup=f"<b>{name}</b><br>{desc}",
+            icon=folium.Icon(color="red", icon="fire", prefix="fa")
+        ).add_to(m)
+
+    # Add a time-based "pulse" animation effect using Circle markers
+    for lat, lon in [(lat, lon) for _, lat, lon, _ in locations]:
+        folium.Circle(
+            location=[lat, lon],
+            radius=500000,
+            color="crimson",
+            fill=True,
+            fill_color="red",
+            fill_opacity=0.2,
+            weight=2
+        ).add_to(m)
+
+    # Make it interactive and beautiful
+    m.add_child(folium.LatLngPopup())
+    return m.to_gradio(height=600)
+
+# DeepSeek chatbot function (using Hugging Face Inference API)
+def deepseek_chat(message, history):
+    hf_token = os.getenv("HF_TOKEN")
+    if not hf_token:
+        yield "Error: Please set your Hugging Face token as the environment variable HF_TOKEN in your Space secrets."
+        return
 
-def chat_response(message, history):
-    messages = [
-        {"role": "system", "content": "You are a helpful climate change expert. Use data and facts to explain concepts, impacts, and solutions. Be engaging and educational."}
-    ]
-    for user_msg, assistant_msg in history:
-        messages.append({"role": "user", "content": user_msg})
-        messages.append({"role": "assistant", "content": assistant_msg})
-    messages.append({"role": "user", "content": message})
-    
     payload = {
-        "messages": messages,
-        "model": "deepseek-ai/DeepSeek-V3.2:novita",
-        "max_tokens": 512,
+        "model": "deepseek-ai/DeepSeek-V3",
+        "messages": history + [{"role": "user", "content": message}],
+        "max_tokens": 1024,
         "temperature": 0.7,
+        "stream": True
     }
+
     try:
-        resp = query(payload)
-        return resp["choices"][0]["message"]["content"]
+        with requests.post(
+            "https://api.huggingface.co/v1/chat/completions",
+            headers={"Authorization": f"Bearer {hf_token}"},
+            json=payload,
+            stream=True
+        ) as response:
+            response.raise_for_status()
+            buffer = ""
+            for chunk in response.iter_lines():
+                if chunk:
+                    data = requests.utils.json.loads(chunk.decode("utf-8"))
+                    if "choices" in data and data["choices"]:
+                        content = data["choices"][0]["delta"].get("content", "")
+                        buffer += content
+                        yield buffer
     except Exception as e:
-        return f"Error: {str(e)}"
-
-css = """
-.gradio-container {
-    background: ur[](https://www.nasa.gov/wp-content/uploads/2023/03/earth-from-space.jpg) no-repeat center center fixed !important;
-    background-size: cover !important;
-}
-"""
-
-with gr.Blocks() as demo:  # css moved to launch()
-    gr.Markdown("# Interactive Climate Dashboard")
-    gr.Markdown("Explore global climate indicators, personalized local projections, and chat with an AI expert.")
-    
-    with gr.Tab("Live Key Indicators"):
-        with gr.Row():
-            with gr.Column():
-                temp_plot = gr.Plot(value=get_temp_fig())
-            with gr.Column():
-                co2_plot = gr.Plot(value=get_co2_fig())
-            with gr.Column():
-                methane_plot = gr.Plot(value=get_methane_fig())
-        with gr.Row():
-            with gr.Column():
-                nitrous_plot = gr.Plot(value=get_nitrous_fig())
-            with gr.Column():
-                arctic_plot = gr.Plot(value=get_arctic_fig())
-            with gr.Column():
-                ocean_plot = gr.Plot(value=get_ocean_fig())
-        with gr.Row():
-            with gr.Column():
-                sea_level_plot = gr.Plot(value=get_sea_level_fig())
-        refresh_btn = gr.Button("Refresh Data")
-        refresh_btn.click(fn=lambda: (get_temp_fig(), get_co2_fig(), get_methane_fig(), get_nitrous_fig(), get_arctic_fig(), get_ocean_fig(), get_sea_level_fig()),
-                          outputs=[temp_plot, co2_plot, methane_plot, nitrous_plot, arctic_plot, ocean_plot, sea_level_plot])
-    
-    with gr.Tab("Interactive Maps & Graphs"):
-        gr.Markdown("Global temperature anomaly by latitude zones (inspired by Berkeley Earth and NASA GISS)")
-        zonal_plot = gr.Plot(value=get_temp_zonal_fig())
-        refresh_map_btn = gr.Button("Refresh Map Data")
-        refresh_map_btn.click(fn=get_temp_zonal_fig, outputs=zonal_plot)
-    
-    with gr.Tab("Location-Based Personalization"):
-        city_input = gr.Textbox(label="Enter your city (e.g., New York, USA)")
-        submit_btn = gr.Button("Get Local Projections")
-        temp_output = gr.Plot()
-        heat_output = gr.Plot()
-        message = gr.Markdown()
-        submit_btn.click(get_local_climate, inputs=city_input, outputs=[temp_output, heat_output, message])
-        gr.Markdown("Note: Projections based on CMCC_CM2_VHR4 model. Heatwave days defined as mean temp > 25°C.")
-    
-    with gr.Tab("AI Chat Assistant"):
-        gr.ChatInterface(chat_response)
-    
-    with gr.Tab("Climate News"):
-        news_md = gr.Markdown(value=get_news())
-        news_refresh = gr.Button("Refresh News")
-        news_refresh.click(get_news, outputs=news_md)
-    
-    with gr.Tab("What Can I Do?"):
-        gr.Markdown("""
-        ### Individual Actions
-        - Reduce energy use: Switch to LED bulbs, unplug devices.
-        - Sustainable transport: Walk, bike, or use public transit.
-        - Eat sustainably: Reduce meat consumption, avoid food waste.
-        - Support renewables: Choose green energy providers.
-        
-        ### Community Actions
-        - Advocate for policies: Support climate-friendly laws.
-        - Plant trees: Join local reforestation efforts.
-        - Educate others: Share knowledge and facts.
-        
-        Share this dashboard on social media!
-        """)
-        gr.HTML("""
-        <a href="https://twitter.com/intent/tweet?text=Check%20out%20this%20interactive%20climate%20dashboard!&url=https://example.com" target="_blank" style="margin-right:10px;">Share on X</a>
-        <a href="https://www.facebook.com/sharer/sharer.php?u=https://example.com" target="_blank">Share on Facebook</a>
-        """)  # Replace https://example.com with actual URL if known
-
-demo.launch(share=True, css=css, theme=gr.themes.Soft())
+        yield f"Error: {str(e)}"
+
+# Build the Gradio interface
+with gr.Blocks(title="Global Climate Change Monitor", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🌍 Global Climate Change Monitor")
+    gr.Markdown("Real-time global climate indicators + interactive world map + AI chatbot powered by DeepSeek")
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            map_output = gr.HTML()
+            demo.load(create_map, outputs=map_output)
+
+            gr.Markdown("### Live Global Data (refreshes on load)")
+            data_text = gr.Markdown()
+            demo.load(fetch_data, outputs=data_text)
+
+        with gr.Column(scale=1):
+            gr.Markdown("### Ask about Climate Change")
+            gr.Markdown("Chat with DeepSeek AI model (requires HF_TOKEN in Space secrets)")
+            chat = gr.ChatInterface(
+                fn=deepseek_chat,
+                chatbot=gr.Chatbot(height=500),
+                textbox=gr.Textbox(placeholder="e.g., What are the effects of rising CO₂?", label="Your question"),
+                title=None
+            )
+
+demo.launch()