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| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import tempfile | |
| import plotly.graph_objects as go | |
| import plotly.express as px | |
| from plotly.subplots import make_subplots | |
| import requests | |
| from datetime import datetime, timedelta | |
| import warnings | |
| import json | |
| warnings.filterwarnings('ignore') | |
| class EnhancedOceanClimateAgent: | |
| def __init__(self): | |
| self.anomaly_threshold = 2.0 | |
| self.critical_temp_change = 1.5 | |
| self.noaa_base_url = "https://api.tidesandcurrents.noaa.gov/api/prod/datagetter" | |
| self.noaa_stations_url = "https://api.tidesandcurrents.noaa.gov/mdapi/prod/webapi/stations.json" | |
| # Popular NOAA stations for different regions | |
| self.default_stations = { | |
| "San Francisco, CA": "9414290", | |
| "New York, NY": "8518750", | |
| "Miami, FL": "8723214", | |
| "Seattle, WA": "9447130", | |
| "Boston, MA": "8443970", | |
| "Los Angeles, CA": "9410660", | |
| "Galveston, TX": "8771450", | |
| "Charleston, SC": "8665530" | |
| } | |
| def get_noaa_data(self, station_id, product, start_date, end_date, units="metric"): | |
| """Fetch data from NOAA API""" | |
| params = { | |
| 'product': product, | |
| 'application': 'OceanClimateAgent', | |
| 'begin_date': start_date.strftime('%Y%m%d'), | |
| 'end_date': end_date.strftime('%Y%m%d'), | |
| 'station': station_id, | |
| 'time_zone': 'gmt', | |
| 'units': units, | |
| 'format': 'json' | |
| } | |
| try: | |
| print(f"📡 Requesting {product} data for station {station_id}") | |
| print("🕐 Date range:", start_date.strftime('%Y-%m-%d'), "to", end_date.strftime('%Y-%m-%d')) | |
| response = requests.get(self.noaa_base_url, params=params, timeout=30) | |
| print(f"🔁 Status code: {response.status_code}") | |
| if response.status_code == 200: | |
| data = response.json() | |
| if 'data' in data: | |
| print(f"✅ Data received: {len(data['data'])} records for {product}") | |
| return pd.DataFrame(data['data']) | |
| elif 'error' in data: | |
| print(f"❌ NOAA error: {data['error'].get('message')}") | |
| else: | |
| print(f"❌ Unknown response structure: {data}") | |
| else: | |
| print(f"❌ API HTTP error {response.status_code}: {response.text}") | |
| except Exception as e: | |
| print(f"❌ Request failed for {product}: {str(e)}") | |
| return None | |
| def get_comprehensive_station_data(self, station_name, days_back=30): | |
| """Get comprehensive data from a NOAA station""" | |
| station_id = self.default_stations.get(station_name) | |
| if not station_id: | |
| return None, "Station not found" | |
| # Ensure end_date is not in the future (NOAA does not support future data) | |
| today = datetime.utcnow().replace(hour=0, minute=0, second=0, microsecond=0) | |
| end_date = min(datetime.utcnow(), today) | |
| start_date = end_date - timedelta(days=days_back) | |
| # Available NOAA products | |
| products_to_fetch = { | |
| 'water_level': 'water_level', | |
| 'water_temperature': 'water_temperature', | |
| 'air_temperature': 'air_temperature', | |
| 'wind': 'wind', | |
| 'air_pressure': 'air_pressure', | |
| 'salinity': 'salinity', | |
| 'currents': 'currents' | |
| } | |
| all_data = {} | |
| success_count = 0 | |
| for product_name, product_code in products_to_fetch.items(): | |
| data = self.get_noaa_data(station_id, product_code, start_date, end_date) | |
| if data is not None and not data.empty: | |
| all_data[product_name] = data | |
| success_count += 1 | |
| if success_count == 0: | |
| return None, "No data available for this station and time period" | |
| return all_data, f"Successfully retrieved {success_count}/{len(products_to_fetch)} data types" | |
| def process_noaa_data(self, raw_data): | |
| """Process and combine NOAA data for analysis""" | |
| if not raw_data: | |
| return None | |
| # Process water level data (primary dataset) | |
| if 'water_level' in raw_data: | |
| df = raw_data['water_level'].copy() | |
| df['datetime'] = pd.to_datetime(df['t']) | |
| df['water_level'] = pd.to_numeric(df['v'], errors='coerce') | |
| # Add other parameters when available | |
| if 'water_temperature' in raw_data: | |
| temp_df = raw_data['water_temperature'].copy() | |
| temp_df['datetime'] = pd.to_datetime(temp_df['t']) | |
| temp_df['water_temp'] = pd.to_numeric(temp_df['v'], errors='coerce') | |
| df = df.merge(temp_df[['datetime', 'water_temp']], on='datetime', how='left') | |
| if 'air_temperature' in raw_data: | |
| air_temp_df = raw_data['air_temperature'].copy() | |
| air_temp_df['datetime'] = pd.to_datetime(air_temp_df['t']) | |
| air_temp_df['air_temp'] = pd.to_numeric(air_temp_df['v'], errors='coerce') | |
| df = df.merge(air_temp_df[['datetime', 'air_temp']], on='datetime', how='left') | |
| if 'wind' in raw_data: | |
| wind_df = raw_data['wind'].copy() | |
| wind_df['datetime'] = pd.to_datetime(wind_df['t']) | |
| wind_df['wind_speed'] = pd.to_numeric(wind_df['s'], errors='coerce') | |
| wind_df['wind_direction'] = pd.to_numeric(wind_df['d'], errors='coerce') | |
| df = df.merge(wind_df[['datetime', 'wind_speed', 'wind_direction']], on='datetime', how='left') | |
| if 'air_pressure' in raw_data: | |
| pressure_df = raw_data['air_pressure'].copy() | |
| pressure_df['datetime'] = pd.to_datetime(pressure_df['t']) | |
| pressure_df['air_pressure'] = pd.to_numeric(pressure_df['v'], errors='coerce') | |
| df = df.merge(pressure_df[['datetime', 'air_pressure']], on='datetime', how='left') | |
| if 'salinity' in raw_data: | |
| salinity_df = raw_data['salinity'].copy() | |
| salinity_df['datetime'] = pd.to_datetime(salinity_df['t']) | |
| salinity_df['salinity'] = pd.to_numeric(salinity_df['v'], errors='coerce') | |
| df = df.merge(salinity_df[['datetime', 'salinity']], on='datetime', how='left') | |
| return df | |
| return None | |
| def detect_anomalies(self, data, column, window=24): # 24 hours for hourly data | |
| """Detect anomalies using rolling statistics""" | |
| if column not in data.columns or data[column].isna().all(): | |
| return pd.Series([False] * len(data)), pd.Series([0] * len(data)) | |
| rolling_mean = data[column].rolling(window=window, center=True, min_periods=1).mean() | |
| rolling_std = data[column].rolling(window=window, center=True, min_periods=1).std() | |
| # Avoid division by zero | |
| rolling_std = rolling_std.fillna(1) | |
| rolling_std = rolling_std.replace(0, 1) | |
| z_scores = np.abs((data[column] - rolling_mean) / rolling_std) | |
| anomalies = z_scores > self.anomaly_threshold | |
| return anomalies, z_scores | |
| def calculate_trends(self, data, column, hours=168): # 7 days | |
| """Calculate trend over specified period""" | |
| if column not in data.columns or data[column].isna().all(): | |
| return 0 | |
| recent_data = data.tail(hours) | |
| if len(recent_data) < 2: | |
| return 0 | |
| x = np.arange(len(recent_data)) | |
| y = recent_data[column].dropna() | |
| if len(y) < 2: | |
| return 0 | |
| x = x[:len(y)] | |
| slope = np.polyfit(x, y, 1)[0] if len(x) > 1 else 0 | |
| return slope | |
| def generate_climate_analysis(self, data, station_name): | |
| """Generate comprehensive climate analysis""" | |
| if data is None or data.empty: | |
| return {}, [] | |
| analysis = {} | |
| alerts = [] | |
| # Water level analysis | |
| if 'water_level' in data.columns: | |
| wl_trend = self.calculate_trends(data, 'water_level') | |
| analysis['water_level_trend'] = wl_trend * 24 # per day | |
| if abs(wl_trend * 24) > 5: # >5cm per day change | |
| alerts.append(f"Significant water level change: {wl_trend*24:.1f}cm/day at {station_name}") | |
| # Temperature analysis | |
| if 'water_temp' in data.columns: | |
| temp_trend = self.calculate_trends(data, 'water_temp') | |
| analysis['water_temp_trend'] = temp_trend * 24 # per day | |
| if temp_trend * 24 > 0.5: # >0.5°C per day | |
| alerts.append(f"Rapid water temperature rise: {temp_trend*24:.2f}°C/day at {station_name}") | |
| # Anomaly detection | |
| for col in ['water_level', 'water_temp', 'wind_speed']: | |
| if col in data.columns: | |
| anomalies, z_scores = self.detect_anomalies(data, col) | |
| anomaly_pct = (anomalies.sum() / len(data)) * 100 | |
| analysis[f'{col}_anomaly_frequency'] = anomaly_pct | |
| if anomaly_pct > 10: | |
| alerts.append(f"High {col.replace('_', ' ')} anomaly frequency: {anomaly_pct:.1f}% at {station_name}") | |
| if not alerts: | |
| alerts.append(f"✅ No significant anomalies detected at {station_name}") | |
| return analysis, alerts | |
| # Initialize the enhanced agent | |
| agent = EnhancedOceanClimateAgent() | |
| def analyze_real_ocean_data(station_name, days_back, anomaly_sensitivity, use_real_data): | |
| """Main analysis function with real NOAA data""" | |
| agent.anomaly_threshold = anomaly_sensitivity | |
| if use_real_data: | |
| # Fetch real NOAA data | |
| raw_data, status_msg = agent.get_comprehensive_station_data(station_name, days_back) | |
| if raw_data is None: | |
| return None, None, None, f"Error: {status_msg}", "No alerts - data unavailable", None | |
| # Process the data | |
| data = agent.process_noaa_data(raw_data) | |
| if data is None or data.empty: | |
| return None, None, None, "No processable data available", "No alerts - data unavailable", None | |
| data_source = f"Real NOAA data from {station_name} ({status_msg})" | |
| else: | |
| # Use synthetic data for demonstration | |
| data = generate_synthetic_data(days_back) | |
| data_source = f"Synthetic demonstration data ({days_back} days)" | |
| # Generate analysis and alerts | |
| analysis, alerts = agent.generate_climate_analysis(data, station_name) | |
| # Create visualizations | |
| fig1 = create_main_dashboard(data, agent) | |
| fig2 = create_anomaly_plots(data, agent) | |
| fig3 = create_correlation_plot(data) | |
| # Format analysis text | |
| analysis_text = format_analysis_results(analysis, data_source) | |
| alerts_text = "\n".join([f"- {alert}" for alert in alerts]) | |
| # Create CSV for download | |
| import tempfile | |
| #Create CSV | |
| def save_csv_temp(data): | |
| tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode='w', newline='', encoding='utf-8') | |
| data.to_csv(tmp.name, index=False) | |
| tmp.close() | |
| return tmp.name | |
| csv_file_path = save_csv_temp(data) | |
| return fig1, fig2, fig3, analysis_text, alerts_text, csv_file_path | |
| def generate_synthetic_data(days): | |
| """Generate synthetic data for demonstration""" | |
| dates = pd.date_range(start=datetime.now() - timedelta(days=days), periods=days*24, freq='H') | |
| # Synthetic water level with tidal patterns | |
| tidal_pattern = 2 * np.sin(2 * np.pi * np.arange(len(dates)) / 12.42) # M2 tide | |
| water_level = 100 + tidal_pattern + np.random.normal(0, 0.3, len(dates)) | |
| # Water temperature with daily cycle | |
| daily_temp_cycle = 2 * np.sin(2 * np.pi * np.arange(len(dates)) / 24) | |
| water_temp = 15 + daily_temp_cycle + np.random.normal(0, 0.5, len(dates)) | |
| # Wind patterns | |
| wind_speed = 5 + 3 * np.sin(2 * np.pi * np.arange(len(dates)) / (24*3)) + np.random.normal(0, 1, len(dates)) | |
| wind_direction = 180 + 45 * np.sin(2 * np.pi * np.arange(len(dates)) / (24*2)) + np.random.normal(0, 20, len(dates)) | |
| return pd.DataFrame({ | |
| 'datetime': dates, | |
| 'water_level': water_level, | |
| 'water_temp': water_temp, | |
| 'wind_speed': np.maximum(0, wind_speed), | |
| 'wind_direction': wind_direction % 360, | |
| 'air_pressure': 1013 + np.random.normal(0, 10, len(dates)) | |
| }) | |
| def create_main_dashboard(data, agent): | |
| """Create main dashboard visualization""" | |
| fig = make_subplots( | |
| rows=2, cols=2, | |
| subplot_titles=('Water Level', 'Water Temperature', 'Wind Speed', 'Air Pressure'), | |
| vertical_spacing=0.1 | |
| ) | |
| # Water Level | |
| if 'water_level' in data.columns: | |
| fig.add_trace( | |
| go.Scatter(x=data['datetime'], y=data['water_level'], | |
| name='Water Level', line=dict(color='blue')), | |
| row=1, col=1 | |
| ) | |
| # Add anomalies | |
| anomalies, _ = agent.detect_anomalies(data, 'water_level') | |
| if anomalies.any(): | |
| anomaly_data = data[anomalies] | |
| fig.add_trace( | |
| go.Scatter(x=anomaly_data['datetime'], y=anomaly_data['water_level'], | |
| mode='markers', name='Anomalies', | |
| marker=dict(color='red', size=6)), | |
| row=1, col=1 | |
| ) | |
| # Water Temperature | |
| if 'water_temp' in data.columns: | |
| fig.add_trace( | |
| go.Scatter(x=data['datetime'], y=data['water_temp'], | |
| name='Water Temp', line=dict(color='red')), | |
| row=1, col=2 | |
| ) | |
| # Wind Speed | |
| if 'wind_speed' in data.columns: | |
| fig.add_trace( | |
| go.Scatter(x=data['datetime'], y=data['wind_speed'], | |
| name='Wind Speed', line=dict(color='green')), | |
| row=2, col=1 | |
| ) | |
| # Air Pressure | |
| if 'air_pressure' in data.columns: | |
| fig.add_trace( | |
| go.Scatter(x=data['datetime'], y=data['air_pressure'], | |
| name='Air Pressure', line=dict(color='purple')), | |
| row=2, col=2 | |
| ) | |
| fig.update_layout(height=600, showlegend=False, title_text="Ocean and Atmospheric Data Dashboard") | |
| return fig | |
| def create_anomaly_plots(data, agent): | |
| """Create anomaly detection plots""" | |
| fig = make_subplots( | |
| rows=1, cols=2, | |
| subplot_titles=('Water Level Anomalies', 'Temperature Anomalies') | |
| ) | |
| # Water level anomalies | |
| if 'water_level' in data.columns: | |
| _, z_scores = agent.detect_anomalies(data, 'water_level') | |
| fig.add_trace( | |
| go.Scatter(x=data['datetime'], y=z_scores, | |
| mode='lines', name='Water Level Z-Score'), | |
| row=1, col=1 | |
| ) | |
| fig.add_hline(y=agent.anomaly_threshold, line_dash="dash", line_color="red", row=1, col=1) | |
| # Temperature anomalies | |
| if 'water_temp' in data.columns: | |
| _, z_scores = agent.detect_anomalies(data, 'water_temp') | |
| fig.add_trace( | |
| go.Scatter(x=data['datetime'], y=z_scores, | |
| mode='lines', name='Temperature Z-Score', line=dict(color='red')), | |
| row=1, col=2 | |
| ) | |
| fig.add_hline(y=agent.anomaly_threshold, line_dash="dash", line_color="red", row=1, col=2) | |
| fig.update_layout(height=400, showlegend=False, title_text="Anomaly Detection Analysis") | |
| return fig | |
| def create_correlation_plot(data): | |
| """Create correlation heatmap""" | |
| numeric_cols = [col for col in ['water_level', 'water_temp', 'wind_speed', 'air_pressure'] | |
| if col in data.columns] | |
| if len(numeric_cols) < 2: | |
| # Return empty plot if insufficient data | |
| fig = go.Figure() | |
| fig.add_annotation(text="Insufficient data for correlation analysis", | |
| xref="paper", yref="paper", x=0.5, y=0.5, showarrow=False) | |
| return fig | |
| corr_matrix = data[numeric_cols].corr() | |
| fig = px.imshow(corr_matrix, | |
| labels=dict(color="Correlation"), | |
| color_continuous_scale='RdBu_r', | |
| aspect="auto", | |
| title="Parameter Correlations") | |
| return fig | |
| def format_analysis_results(analysis, data_source): | |
| """Format analysis results for display""" | |
| result = f"### {data_source}\n\n**Key Trends:**\n" | |
| for key, value in analysis.items(): | |
| if 'trend' in key: | |
| param = key.replace('_trend', '').replace('_', ' ').title() | |
| unit = 'cm/day' if 'water_level' in key else '°C/day' if 'temp' in key else 'units/day' | |
| result += f"- {param}: {value:.3f} {unit}\n" | |
| elif 'anomaly_frequency' in key: | |
| param = key.replace('_anomaly_frequency', '').replace('_', ' ').title() | |
| result += f"- {param} anomalies: {value:.1f}%\n" | |
| return result | |
| # Create Gradio interface | |
| with gr.Blocks(title="Enhanced Ocean Climate Monitoring AI Agent", theme=gr.themes.Ocean()) as demo: | |
| gr.Markdown(""" | |
| # Enhanced Ocean Climate Monitoring AI Agent | |
| ### Real-time Analysis with NOAA Data Integration | |
| This enhanced AI agent can fetch real ocean data from NOAA stations or use synthetic data for demonstration. | |
| Monitor water levels, temperature, currents, and detect climate anomalies at major coastal locations. | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown("### Configuration") | |
| station_name = gr.Dropdown( | |
| choices=list(agent.default_stations.keys()), | |
| value="San Francisco, CA", | |
| label="NOAA Station Location" | |
| ) | |
| days_back = gr.Slider( | |
| minimum=7, | |
| maximum=90, | |
| value=30, | |
| step=1, | |
| label="Days of Historical Data" | |
| ) | |
| anomaly_sensitivity = gr.Slider( | |
| minimum=1.0, | |
| maximum=3.0, | |
| value=2.0, | |
| step=0.1, | |
| label="Anomaly Detection Sensitivity" | |
| ) | |
| use_real_data = gr.Checkbox( | |
| label="Use Real NOAA Data", | |
| value=True, | |
| info="Uncheck to use synthetic data" | |
| ) | |
| analyze_btn = gr.Button("Analyze Ocean Data", variant="primary") | |
| with gr.Column(scale=2): | |
| gr.Markdown("### Climate Alerts") | |
| alerts_output = gr.Markdown() | |
| with gr.Row(): | |
| analysis_output = gr.Markdown() | |
| with gr.Tab("Main Dashboard"): | |
| dashboard_plot = gr.Plot() | |
| with gr.Tab("Anomaly Detection"): | |
| anomaly_plot = gr.Plot() | |
| with gr.Tab("Correlations"): | |
| correlation_plot = gr.Plot() | |
| with gr.Tab("Data Export"): | |
| gr.Markdown("### Download Analyzed Data") | |
| csv_output = gr.File(label="Download CSV Data") | |
| gr.Markdown("*Note: Real NOAA data usage is subject to their terms of service*") | |
| # Set up the analysis function | |
| analyze_btn.click( | |
| fn=analyze_real_ocean_data, | |
| inputs=[station_name, days_back, anomaly_sensitivity, use_real_data], | |
| outputs=[dashboard_plot, anomaly_plot, correlation_plot, analysis_output, alerts_output, csv_output] | |
| ) | |
| # Auto-run on startup with synthetic data | |
| demo.load( | |
| fn=analyze_real_ocean_data, | |
| inputs=[ | |
| gr.Text(value="San Francisco, CA", visible=False), | |
| gr.Number(value=30, visible=False), | |
| gr.Number(value=2.0, visible=False), | |
| gr.Checkbox(value=False, visible=False) # Start with synthetic data | |
| ], | |
| outputs=[dashboard_plot, anomaly_plot, correlation_plot, analysis_output, alerts_output, csv_output] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |