Add weather agent for Del Mar, California

agents/weather_agent.py

@@ -0,0 +1,232 @@
+# agents/weather_agent.py
+# Weather Agent: Fetches and displays weather information for Del Mar, California
+
+import requests
+import json
+from datetime import datetime
+
+def get_weather_delmar(use_mock=False):
+    """
+    Fetches current weather data for Del Mar, California.
+    Uses wttr.in service which provides free weather data without API key.
+
+    Args:
+        use_mock: If True, returns mock data (useful for testing without internet)
+
+    Returns:
+        str: Formatted weather report or error message
+    """
+    print("--- Running Weather Agent: Del Mar, California ---")
+
+    # If mock mode or if we can't connect, use sample data
+    if use_mock:
+        return get_mock_weather_delmar()
+
+    try:
+        # Del Mar, California coordinates
+        location = "Del+Mar,California"
+
+        # Using wttr.in API for weather data (free, no API key needed)
+        url = f"https://wttr.in/{location}?format=j1"
+
+        print(f"Fetching weather data from: {url}")
+        response = requests.get(url, timeout=10)
+        response.raise_for_status()
+
+        weather_data = response.json()
+
+        # Extract current conditions
+        current = weather_data.get('current_condition', [{}])[0]
+        nearest_area = weather_data.get('nearest_area', [{}])[0]
+
+        # Format the weather report
+        report = format_weather_report(current, nearest_area, weather_data)
+
+        print("Weather data fetched successfully.")
+        return report
+
+    except requests.exceptions.RequestException as e:
+        print(f"Warning: Failed to fetch live weather data: {e}")
+        print("Falling back to mock weather data for demonstration...")
+        return get_mock_weather_delmar()
+    except Exception as e:
+        print(f"Warning: Unexpected error occurred: {e}")
+        print("Falling back to mock weather data for demonstration...")
+        return get_mock_weather_delmar()
+
+
+def get_mock_weather_delmar():
+    """
+    Returns mock weather data for Del Mar, California.
+    This is used when internet connection is unavailable.
+
+    Returns:
+        str: Formatted weather report with sample data
+    """
+    print("Using mock weather data for demonstration purposes...")
+
+    # Get today's date
+    today = datetime.now().strftime("%A, %B %d, %Y")
+
+    # Mock weather data representing typical Del Mar weather
+    report = f"""
+{'='*60}
+WEATHER REPORT FOR DEL MAR, CALIFORNIA
+{'='*60}
+
+Location: Del Mar, San Diego County, California, USA
+Date: {today}
+
+CURRENT CONDITIONS:
+------------------
+Weather: Partly cloudy
+Temperature: 72°F (22°C)
+Feels Like: 70°F (21°C)
+Humidity: 65%
+Wind: 8 mph from W
+UV Index: 6
+Visibility: 10 miles
+Pressure: 1013 mb
+Cloud Cover: 25%
+
+TODAY'S FORECAST:
+-----------------
+High: 75°F (24°C)
+Low: 62°F (17°C)
+Average: 68°F (20°C)
+
+HOURLY HIGHLIGHTS:
+------------------
+9:00 AM: 68°F - Sunny
+12:00 PM: 73°F - Partly cloudy
+6:00 PM: 70°F - Partly cloudy
+
+{'='*60}
+
+Note: This is demonstration data. Del Mar typically enjoys a mild 
+Mediterranean climate with comfortable temperatures year-round, 
+coastal breezes, and low rainfall. For live weather data, ensure 
+internet connectivity is available.
+
+{'='*60}
+"""
+
+    return report
+
+
+def format_weather_report(current, area, full_data):
+    """
+    Formats weather data into a readable report.
+
+    Args:
+        current: Current weather conditions
+        area: Location information
+        full_data: Full weather data including forecast
+
+    Returns:
+        str: Formatted weather report
+    """
+    # Extract location info
+    area_name = area.get('areaName', [{}])[0].get('value', 'Del Mar')
+    region = area.get('region', [{}])[0].get('value', 'California')
+    country = area.get('country', [{}])[0].get('value', 'USA')
+
+    # Extract current conditions
+    temp_f = current.get('temp_F', 'N/A')
+    temp_c = current.get('temp_C', 'N/A')
+    feels_like_f = current.get('FeelsLikeF', 'N/A')
+    feels_like_c = current.get('FeelsLikeC', 'N/A')
+    humidity = current.get('humidity', 'N/A')
+    weather_desc = current.get('weatherDesc', [{}])[0].get('value', 'N/A')
+    wind_speed = current.get('windspeedMiles', 'N/A')
+    wind_dir = current.get('winddir16Point', 'N/A')
+    uv_index = current.get('uvIndex', 'N/A')
+    visibility = current.get('visibilityMiles', 'N/A')
+    pressure = current.get('pressure', 'N/A')
+    cloud_cover = current.get('cloudcover', 'N/A')
+
+    # Get today's date
+    today = datetime.now().strftime("%A, %B %d, %Y")
+
+    # Build the report
+    report = f"""
+{'='*60}
+WEATHER REPORT FOR DEL MAR, CALIFORNIA
+{'='*60}
+
+Location: {area_name}, {region}, {country}
+Date: {today}
+
+CURRENT CONDITIONS:
+------------------
+Weather: {weather_desc}
+Temperature: {temp_f}°F ({temp_c}°C)
+Feels Like: {feels_like_f}°F ({feels_like_c}°C)
+Humidity: {humidity}%
+Wind: {wind_speed} mph from {wind_dir}
+UV Index: {uv_index}
+Visibility: {visibility} miles
+Pressure: {pressure} mb
+Cloud Cover: {cloud_cover}%
+
+"""
+
+    # Add forecast if available
+    if 'weather' in full_data and len(full_data['weather']) > 0:
+        report += "TODAY'S FORECAST:\n"
+        report += "-" * 16 + "\n"
+
+        today_forecast = full_data['weather'][0]
+        max_temp_f = today_forecast.get('maxtempF', 'N/A')
+        max_temp_c = today_forecast.get('maxtempC', 'N/A')
+        min_temp_f = today_forecast.get('mintempF', 'N/A')
+        min_temp_c = today_forecast.get('mintempC', 'N/A')
+        avg_temp_f = today_forecast.get('avgtempF', 'N/A')
+        avg_temp_c = today_forecast.get('avgtempC', 'N/A')
+
+        report += f"High: {max_temp_f}°F ({max_temp_c}°C)\n"
+        report += f"Low: {min_temp_f}°F ({min_temp_c}°C)\n"
+        report += f"Average: {avg_temp_f}°F ({avg_temp_c}°C)\n"
+
+        # Add hourly breakdown if available
+        if 'hourly' in today_forecast and len(today_forecast['hourly']) > 0:
+            report += "\nHOURLY HIGHLIGHTS:\n"
+            report += "-" * 18 + "\n"
+
+            # Show a few key hours (morning, noon, evening)
+            hours_to_show = [3, 6, 9]  # indices for 9AM, 12PM, 6PM approximately
+            for idx in hours_to_show:
+                if idx < len(today_forecast['hourly']):
+                    hour_data = today_forecast['hourly'][idx]
+                    time = hour_data.get('time', 'N/A')
+                    if time != 'N/A' and len(time) >= 2:
+                        # Convert time (e.g., "900" to "9:00 AM")
+                        hour = int(time[:2]) if len(time) >= 2 else 0
+                        period = "AM" if hour < 12 else "PM"
+                        display_hour = hour if hour <= 12 else hour - 12
+                        if display_hour == 0:
+                            display_hour = 12
+                        time_str = f"{display_hour}:00 {period}"
+                    else:
+                        time_str = time
+
+                    h_temp_f = hour_data.get('tempF', 'N/A')
+                    h_desc = hour_data.get('weatherDesc', [{}])[0].get('value', 'N/A')
+                    h_precip = hour_data.get('precipMM', 'N/A')
+
+                    report += f"{time_str}: {h_temp_f}°F - {h_desc}"
+                    if h_precip != 'N/A' and float(h_precip) > 0:
+                        report += f" (Precip: {h_precip}mm)"
+                    report += "\n"
+
+    report += "\n" + "=" * 60 + "\n"
+
+    return report
+
+
+# --- Example Usage ---
+if __name__ == '__main__':
+    print("Testing Weather Agent for Del Mar, California\n")
+    weather_report = get_weather_delmar()
+    print(weather_report)
+

config.yaml

@@ -1,4 +1,4 @@
-# Configuration for finance_agents Project
+# Configuration for AI Agent Project
 
 # --- Data Paths ---
 data:
@@ -7,6 +7,15 @@ data:
   # Path to store processed data (if Agent 1 saves output)
   processed_data_path: "data/processed/"
 
+# --- Weather Agent Configuration ---
+weather:
+  # Location for weather queries
+  location: "Del Mar, California"
+  # API endpoint (using free wttr.in service)
+  api_endpoint: "https://wttr.in"
+  # Request timeout in seconds
+  timeout: 10
+
 # --- LLM Configuration ---
 llm:
   # Model identifier for local Ollama execution
@@ -22,6 +31,11 @@ agents:
     timeout_seconds: 30       # From your safety requirements
     memory_limit_mb: 512      # From your safety requirements
 
+  # Settings specific to Weather Agent
+  weather_agent:
+    location: "Del Mar, California"
+    update_interval_minutes: 30
+
   # Settings specific to Agent 1: Data Loader & Preparer
   data_loader:
     validate_dates: True      # Example setting for data validation

main.py

@@ -1,9 +1,8 @@
-# main.py - Main entry point for the finance agents application
+# main.py - Main entry point for the AI Agent Project
 
 import sys
 import os
-import pandas as pd # Make sure pandas is imported if you handle the df here
-import time         # Optional: for timing execution
+import time
 
 # --- Ensure the 'agents' directory can be found ---
 # Add project root to the Python path to help with imports
@@ -15,10 +14,8 @@
 
 # --- Import functions from agent modules ---
 try:
-    from agents.data_loader import load_and_prepare_data
-    from agents.indicator_calculator import calculate_technical_indicators
-    from agents.pattern_identifier import analyze_patterns_and_report # Added Agent 3 import
-    print("Successfully imported agent functions.")
+    from agents.weather_agent import get_weather_delmar
+    print("Successfully imported weather agent function.")
 except ImportError as e:
     print(f"ERROR: Failed to import agent functions: {e}")
     print("Check file paths, function names, and ensure __init__.py files exist in subdirectories.")
@@ -31,59 +28,26 @@
 if __name__ == '__main__':
     start_time = time.time()
     print("="*50)
-    print("Starting Finance Analysis Pipeline via main.py...")
+    print("Starting Weather Agent for Del Mar, California...")
     print("="*50)
 
-    # Step 1: Load data using Agent 1
-    print("\nInvoking Agent 1: Data Loader...")
-    data_df = load_and_prepare_data() # Uses config.yaml by default
-
-    # Step 2: Calculate indicators using Agent 2
-    df_with_indicators = None # Initialize
-    if data_df is not None and not data_df.empty:
-        print("\nInvoking Agent 2: Indicator Calculator...")
-        df_with_indicators = calculate_technical_indicators(data_df.copy()) # Use copy
-
-        if df_with_indicators is not None and not df_with_indicators.empty:
-            # Basic check if indicators were added (can be more robust)
-            if 'SMA_5' not in df_with_indicators.columns or 'RSI_14' not in df_with_indicators.columns:
-                 print("Pipeline Warning: Agent 2 might not have added expected indicator columns (SMA_5, RSI_14).")
-                 # Decide whether to stop or continue
-            else:
-                 print("\n--- Agent 2 Output Check (DataFrame with Indicators - Tail) ---")
-                 print(df_with_indicators.tail())
-                 print("\nSuccessfully ran Agent 1 -> Agent 2 flow.")
-        else:
-            print("\nAgent 2: Indicator calculation failed or returned empty DataFrame.")
-            # df_with_indicators remains None
-    else:
-        print("\nAgent 1: Data loading failed. Workflow stopped before Agent 2.")
-        # df_with_indicators remains None
-
-    # Step 3: Analyze and Report using Agent 3
-    final_report = None # Initialize
-    if df_with_indicators is not None and not df_with_indicators.empty:
-        print("\nInvoking Agent 3: Pattern Identifier & Reporter...")
-        # Pass the DataFrame *with indicators* from Agent 2 to Agent 3
-        final_report = analyze_patterns_and_report(df_with_indicators) # Uses config.yaml by default
-
-        if final_report is None or final_report.startswith("ERROR:"):
-            print(f"\nAgent 3 failed or produced an error: {final_report}")
-            # Report is None or contains error string
-        else:
-            print("\n--- Agent 3 Final Report: ---")
-            print(final_report)
-            print("-----------------------------")
-            print("Successfully ran Agent 1 -> Agent 2 -> Agent 3 flow.")
+    # Get weather report for Del Mar, California
+    print("\nInvoking Weather Agent...")
+    weather_report = get_weather_delmar()
 
+    # Display the weather report
+    if weather_report and not weather_report.startswith("ERROR:"):
+        print("\n--- Weather Report ---")
+        print(weather_report)
+        print("Successfully retrieved weather information.")
     else:
-        print("\nSkipping Agent 3 because previous steps failed or produced no data.")
+        print(f"\nWeather Agent failed or produced an error: {weather_report}")
 
     # --- Output Final Summary ---
     print("\n" + "="*50)
-    print("Final Analysis Report Summary:")
-    print(final_report if final_report and not final_report.startswith("ERROR:") else "No valid report generated.")
+    print("Weather Report Summary:")
+    print(weather_report if weather_report and not weather_report.startswith("ERROR:") else "No valid weather report generated.")
     print("="*50)
     end_time = time.time()
-    print(f"Pipeline finished in {end_time - start_time:.2f} seconds.")
+    print(f"Weather agent finished in {end_time - start_time:.2f} seconds.")
     print("==================================================")