Formatted Weather to Pass Into AI

ai.py

@@ -2,31 +2,36 @@
 
 > pip install openai
 """
+# pylint: disable=C0301
 import os
 from openai import OpenAI
 
-# To authenticate with the model you will need to generate a personal access token (PAT) in your GitHub settings. 
-# Create your PAT token by following instructions here: https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens
-client = OpenAI(
-    base_url="https://models.inference.ai.azure.com",
-    api_key=os.getenv("GITHUB_TOKEN"),
-)
+def generate_ai_text(forecast):
+    """
+    Method that generates AI text
+    """
+    # To authenticate with the model you will need to generate a personal access token (PAT) in your GitHub settings.
+    # Create your PAT token by following instructions here: https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens
+    client = OpenAI(
+        base_url="https://models.inference.ai.azure.com",
+        api_key=os.getenv("OPENAI_API_KEY"),
+    )
 
-response = client.chat.completions.create(
-    messages=[
-        {
-            "role": "system",
-            "content": "You are a young meteorologist explaining the next day's forecast in Syracuse NY in the morning",
-        },
-        {
-            "role": "user",
-            "content": "Saturday, Temperature: 15, Wind Speed: 5 to 9 mph, windDirection: NW, detailedForecast: Partly sunny, with a high near 15. Wind chill values as low as -1. Northwest wind 5 to 9 mph. New snow accumulation of less than half an inch possible. ",
-        }
-    ],
-    model="gpt-4o",
-    temperature=1,
-    max_tokens=4096,
-    top_p=1
-)
+    response = client.chat.completions.create(
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a young meteorologist explaining the next three day's forecast in Syracuse NY in the morning(Say 'Good Morning Syracuse!'). Act like you're making a social media post. Make sure to Consildate the days into one description (don't do morning and night seperately)",
+            },
+            {
+                "role": "user",
+                "content": forecast,
+            }
+        ],
+        model="gpt-4o",
+        temperature=1,
+        max_tokens=4096,
+        top_p=1
+    )
 
-print(response.choices[0].message.content)
+    print(response.choices[0].message.content)

bluesky.py

@@ -1,14 +1,6 @@
 """
-The Module contains the Authentication methods for the following:
-
-BlueSkyAPI
-NWSAPI
-OpenAIAPI
-
-Dependencies:
-
-Author:
-- Andrew Markarian
+This module handles all interactions with the Bluesky API, including authentication, session management, 
+and posting formatted weather updates to the Bluesky social media platform.
 """
 
 import os

main.py

@@ -18,8 +18,12 @@
 
 import bluesky
 import weather
+import ai
 
 def main():
+    """
+    This runs the main module of BlueSkyTracker
+    """
     print("Loading Credentials...")
     bluesky_handle, bluesky_app_password = bluesky.load_bluesky_credentials()
 
@@ -29,7 +33,12 @@ def main():
 
     print("Fetching Forecast")
     forecast = weather.fetch_weather_forecast()
-    print(forecast)
-
+
+    print("Formatting Weather Data")
+    forecast = weather.format_weather_data(forecast)
+
+    print("Generating AI Text")
+    ai.generate_ai_text(forecast)
+
 if __name__ == "__main__":
-    main()
+    main()

tests/weather_test.py

@@ -41,9 +41,9 @@ def test_successful_api_call(self, mock_get):
         result = fetch_weather_forecast()
 
         self.assertIsNotNone(result)
-        self.assertEqual(len(result), 3)
+        self.assertEqual(len(result), 2)
         self.assertEqual(result["day_1"][0]["name"], "Monday")
-        self.assertEqual(result["day_3"][0]["name"], "Wednesday")
+        self.assertEqual(result["day_2"][0]["name"], "Tuesday")
 
     @patch("requests.get")
     def test_api_connection_error(self, mock_get):

weather.py

@@ -1,3 +1,9 @@
+"""
+This module handles all weather-related functionality within the project. It is responsible for 
+fetching weather forecast data from the National Weather Service (NWS) API, parsing and extracting 
+relevant information, and formatting it for use in other parts of the project.
+"""
+# pylint: disable=C0301
 import requests
 
 def fetch_weather_forecast():
@@ -9,15 +15,15 @@ def fetch_weather_forecast():
     try:
         url = "https://api.weather.gov/gridpoints/BGM/52,99/forecast"
 
-        response = requests.get(url)
+        response = requests.get(url, timeout=10)
         response.raise_for_status()
         data = response.json()
 
         forecast = data.get("properties", {}).get("periods", [])
 
         forecast_days = []
 
-        for i in range(6):
+        for i in range(4):
             period = forecast[i]
             forecast_days.append({
                 "name": period.get("name"),
@@ -28,16 +34,32 @@ def fetch_weather_forecast():
                 "windDirection": period.get("windDirection"),
                 "detailedForecast": period.get("detailedForecast")
             })
-        
-        # Group forecast into three-day structure
+
+        # Group forecast into two-day structure
         consolidated_forecast = {
             "day_1": forecast_days[:2],
             "day_2": forecast_days[2:4],
-            "day_3": forecast_days[4:6]
         }
 
         return consolidated_forecast
-    
+
     except requests.exceptions.RequestException as err:
         print("Error Fetching Weather Data: ", {err})
-        return None
+        return None
+
+def format_weather_data(weather_data):
+    """
+    This method is responsible for formatting the weather data retrieved from fetch_weather_data()
+    """
+    weather_input = ""
+    for forecast in weather_data.values():
+        weather_input += f"{forecast[0]['name']} & {forecast[1]['name']}:\n"
+        for period in forecast:
+            weather_input += (
+                f"Temperature: {period['temperature']}{period['temperatureUnit']}\n"
+                f"Probability Of Precipitation: {period['probabilityOfPrecipitation']}\n"
+                f"Wind Speed: {period['windSpeed']}, Direction {period['windDirection']}\n"
+                f"Detailed Forecast: {period['detailedForecast']}\n\n"
+            )
+    print(weather_input)
+    return weather_input