update tweet post every 2 days

foodie_engagement_tweet.py

@@ -1,8 +1,8 @@
 import random
 import logging
-from datetime import datetime
+from datetime import datetime, timedelta
 import openai
-from foodie_utils import post_tweet, AUTHORS
+from foodie_utils import post_tweet, AUTHORS, SUMMARY_MODEL
 
 # Setup logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -19,7 +19,7 @@ def generate_engagement_tweet(author):
     
     try:
         response = openai.ChatCompletion.create(
-            model="gpt-4o",
+            model=SUMMARY_MODEL,
             messages=[
                 {"role": "system", "content": "You are a social media expert crafting engaging tweets."},
                 {"role": "user", "content": prompt}
@@ -44,18 +44,26 @@ def generate_engagement_tweet(author):
         return template.format(handle=author_handle)
 
 def post_engagement_tweet():
-    for author in AUTHORS:
+    # Reference date for calculating the 2-day interval
-        tweet = generate_engagement_tweet(author)
+    reference_date = datetime(2025, 4, 29, tzinfo=timezone.utc)  # Starting from April 29, 2025
-        
+    current_date = datetime.now(timezone.utc)
-        logging.info(f"Posting engagement tweet for {author['username']}: {tweet}")
+    
-        if post_tweet(author, tweet):
+    # Calculate the number of days since the reference date
-            logging.info(f"Successfully posted engagement tweet for {author['username']}")
+    days_since_reference = (current_date - reference_date).days
-        else:
+    
-            logging.warning(f"Failed to post engagement tweet for {author['username']}")
+    # Post only if the number of days since the reference date is divisible by 2
+    if days_since_reference % 2 == 0:
+        logging.info("Today is an engagement tweet day (every 2 days). Posting...")
+        for author in AUTHORS:
+            tweet = generate_engagement_tweet(author)
+            
+            logging.info(f"Posting engagement tweet for {author['username']}: {tweet}")
+            if post_tweet(author, tweet):
+                logging.info(f"Successfully posted engagement tweet for {author['username']}")
+            else:
+                logging.warning(f"Failed to post engagement tweet for {author['username']}")
+    else:
+        logging.info("Today is not an engagement tweet day (every 2 days). Skipping...")
 
 if __name__ == "__main__":
-    # Run only on Mondays
+    post_engagement_tweet()
-    if datetime.now(timezone.utc).weekday() == 0:  # Monday (0 = Monday)
-        post_engagement_tweet()
-    else:
-        logging.info("Not Monday - skipping engagement tweet posting")

foodie_weekly_thread.py

@@ -3,7 +3,7 @@ from datetime import datetime, timedelta
 import logging
 import random
 import openai
-from foodie_utils import post_tweet, AUTHORS
+from foodie_utils import post_tweet, AUTHORS, SUMMARY_MODEL
 
 # Setup logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -46,7 +46,7 @@ def generate_intro_tweet(author):
     
     try:
         response = openai.ChatCompletion.create(
-            model="gpt-4o",
+            model=SUMMARY_MODEL,
             messages=[
                 {"role": "system", "content": "You are a social media expert crafting engaging tweets."},
                 {"role": "user", "content": prompt}

generate_backgrounds.py

@@ -1,55 +0,0 @@
-import json
-import logging
-from openai import OpenAI
-from foodie_config import OPENAI_API_KEY, AUTHORS, LIGHT_TASK_MODEL
-from datetime import datetime, timezone
-
-logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
-
-client = OpenAI(api_key=OPENAI_API_KEY)
-
-def generate_background(author):
-    # Include the DOB in the prompt to contextualize the timeline
-    dob = author.get('dob', '1980-01-01')  # Fallback DOB if not provided
-    current_year = datetime.now().year
-    birth_year = int(dob.split('-')[0])
-    age = current_year - birth_year  # Calculate approximate age
-
-    prompt = (
-        f"Generate a fictional background for a food writer named {author['username']} with the persona '{author['persona']}'. "
-        f"They were born on {dob} and are currently {age} years old. Use this to create a realistic timeline for their life events. "
-        "Include: hometown, cultural influences, an early food memory (from their childhood, appropriate for their birth year), "
-        "and how they became a food writer (considering their age and career timeline). "
-        f"Match the tone of their bio: '{author['bio']}'. Keep it concise (100-150 words). "
-        "Return as JSON: {'username': '...', 'hometown': '...', 'cultural_influences': '...', 'early_memory': '...', 'career_path': '...'}"
-    )
-    try:
-        response = client.chat.completions.create(
-            model=LIGHT_TASK_MODEL,
-            messages=[
-                {"role": "system", "content": prompt},
-                {"role": "user", "content": f"Generate background for {author['username']}."}
-            ],
-            max_tokens=200
-        )
-        raw_result = response.choices[0].message.content.strip()
-        cleaned_result = raw_result.replace('```json', '').replace('```', '').strip()
-        return json.loads(cleaned_result)
-    except Exception as e:
-        logging.error(f"Failed to generate background for {author['username']}: {e}")
-        return None
-
-def main():
-    backgrounds = []
-    for author in AUTHORS:
-        background = generate_background(author)
-        if background:
-            backgrounds.append(background)
-            logging.info(f"Generated background for {author['username']}")
-    
-    with open('/home/shane/foodie_automator/author_backgrounds.json', 'w') as f:
-        json.dump(backgrounds, f, indent=2)
-    logging.info("Saved backgrounds to author_backgrounds.json")
-
-if __name__ == "__main__":
-    main()