Updated open API Syntax

foodie_automator_google.py

@@ -226,10 +226,11 @@ def curate_from_google_trends():
         # Summarize the trend
         num_paragraphs = determine_paragraph_count(interest_score)
         extra_prompt = (
-            f"Generate exactly {num_paragraphs} paragraphs. "
+            f"Generate exactly {num_paragraphs} paragraphs.\n"
-            f"FOCUS: Summarize ONLY the provided content, explicitly mentioning '{title}' and sticking to its specific topic and details. "
+            f"FOCUS: Summarize ONLY the provided content, explicitly mentioning '{title}' and sticking to its specific topic and details.\n"
-            f"Do NOT introduce unrelated concepts. Expand on the core idea with relevant context about its appeal or significance in food trends."
+            f"Do NOT introduce unrelated concepts.\n"
-            "Do not include emojis in the summary."
+            f"Expand on the core idea with relevant context about its appeal or significance in food trends.\n"
+            f"Do not include emojis in the summary."
         )
         content_to_summarize = scoring_content
         final_summary = summarize_with_gpt4o(

foodie_automator_reddit.py

@@ -264,12 +264,12 @@ def curate_from_reddit():
         
         num_paragraphs = determine_paragraph_count(interest_score)
         extra_prompt = (
-            f"Generate exactly {num_paragraphs} paragraphs. "
+            f"Generate exactly {num_paragraphs} paragraphs.\n"
-            f"FOCUS: Summarize ONLY the provided content, explicitly mentioning '{title}' and sticking to its specific topic and details. "
+            f"FOCUS: Summarize ONLY the provided content, explicitly mentioning '{title}' and sticking to its specific topic and details.\n"
-            "Incorporate relevant insights from these top comments if available: {', '.join(top_comments) if top_comments else 'None'}. "
+            f"Incorporate relevant insights from these top comments if available: {', '.join(top_comments) if top_comments else 'None'}.\n"
-            "Do NOT introduce unrelated concepts unless in the content or comments. "
+            f"Do NOT introduce unrelated concepts unless in the content or comments.\n"
-            "If brief, expand on the core idea with relevant context about its appeal or significance. "
+            f"If brief, expand on the core idea with relevant context about its appeal or significance.\n"
-            "Do not include emojis in the summary."
+            f"Do not include emojis in the summary."
         )
         content_to_summarize = f"{title}\n\n{summary}"
         if top_comments:

foodie_automator_rss.py

@@ -266,10 +266,11 @@ def curate_from_rss():
 
         num_paragraphs = determine_paragraph_count(interest_score)
         extra_prompt = (
-            f"Generate exactly {num_paragraphs} paragraphs. "
+            f"Generate exactly {num_paragraphs} paragraphs.\n"
-            f"FOCUS: Summarize ONLY the provided content, explicitly mentioning '{title}' and sticking to its specific topic and details. "
+            f"FOCUS: Summarize ONLY the provided content, explicitly mentioning '{title}' and sticking to its specific topic and details.\n"
-            f"Do NOT introduce unrelated concepts. Expand on the core idea with relevant context about its appeal or significance."
+            f"Do NOT introduce unrelated concepts.\n"
-            "Do not include emojis in the summary."
+            f"Expand on the core idea with relevant context about its appeal or significance.\n"
+            f"Do not include emojis in the summary."
         )
         content_to_summarize = scoring_content
         final_summary = summarize_with_gpt4o(

foodie_engagement_tweet.py

@@ -1,14 +1,21 @@
 import random
 import logging
-from datetime import datetime, timedelta
+from datetime import datetime, timedelta, timezone
-import openai
+from openai import OpenAI  # Add this import
 from foodie_utils import post_tweet, AUTHORS, SUMMARY_MODEL
+from dotenv import load_dotenv  # Add this import
 
 # Setup logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
+# Load environment variables
+load_dotenv()
+
+# Initialize OpenAI client
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
 def generate_engagement_tweet(author):
-    author_handle = author["handle"]
+    author_handle = author["x_username"]  # Updated to use x_username from X_API_CREDENTIALS
     prompt = (
         f"Generate a concise tweet (under 280 characters) for {author_handle}. "
         f"Create an engaging food-related question or statement to spark interaction. "
@@ -18,7 +25,7 @@ def generate_engagement_tweet(author):
     )
     
     try:
-        response = openai.ChatCompletion.create(
+        response = client.chat.completions.create(
             model=SUMMARY_MODEL,
             messages=[
                 {"role": "system", "content": "You are a social media expert crafting engaging tweets."},
@@ -35,13 +42,13 @@ def generate_engagement_tweet(author):
         logging.warning(f"Failed to generate engagement tweet for {author['username']}: {e}")
         # Fallback templates
         engagement_templates = [
-            "Whats the most mouthwatering dish youve seen this week Share below and follow {handle} for more foodie ideas on InsiderFoodie.com Link: https://insiderfoodie.com",
+            f"Whats the most mouthwatering dish youve seen this week Share below and follow {author_handle} for more foodie ideas on InsiderFoodie.com Link: https://insiderfoodie.com",
-            "Food lovers unite Whats your go to comfort food Tell us and like this tweet for more tasty ideas from {handle} on InsiderFoodie.com Link: https://insiderfoodie.com",
+            f"Food lovers unite Whats your go to comfort food Tell us and like this tweet for more tasty ideas from {author_handle} on InsiderFoodie.com Link: https://insiderfoodie.com",
-            "Ever tried a dish that looked too good to eat Share your favorites and follow {handle} for more culinary trends on InsiderFoodie.com Link: https://insiderfoodie.com",
+            f"Ever tried a dish that looked too good to eat Share your favorites and follow {author_handle} for more culinary trends on InsiderFoodie.com Link: https://insiderfoodie.com",
-            "What food trend are you loving right now Let us know and like this tweet to keep up with {handle} on InsiderFoodie.com Link: https://insiderfoodie.com"
+            f"What food trend are you loving right now Let us know and like this tweet to keep up with {author_handle} on InsiderFoodie.com Link: https://insiderfoodie.com"
         ]
         template = random.choice(engagement_templates)
-        return template.format(handle=author_handle)
+        return template
 
 def post_engagement_tweet():
     # Reference date for calculating the 2-day interval

foodie_utils.py

@@ -127,7 +127,6 @@ def generate_article_tweet(author, post, persona):
     url = post["url"]
     author_handle = f"@{author['username']}"
     
-    # Base tweet content
     prompt = (
         f"Generate a concise tweet (under 280 characters) for {author_handle} using the persona '{persona}'. "
         f"Summarize the article '{title}' and include the link '{url}'. "
@@ -137,8 +136,8 @@ def generate_article_tweet(author, post, persona):
         f"Do not include hashtags or emojis."
     )
     
-    response = openai.ChatCompletion.create(
+    response = openai.chat.completions.create(
-        model=SUMMARY_MODEL,
+        model="gpt-4o",
         messages=[
             {"role": "system", "content": "You are a social media expert crafting engaging tweets."},
             {"role": "user", "content": prompt}
@@ -307,7 +306,7 @@ def smart_image_and_filter(title, summary):
             "for an image search about food industry trends or viral content. Prioritize specific terms if present, "
             "otherwise focus on the main theme. "
             "Return 'SKIP' if the article is about home appliances, recipes, promotions, or contains 'homemade', else 'KEEP'. "
-            "Return as JSON: {'image_query': 'specific term', 'relevance': ['keyword1', 'keyword2'], 'action': 'KEEP' or 'SKIP'}"
+            "Return as JSON with double quotes for all property names and string values (e.g., {\"image_query\": \"specific term\", \"relevance\": [\"keyword1\", \"keyword2\"], \"action\": \"KEEP\" or \"SKIP\"})."
         )
         
         response = client.chat.completions.create(
@@ -321,11 +320,15 @@ def smart_image_and_filter(title, summary):
         raw_result = response.choices[0].message.content.strip()
         logging.info(f"Raw GPT smart image/filter response: '{raw_result}'")
         
+        # Remove ```json markers and fix single quotes in JSON structure
         cleaned_result = re.sub(r'```json\s*|\s*```', '', raw_result).strip()
+        # Replace single quotes with double quotes, but preserve single quotes within string values
+        fixed_result = re.sub(r"(?<!\\)'(?=\s*[\w\s]*\])|(?<=\[|\{|\s)'|'(?=\s*[\]\},:])|(?<=\w)'(?=\s*:)", '"', cleaned_result)
+        
         try:
-            result = json.loads(cleaned_result)
+            result = json.loads(fixed_result)
         except json.JSONDecodeError as e:
-            logging.warning(f"JSON parsing failed: {e}, raw: '{cleaned_result}'. Using fallback.")
+            logging.warning(f"JSON parsing failed: {e}, raw: '{fixed_result}'. Using fallback.")
             return "food trends", ["cuisine", "dining"], False
         
         if not isinstance(result, dict) or "image_query" not in result or "relevance" not in result or "action" not in result:
@@ -468,6 +471,7 @@ def summarize_with_gpt4o(content, source_name, link, interest_score=0, extra_pro
         full_prompt = (
             f"{prompt}\n\n"
             f"{extra_prompt}\n\n"
+            f"Avoid using the word 'elevate'—use more humanized language like 'level up' or 'bring to life'.\n"
             f"Content to summarize:\n{content}\n\n"
             f"Source: {source_name}\n"
             f"Link: {link}"

foodie_weekly_thread.py

@@ -2,12 +2,15 @@ import json
 from datetime import datetime, timedelta
 import logging
 import random
-import openai
+from openai import OpenAI  # Add this import
 from foodie_utils import post_tweet, AUTHORS, SUMMARY_MODEL
 
 # Setup logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
+# Initialize OpenAI client
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
 RECENT_POSTS_FILE = "/home/shane/foodie_automator/recent_posts.json"
 
 def load_recent_posts():
@@ -45,7 +48,7 @@ def generate_intro_tweet(author):
     )
     
     try:
-        response = openai.ChatCompletion.create(
+        response = client.chat.completions.create(
             model=SUMMARY_MODEL,
             messages=[
                 {"role": "system", "content": "You are a social media expert crafting engaging tweets."},
@@ -82,7 +85,7 @@ def post_weekly_thread():
     # Group posts by author
     posts_by_author = {}
     for post in weekly_posts:
-        author = post["author"]
+        author = post["author_username"]  # Updated to match the key in recent_posts.json
         if author not in posts_by_author:
             posts_by_author[author] = []
         posts_by_author[author].append(post)
@@ -94,8 +97,8 @@ def post_weekly_thread():
             logging.info(f"No posts found for {author['username']} this week")
             continue
         
-        # Sort by interest score and take top 10
+        # Sort by timestamp (as a proxy for interest_score) and take top 10
-        author_posts.sort(key=lambda x: x.get("interest_score", 0), reverse=True)
+        author_posts.sort(key=lambda x: x.get("timestamp", ""), reverse=True)
         top_posts = author_posts[:10]
         
         if not top_posts:

requirements.txt

@@ -1,7 +1,7 @@
 requests==2.32.3
 selenium==4.29.0
 duckduckgo_search==7.5.4
-openai==1.35.3
+openai==1.75.0
 praw==7.8.1
 beautifulsoup4==4.13.3
 Pillow==11.1.0