import base64
import json
import logging
import os
import random
import re
from PIL import Image
import pytesseract
import io
import tempfile
import requests
import time
from datetime import datetime, timedelta
from openai import OpenAI
from urllib.parse import quote
from duckduckgo_search import DDGS
from bs4 import BeautifulSoup
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util.retry import Retry
from foodie_config import (
RECIPE_KEYWORDS, PROMO_KEYWORDS, HOME_KEYWORDS, PRODUCT_KEYWORDS, SUMMARY_PERSONA_PROMPTS,
get_clean_source_name, AUTHORS, LIGHT_TASK_MODEL, SUMMARY_MODEL
)
#test
client = OpenAI(api_key="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")
def load_json_file(filename, expiration_days=None):
data = []
if os.path.exists(filename):
try:
with open(filename, 'r') as f:
lines = f.readlines()
for i, line in enumerate(lines, 1):
if line.strip():
try:
entry = json.loads(line.strip())
if not isinstance(entry, dict) or "title" not in entry or "timestamp" not in entry:
logging.warning(f"Skipping malformed entry in {filename} at line {i}: {entry}")
continue
data.append(entry)
except json.JSONDecodeError as e:
logging.warning(f"Skipping invalid JSON line in {filename} at line {i}: {e}")
if expiration_days:
cutoff = (datetime.now() - timedelta(days=expiration_days)).isoformat()
data = [entry for entry in data if entry["timestamp"] > cutoff]
logging.info(f"Loaded {len(data)} entries from {filename}, {len(data)} valid after expiration check")
except Exception as e:
logging.error(f"Failed to load {filename}: {e}")
data = [] # Reset to empty on failure
return data
def save_json_file(filename, key, value):
entry = {"title": key, "timestamp": value}
PRUNE_INTERVAL_DAYS = 180
try:
data = load_json_file(filename, expiration_days=PRUNE_INTERVAL_DAYS)
# Remove duplicates by title
data = [item for item in data if item["title"] != key]
data.append(entry)
with open(filename, 'w') as f:
for item in data:
json.dump(item, f)
f.write('\n')
logging.info(f"Saved '{key}' to {filename}")
print(f"DEBUG: Saved '{key}' to {filename}")
loaded_data = load_json_file(filename, expiration_days=PRUNE_INTERVAL_DAYS)
logging.info(f"Pruned {filename} to {len(loaded_data)} entries (older than {PRUNE_INTERVAL_DAYS} days removed)")
except Exception as e:
logging.error(f"Failed to save or prune {filename}: {e}")
def select_best_persona(interest_score, content=""):
logging.info("Using select_best_persona with interest_score and content")
personas = ["Visionary Editor", "Foodie Critic", "Trend Scout", "Culture Connoisseur"]
content_lower = content.lower()
if any(kw in content_lower for kw in ["tech", "ai", "innovation", "sustainability"]):
return random.choice(["Trend Scout", "Visionary Editor"])
elif any(kw in content_lower for kw in ["review", "critic", "taste", "flavor"]):
return "Foodie Critic"
elif any(kw in content_lower for kw in ["culture", "tradition", "history"]):
return "Culture Connoisseur"
if interest_score >= 8:
return random.choice(personas[:2])
elif interest_score >= 6:
return random.choice(personas[2:])
return random.choice(personas)
def get_image(search_query):
api_key = "14836528-999c19a033d77d463113b1fb8"
base_url = "https://pixabay.com/api/"
queries = [search_query.split()[:2], search_query.split()]
for query in queries:
short_query = " ".join(query)
params = {
"key": api_key,
"q": short_query,
"image_type": "photo",
"safesearch": True,
"per_page": 20
}
try:
logging.info(f"Fetching Pixabay image for query '{short_query}'")
response = requests.get(base_url, params=params, timeout=10)
response.raise_for_status()
data = response.json()
if not data.get("hits"):
logging.warning(f"No image hits for query '{short_query}'")
continue
valid_images = [
hit for hit in data["hits"]
if all(tag not in hit.get("tags", "").lower() for tag in ["dog", "cat", "family", "child", "baby"])
]
if not valid_images:
logging.warning(f"No valid images for query '{short_query}' after filtering")
continue
image = random.choice(valid_images)
image_url = image["webformatURL"]
image_source = "Pixabay"
uploader = image.get("user", "Unknown")
pixabay_url = image["pageURL"]
logging.info(f"Fetched image URL: {image_url} by {uploader} for query '{short_query}'")
print(f"DEBUG: Image selected for query '{short_query}': {image_url}")
return image_url, image_source, uploader, pixabay_url
except requests.exceptions.RequestException as e:
logging.error(f"Image fetch failed for query '{short_query}': {e}")
continue
logging.error(f"All Pixabay image queries failed: {queries}")
return None, None, None, None
def generate_image_query(content):
try:
response = client.chat.completions.create(
model=LIGHT_TASK_MODEL,
messages=[
{"role": "system", "content": (
"From this content (title and summary), generate two sets of 2-3 concise keywords for an image search about restaurant/food industry trends:\n"
"1. Search keywords: For finding images (e.g., 'AI restaurant technology'). Focus on key themes like technology, sustainability, dining, or specific food concepts.\n"
"2. Relevance keywords: For filtering relevant images (e.g., 'ai tech dining'). Focus on core concepts to ensure match.\n"
"Avoid vague terms like 'trends', 'future', or unrelated words like 'dog', 'family'. "
"Return as JSON: {'search': 'keyword1 keyword2', 'relevance': 'keyword3 keyword4'}"
)},
{"role": "user", "content": content}
],
max_tokens=100
)
raw_result = response.choices[0].message.content.strip()
logging.info(f"Raw GPT image query response: '{raw_result}'")
print(f"DEBUG: Raw GPT image query response: '{raw_result}'")
cleaned_result = re.sub(r'```json\s*|\s*```', '', raw_result).strip()
result = json.loads(cleaned_result)
if not isinstance(result, dict) or "search" not in result or "relevance" not in result or len(result["search"].split()) < 2:
logging.warning(f"Invalid image query format: {result}, using fallback")
words = re.findall(r'\w+', content.lower())
filtered_words = [w for w in words if w not in RECIPE_KEYWORDS + PROMO_KEYWORDS + ['trends', 'future', 'dog', 'family']]
search = " ".join(filtered_words[:3]) or "restaurant innovation"
relevance = filtered_words[3:6] or ["dining", "tech"]
result = {"search": search, "relevance": " ".join(relevance)}
logging.info(f"Generated image query: {result}")
print(f"DEBUG: Image query from content: {result}")
return result["search"], result["relevance"].split()
except json.JSONDecodeError as e:
logging.error(f"JSON parsing failed for image query: {e}, raw response: '{raw_result}'")
words = re.findall(r'\w+', content.lower())
filtered_words = [w for w in words if w not in RECIPE_KEYWORDS + PROMO_KEYWORDS + ['trends', 'future', 'dog', 'family']]
search = " ".join(filtered_words[:3]) or "restaurant innovation"
relevance = filtered_words[3:6] or ["dining", "tech"]
logging.info(f"Fallback image query: {{'search': '{search}', 'relevance': '{' '.join(relevance)}'}}")
return search, relevance
except Exception as e:
logging.error(f"Image query generation failed: {e}")
print(f"Image Query Error: {e}")
return None, None
def smart_image_and_filter(title, summary):
try:
content = f"{title}\n\n{summary}"
prompt = (
"Analyze this article title and summary. Extract key entities (brands, locations, cuisines, or topics) "
"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'}"
)
response = client.chat.completions.create(
model=LIGHT_TASK_MODEL,
messages=[
{"role": "system", "content": prompt},
{"role": "user", "content": content}
],
max_tokens=100
)
raw_result = response.choices[0].message.content.strip()
logging.info(f"Raw GPT smart image/filter response: '{raw_result}'")
# Clean and parse JSON
cleaned_result = re.sub(r'```json\s*|\s*```', '', raw_result).strip()
try:
result = json.loads(cleaned_result)
except json.JSONDecodeError as e:
logging.warning(f"JSON parsing failed: {e}, raw: '{cleaned_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:
logging.warning(f"Invalid GPT response format: {result}, using fallback")
return "food trends", ["cuisine", "dining"], False
image_query = result["image_query"]
relevance_keywords = result["relevance"]
skip_flag = result["action"] == "SKIP" or "homemade" in title.lower() or "homemade" in summary.lower()
logging.info(f"Smart image query: {image_query}, Relevance: {relevance_keywords}, Skip: {skip_flag}")
if not image_query or len(image_query.split()) < 2:
logging.warning(f"Image query '{image_query}' too vague, using fallback")
return "food trends", ["cuisine", "dining"], skip_flag
return image_query, relevance_keywords, skip_flag
except Exception as e:
logging.error(f"Smart image/filter failed: {e}, using fallback")
return "food trends", ["cuisine", "dining"], False
def upload_image_to_wp(image_url, post_title, wp_base_url, wp_username, wp_password, image_source="Pixabay", uploader=None, pixabay_url=None):
try:
safe_title = post_title.encode('ascii', 'ignore').decode('ascii').replace(' ', '_')[:50]
headers = {
"Authorization": f"Basic {base64.b64encode(f'{wp_username}:{wp_password}'.encode()).decode()}",
"Content-Disposition": f"attachment; filename={safe_title}.jpg",
"Content-Type": "image/jpeg"
}
image_headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
}
logging.info(f"Fetching image from {image_url} for '{post_title}'")
image_response = requests.get(image_url, headers=image_headers, timeout=10)
image_response.raise_for_status()
response = requests.post(
f"{wp_base_url}/media",
headers=headers,
data=image_response.content
)
response.raise_for_status()
image_id = response.json()["id"]
caption = f'{image_source} by {uploader}' if pixabay_url and uploader else image_source
requests.post(
f"{wp_base_url}/media/{image_id}",
headers={"Authorization": headers["Authorization"], "Content-Type": "application/json"},
json={"caption": caption}
)
logging.info(f"Uploaded image '{safe_title}.jpg' to WP (ID: {image_id}) with caption '{caption}'")
return image_id
except Exception as e:
logging.error(f"Image upload to WP failed for '{post_title}': {e}")
return None
def determine_paragraph_count(interest_score):
if interest_score >= 9:
return 5
elif interest_score >= 7:
return 4
return 3
def is_interesting(summary):
try:
response = client.chat.completions.create(
model=LIGHT_TASK_MODEL,
messages=[
{"role": "system", "content": (
"Rate this content from 0-10 based on its rarity, buzzworthiness, and engagement potential for food lovers, covering a wide range of food topics (skip recipes). "
"Score 8-10 for rare, highly shareable ideas that grab attention. "
"Score 5-7 for fresh, engaging updates with broad appeal. Score below 5 for common or unremarkable content. "
"Return only a number."
)},
{"role": "user", "content": f"Content: {summary}"}
],
max_tokens=5
)
raw_score = response.choices[0].message.content.strip()
score = int(raw_score) if raw_score.isdigit() else 0
print(f"Interest Score for '{summary[:50]}...': {score} (raw: {raw_score})")
logging.info(f"Interest Score: {score} (raw: {raw_score})")
return score
except Exception as e:
logging.error(f"Interestingness scoring failed: {e}")
print(f"Interest Error: {e}")
return 0
def generate_title_from_summary(summary):
banned_words = ["elevate", "elevating", "elevated"]
for attempt in range(3):
try:
response = client.chat.completions.create(
model=LIGHT_TASK_MODEL,
messages=[
{"role": "system", "content": (
"Generate a concise, engaging title (under 100 characters) based on this summary, covering food topics. "
"Craft it with Upworthy/Buzzfeed flair—think ‘you won’t believe this’ or ‘this is nuts’—for food insiders. "
"Avoid quotes, emojis, special characters, or the words 'elevate', 'elevating', 'elevated'. "
"End with a question to spark shares."
)},
{"role": "user", "content": f"Summary: {summary}"}
],
max_tokens=30
)
title = response.choices[0].message.content.strip().replace('"', '').replace("'", "")
if ':' in title:
title = title.split(':', 1)[1].strip()
if len(title) > 100 or any(word in title.lower() for word in banned_words):
reason = "length" if len(title) > 100 else "banned word"
print(f"Rejected title (attempt {attempt + 1}/3): '{title}' due to {reason}")
logging.info(f"Rejected title (attempt {attempt + 1}/3): '{title}' due to {reason}")
continue
logging.info(f"Generated title: {title}")
return title
except Exception as e:
logging.error(f"Title generation failed (attempt {attempt + 1}/3): {e}")
print(f"Title Error: {e}")
print("Failed to generate valid title after 3 attempts")
logging.info("Failed to generate valid title after 3 attempts")
return None
def summarize_with_gpt4o(content, source_name, link, interest_score=0, extra_prompt=""):
try:
persona = select_best_persona(interest_score, content)
prompt = SUMMARY_PERSONA_PROMPTS.get(persona, "Write a concise, engaging summary that captures the essence of the content for food lovers.")
logging.info(f"Using {persona} with interest_score and content")
full_prompt = (
f"{prompt}\n\n"
f"{extra_prompt}\n\n"
f"Content to summarize:\n{content}\n\n"
f"Source: {source_name}\n"
f"Link: {link}"
)
response = client.chat.completions.create(
model=SUMMARY_MODEL,
messages=[
{"role": "system", "content": full_prompt},
{"role": "user", "content": content}
],
max_tokens=1000,
temperature=0.7
)
summary = response.choices[0].message.content.strip()
logging.info(f"Processed summary (Persona: {persona}): {summary}")
return summary
except Exception as e:
logging.error(f"Summary generation failed with model {SUMMARY_MODEL}: {e}")
return None
def smart_image_and_filter(title, summary):
try:
content = f"{title}\n\n{summary}"
prompt = (
'Analyze this article title and summary. Extract key entities (brands, locations, cuisines, or topics) '
'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 with double quotes: {"image_query": "specific term", "relevance": ["keyword1", "keyword2"], "action": "KEEP" or "SKIP"}'
)
response = client.chat.completions.create(
model=LIGHT_TASK_MODEL,
messages=[
{"role": "system", "content": prompt},
{"role": "user", "content": content}
],
max_tokens=100
)
raw_result = response.choices[0].message.content.strip()
logging.info(f"Raw GPT smart image/filter response: '{raw_result}'")
cleaned_result = re.sub(r'```json\s*|\s*```', '', raw_result).strip()
try:
result = json.loads(cleaned_result)
except json.JSONDecodeError as e:
logging.warning(f"JSON parsing failed: {e}, raw: '{cleaned_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:
logging.warning(f"Invalid GPT response format: {result}, using fallback")
return "food trends", ["cuisine", "dining"], False
image_query = result["image_query"]
relevance_keywords = result["relevance"]
skip_flag = result["action"] == "SKIP" or "homemade" in title.lower() or "homemade" in summary.lower()
logging.info(f"Smart image query: {image_query}, Relevance: {relevance_keywords}, Skip: {skip_flag}")
if not image_query or len(image_query.split()) < 2:
logging.warning(f"Image query '{image_query}' too vague, using fallback")
return "food trends", ["cuisine", "dining"], skip_flag
return image_query, relevance_keywords, skip_flag
except Exception as e:
logging.error(f"Smart image/filter failed: {e}, using fallback")
return "food trends", ["cuisine", "dining"], False
def is_interesting(summary):
try:
response = client.chat.completions.create(
model=LIGHT_TASK_MODEL,
messages=[
{"role": "system", "content": (
"Rate this content from 0-10 based on its rarity, buzzworthiness, and engagement potential for food lovers, covering a wide range of food topics (skip recipes). "
"Score 8-10 for rare, highly shareable ideas that grab attention. "
"Score 5-7 for fresh, engaging updates with broad appeal. Score below 5 for common or unremarkable content. "
"Return only a number."
)},
{"role": "user", "content": f"Content: {summary}"}
],
max_tokens=5
)
raw_score = response.choices[0].message.content.strip()
score = int(raw_score) if raw_score.isdigit() else 0
print(f"Interest Score for '{summary[:50]}...': {score} (raw: {raw_score})")
logging.info(f"Interest Score: {score} (raw: {raw_score})")
return score
except Exception as e:
logging.error(f"Interestingness scoring failed with model {LIGHT_TASK_MODEL}: {e}")
print(f"Interest Error: {e}")
return 0
def select_paragraphs(paragraphs, target_count, persona, original_content):
"""Select or generate paragraphs to match target_count, preserving key content."""
if len(paragraphs) == target_count and all(60 <= len(p.split()) <= 80 for p in paragraphs):
return paragraphs
# Score paragraphs by food-related keywords
keywords = ["food", "dish", "trend", "menu", "cuisine", "flavor", "taste", "eat", "dining", "restaurant"]
scores = []
for para in paragraphs:
score = sum(para.lower().count(kw) for kw in keywords)
word_count = len(para.split())
# Penalize paragraphs outside word range
score -= abs(word_count - 70) # Favor ~70 words
scores.append(score)
# Handle too many paragraphs
if len(paragraphs) > target_count:
# Keep last paragraph unless it's low-scoring
if scores[-1] >= min(scores[:-1]) or len(paragraphs) == target_count + 1:
selected_indices = sorted(range(len(paragraphs)-1), key=lambda i: scores[i], reverse=True)[:target_count-1] + [len(paragraphs)-1]
else:
selected_indices = sorted(range(len(paragraphs)), key=lambda i: scores[i], reverse=True)[:target_count]
selected = [paragraphs[i] for i in sorted(selected_indices)]
else:
selected = paragraphs[:]
# Handle word count adjustments or too few paragraphs
adjusted = []
for para in selected:
word_count = len(para.split())
if word_count < 60 or word_count > 80:
# Rephrase to fit 60-80 words
rephrase_prompt = (
f"Rephrase this paragraph to exactly 60-80 words, keeping the same tone as a {persona} and all key ideas: '{para}'"
)
try:
response = client.chat.completions.create(
model=SUMMARY_MODEL,
messages=[
{"role": "system", "content": rephrase_prompt},
{"role": "user", "content": para}
],
max_tokens=150,
temperature=0.7
)
new_para = response.choices[0].message.content.strip()
if 60 <= len(new_para.split()) <= 80:
adjusted.append(new_para)
else:
adjusted.append(para) # Fallback to original if rephrase fails
except Exception as e:
logging.warning(f"Rephrasing failed for paragraph: {e}")
adjusted.append(para)
else:
adjusted.append(para)
# Generate additional paragraphs if needed
while len(adjusted) < target_count:
extra_prompt = (
f"Generate one additional paragraph (60-80 words) in the style of a {persona}, "
f"based on this content: '{original_content[:200]}...'. Match the tone of: '{adjusted[-1] if adjusted else 'This trend is fire!'}'"
)
try:
response = client.chat.completions.create(
model=SUMMARY_MODEL,
messages=[
{"role": "system", "content": extra_prompt},
{"role": "user", "content": original_content}
],
max_tokens=150,
temperature=0.7
)
new_para = response.choices[0].message.content.strip()
if 60 <= len(new_para.split()) <= 80:
adjusted.append(new_para)
else:
adjusted.append("This trend is sparking buzz across menus!") # Fallback
except Exception as e:
logging.warning(f"Extra paragraph generation failed: {e}")
adjusted.append("This vibe is shaking up the food scene!")
return adjusted[:target_count]
def insert_link_naturally(summary, source_name, source_url):
import re
try:
prompt = (
"Take this summary and insert a single HTML link naturally into one paragraph (randomly chosen). "
"Use the format '{source_name}' and weave it into the text seamlessly, "
"e.g., 'The latest scoop from {source_name} reveals...' or '{source_name} uncovers this wild shift.' "
"Vary the phrasing creatively to avoid repetition (don’t always use 'dives into'). "
"Place the link at a sentence boundary (after a period, not within numbers like '6.30am' or '1.5'). "
"Maintain the original tone and flow, ensuring the link reads as part of the sentence, not standalone. "
"Return the modified summary with exactly one link, no extra formatting or newlines beyond the original.\n\n"
"Summary:\n{summary}\n\n"
"Source Name: {source_name}\nSource URL: {source_url}"
).format(summary=summary, source_name=source_name, source_url=source_url)
response = client.chat.completions.create(
model=LIGHT_TASK_MODEL,
messages=[
{"role": "system", "content": prompt},
{"role": "user", "content": "Insert the link naturally into the summary."}
],
max_tokens=1000,
temperature=0.7
)
new_summary = response.choices[0].message.content.strip()
link_pattern = f'{source_name}'
if new_summary and new_summary.count(link_pattern) == 1:
logging.info(f"Summary with naturally embedded link: {new_summary}")
return new_summary
logging.warning(f"GPT failed to insert link correctly: {new_summary}. Using fallback.")
except Exception as e:
logging.error(f"Link insertion failed: {e}")
# Fallback: Protect times and insert at sentence end
time_pattern = r'\b\d{1,2}\.\d{2}(?:am|pm)\b' # Matches 6.30am, 12.15pm
protected_summary = re.sub(time_pattern, lambda m: m.group(0).replace('.', '@'), summary)
paragraphs = protected_summary.split('\n')
if not paragraphs or all(not p.strip() for p in paragraphs):
logging.error("No valid paragraphs to insert link.")
return summary
target_para = random.choice([p for p in paragraphs if p.strip()])
phrases = [
f"The scoop from {link_pattern} spills the details",
f"{link_pattern} uncovers this wild shift",
f"This gem via {link_pattern} drops some truth",
f"{link_pattern} breaks down the buzz"
]
insertion_phrase = random.choice(phrases)
# Find sentence boundary, avoiding protected times
sentences = re.split(r'(?<=[.!?])\s+', target_para)
insertion_point = -1
for i, sent in enumerate(sentences):
if sent.strip() and '@' not in sent: # Avoid sentences with protected times
insertion_point = sum(len(s) + 1 for s in sentences[:i+1])
break
if insertion_point == -1:
insertion_point = len(target_para) # Append if no good boundary
# Add space after insertion phrase
new_para = f"{target_para[:insertion_point]} {insertion_phrase}. {target_para[insertion_point:]}".strip()
paragraphs[paragraphs.index(target_para)] = new_para
new_summary = '\n'.join(paragraphs)
# Restore periods in times
new_summary = new_summary.replace('@', '.')
logging.info(f"Fallback summary with link: {new_summary}")
return new_summary
def generate_category_from_summary(summary):
try:
if not isinstance(summary, str) or not summary.strip():
logging.warning(f"Invalid summary for category generation: {summary}. Defaulting to 'Trends'.")
return "Trends"
response = client.chat.completions.create(
model=LIGHT_TASK_MODEL,
messages=[
{"role": "system", "content": (
"Based on this summary, select the most relevant category from: Food, Culture, Trends, Health, Lifestyle, Drink, Eats. "
"Return only the category name."
)},
{"role": "user", "content": summary}
],
max_tokens=10
)
category = response.choices[0].message.content.strip()
logging.info(f"Generated category: {category}")
return category if category in ["Food", "Culture", "Trends", "Health", "Lifestyle", "Drink", "Eats"] else "Trends"
except Exception as e:
logging.error(f"Category generation failed: {e}")
return "Trends"
def get_wp_category_id(category_name, wp_base_url, wp_username, wp_password):
try:
headers = {"Authorization": f"Basic {base64.b64encode(f'{wp_username}:{wp_password}'.encode()).decode()}"}
response = requests.get(f"{wp_base_url}/categories", headers=headers, params={"search": category_name})
response.raise_for_status()
categories = response.json()
for cat in categories:
if cat["name"].lower() == category_name.lower():
return cat["id"]
return None
except Exception as e:
logging.error(f"Failed to get WP category ID for '{category_name}': {e}")
return None
def create_wp_category(category_name, wp_base_url, wp_username, wp_password):
try:
headers = {
"Authorization": f"Basic {base64.b64encode(f'{wp_username}:{wp_password}'.encode()).decode()}",
"Content-Type": "application/json"
}
payload = {"name": category_name}
response = requests.post(f"{wp_base_url}/categories", headers=headers, json=payload)
response.raise_for_status()
return response.json()["id"]
except Exception as e:
logging.error(f"Failed to create WP category '{category_name}': {e}")
return None
def get_wp_tag_id(tag_name, wp_base_url, wp_username, wp_password):
try:
headers = {"Authorization": f"Basic {base64.b64encode(f'{wp_username}:{wp_password}'.encode()).decode()}"}
response = requests.get(f"{wp_base_url}/tags", headers=headers, params={"search": tag_name})
response.raise_for_status()
tags = response.json()
for tag in tags:
if tag["name"].lower() == tag_name.lower():
return tag["id"]
return None
except Exception as e:
logging.error(f"Failed to get WP tag ID for '{tag_name}': {e}")
return None
def post_to_wp(post_data, category, link, author, image_url, original_source, image_source="Pixabay", uploader=None, pixabay_url=None, interest_score=4, post_id=None):
wp_base_url = "https://insiderfoodie.com/wp-json/wp/v2"
logging.info(f"Starting post_to_wp for '{post_data['title']}', image_source: {image_source}")
if not isinstance(author, dict) or "username" not in author or "password" not in author:
raise ValueError(f"Invalid author data: {author}. Expected a dictionary with 'username' and 'password' keys.")
wp_username = author["username"]
wp_password = author["password"]
if not isinstance(interest_score, int):
logging.error(f"Invalid interest_score type: {type(interest_score)}, value: '{interest_score}'. Defaulting to 4.")
interest_score = 4
elif interest_score < 0 or interest_score > 10:
logging.warning(f"interest_score out of valid range (0-10): {interest_score}. Clamping to 4.")
interest_score = min(max(interest_score, 0), 10)
try:
headers = {
"Authorization": f"Basic {base64.b64encode(f'{wp_username}:{wp_password}'.encode()).decode()}",
"Content-Type": "application/json"
}
auth_test = requests.get(f"{wp_base_url}/users/me", headers=headers)
auth_test.raise_for_status()
logging.info(f"Auth test passed for {wp_username}: {auth_test.json()['id']}")
category_id = get_wp_category_id(category, wp_base_url, wp_username, wp_password)
if not category_id:
category_id = create_wp_category(category, wp_base_url, wp_username, wp_password)
logging.info(f"Created new category '{category}' with ID {category_id}")
else:
logging.info(f"Found existing category '{category}' with ID {category_id}")
tags = [1]
if interest_score >= 9:
picks_tag_id = get_wp_tag_id("Picks", wp_base_url, wp_username, wp_password)
if picks_tag_id and picks_tag_id not in tags:
tags.append(picks_tag_id)
logging.info(f"Added 'Picks' tag (ID: {picks_tag_id}) to post due to high interest score: {interest_score}")
content = post_data["content"]
if content is None:
logging.error(f"Post content is None for title '{post_data['title']}' - using fallback")
content = "Content unavailable. Check the original source for details."
formatted_content = "\n".join(f"{para}
" for para in content.split('\n') if para.strip())
author_id_map = {
"shanehill": 5,
"javiermorales": 2,
"aishapatel": 3,
"liennguyen": 4,
"keishawashington": 6,
"lilamoreau": 7
}
author_id = author_id_map.get(author["username"], 5)
payload = {
"title": post_data["title"],
"content": formatted_content,
"status": "publish",
"categories": [category_id],
"tags": tags,
"author": author_id,
"meta": {
"original_link": link,
"original_source": original_source,
"interest_score": interest_score
}
}
if image_url and not post_id:
logging.info(f"Attempting image upload for '{post_data['title']}', URL: {image_url}, source: {image_source}")
image_id = upload_image_to_wp(image_url, post_data["title"], wp_base_url, wp_username, wp_password, image_source, uploader, pixabay_url)
if not image_id:
logging.info(f"Flickr upload failed for '{post_data['title']}', falling back to Pixabay")
pixabay_query = post_data["title"][:50]
image_url, image_source, uploader, pixabay_url = get_image(pixabay_query)
if image_url:
image_id = upload_image_to_wp(image_url, post_data["title"], wp_base_url, wp_username, wp_password, image_source, uploader, pixabay_url)
if image_id:
payload["featured_media"] = image_id
else:
logging.warning(f"All image uploads failed for '{post_data['title']}' - posting without image")
endpoint = f"{wp_base_url}/posts/{post_id}" if post_id else f"{wp_base_url}/posts"
method = requests.post # Use POST for both create and update (WP API handles it)
logging.debug(f"Sending WP request to {endpoint} with payload: {json.dumps(payload, indent=2)}")
response = method(endpoint, headers=headers, json=payload)
response.raise_for_status()
post_info = response.json()
logging.debug(f"WP response: {json.dumps(post_info, indent=2)}")
if not isinstance(post_info, dict) or "id" not in post_info:
raise ValueError(f"Invalid WP response: {post_info}")
post_id = post_info["id"]
post_url = post_info["link"]
logging.info(f"Posted/Updated by {author['username']}: {post_data['title']} (ID: {post_id})")
return post_id, post_url
except requests.exceptions.RequestException as e:
logging.error(f"WP API request failed: {e} - Response: {e.response.text if e.response else 'No response'}")
print(f"WP Error: {e}")
return None, None
except KeyError as e:
logging.error(f"WP payload error - Missing key: {e} - Author data: {author}")
print(f"WP Error: {e}")
return None, None
except Exception as e:
logging.error(f"WP posting failed: {e}")
print(f"WP Error: {e}")
return None, None
def get_flickr_image_via_ddg(search_query, relevance_keywords):
try:
with DDGS() as ddgs:
results = ddgs.images(
f"{search_query} flickr site:flickr.com -poster -infographic -chart -graph -data -stats -text -typography",
license_image="sharecommercially",
max_results=30
)
if not results:
logging.warning(f"No Flickr images found via DDG for query '{search_query}'")
return None, None, None, None
headers = {'User-Agent': 'InsiderFoodieBot/1.0 (https://insiderfoodie.com; contact@insiderfoodie.com)'}
candidates = []
for r in results:
image_url = r.get("image", "")
page_url = r.get("url", "")
if not image_url or "live.staticflickr.com" not in image_url:
continue
try:
response = requests.get(page_url, headers=headers, timeout=10)
response.raise_for_status()
soup = BeautifulSoup(response.content, 'html.parser')
tags_elem = soup.find_all('a', class_='tag')
tags = [tag.text.strip().lower() for tag in tags_elem] if tags_elem else []
title_elem = soup.find('h1', class_='photo-title')
title = title_elem.text.strip().lower() if title_elem else r.get("title", "").lower()
exclude_keywords = [
"poster", "infographic", "chart", "graph", "data", "stats", "text", "typography",
"design", "advertisement", "illustration", "diagram", "layout", "print"
]
matched_keywords = [kw for kw in exclude_keywords if kw in tags or kw in title]
if matched_keywords:
logging.info(f"Skipping text-heavy image: {image_url} (tags: {tags}, title: {title}, matched: {matched_keywords})")
continue
uploader = soup.find('a', class_='owner-name')
uploader = uploader.text.strip() if uploader else "Flickr User"
candidates.append({
"image_url": image_url,
"page_url": page_url,
"uploader": uploader,
"tags": tags,
"title": title
})
except requests.exceptions.RequestException as e:
logging.info(f"Skipping unavailable image: {image_url} (page: {page_url}, error: {e})")
continue
if not candidates:
logging.warning(f"No valid candidate images after filtering for '{search_query}'")
return None, None, None, None
result = random.choice(candidates)
image_url = result["image_url"]
# OCR check on the selected image
temp_file = None
try:
img_response = requests.get(image_url, headers=headers, timeout=10)
img_response.raise_for_status()
with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
temp_file.write(img_response.content)
temp_path = temp_file.name
img = Image.open(temp_path)
text = pytesseract.image_to_string(img)
char_count = len(text.strip())
logging.info(f"OCR processed {image_url}: {char_count} characters detected")
if char_count > 200:
logging.info(f"Skipping text-heavy image (OCR): {image_url} (char_count: {char_count})")
return None, None, None, None # Fall back to Pixabay
# Success: Save and return
flickr_data = {
"title": search_query,
"image_url": image_url,
"source": "Flickr",
"uploader": result["uploader"],
"page_url": result["page_url"],
"timestamp": datetime.now().isoformat(),
"ocr_chars": char_count
}
flickr_file = "/home/shane/foodie_automator/flickr_images.json"
with open(flickr_file, 'a') as f:
json.dump(flickr_data, f)
f.write('\n')
logging.info(f"Saved Flickr image to {flickr_file}: {image_url}")
logging.info(f"Fetched Flickr image URL: {image_url} by {result['uploader']} for query '{search_query}' (tags: {result['tags']})")
print(f"DEBUG: Flickr image selected: {image_url}")
return image_url, "Flickr", result["uploader"], result["page_url"]
except requests.exceptions.HTTPError as e:
if e.response.status_code == 429:
logging.warning(f"Rate limit hit for {image_url}. Falling back to Pixabay.")
return None, None, None, None
else:
logging.warning(f"Download failed for {image_url}: {e}")
return None, None, None, None
except Exception as e:
logging.warning(f"OCR processing failed for {image_url}: {e}")
return None, None, None, None
finally:
if temp_file and os.path.exists(temp_path):
os.unlink(temp_path)
except Exception as e:
logging.error(f"Flickr/DDG image fetch failed for '{search_query}': {e}")
return None, None, None, None
def select_best_author(summary):
try:
response = client.chat.completions.create(
model=LIGHT_TASK_MODEL,
messages=[
{"role": "system", "content": (
"Based on this restaurant/food industry trend summary, pick the most suitable author from: "
"shanehill, javiermorales, aishapatel, liennguyen, keishawashington, lilamoreau. "
"Consider their expertise: shanehill (global dining trends), javiermorales (food critique), "
"aishapatel (emerging food trends), liennguyen (cultural dining), keishawashington (soul food heritage), "
"lilamoreau (global street food). Return only the username."
)},
{"role": "user", "content": summary}
],
max_tokens=20
)
author = response.choices[0].message.content.strip()
valid_authors = ["shanehill", "javiermorales", "aishapatel", "liennguyen", "keishawashington", "lilamoreau"]
logging.info(f"Selected author: {author}")
return author if author in valid_authors else "shanehill"
except Exception as e:
logging.error(f"Author selection failed: {e}")
return "shanehill"
def prepare_post_data(final_summary, original_title, context_info=""):
innovative_title = generate_title_from_summary(final_summary)
if not innovative_title:
logging.info(f"Title generation failed for '{original_title}' {context_info}")
return None, None, None, None, None, None, None
# Note: This function still uses generate_image_query, but curate_from_rss overrides it with smart_image_and_filter
search_query, relevance_keywords = generate_image_query(f"{innovative_title}\n\n{final_summary}")
if not search_query:
logging.info(f"Image query generation failed for '{innovative_title}' {context_info}")
return None, None, None, None, None, None, None
logging.info(f"Fetching Flickr image for query: '{search_query}' {context_info}")
image_url, image_source, uploader, page_url = get_flickr_image_via_ddg(search_query, relevance_keywords)
if not image_url:
logging.info(f"Flickr fetch failed for '{search_query}' - falling back to Pixabay {context_info}")
image_query, _ = generate_image_query(f"{innovative_title}\n\n{final_summary}")
image_url, image_source, uploader, page_url = get_image(image_query)
if not image_url:
logging.info(f"Pixabay fetch failed for title '{innovative_title}' - falling back to summary {context_info}")
image_query, _ = generate_image_query(f"{final_summary}")
image_url, image_source, uploader, page_url = get_image(image_query)
if not image_url:
logging.info(f"Image fetch failed again for '{original_title}' - proceeding without image {context_info}")
post_data = {"title": innovative_title, "content": final_summary}
selected_username = select_best_author(final_summary)
author = next((a for a in AUTHORS if a["username"] == selected_username), None)
if not author:
logging.error(f"Author '{selected_username}' not found in AUTHORS, defaulting to shanehill")
author = {"username": "shanehill", "password": "LKfH JF0x CnnU SSxK s9f1 993x"}
category = generate_category_from_summary(final_summary)
return post_data, author, category, image_url, image_source, uploader, page_url