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import re
import json
import unicodedata
import io
import urllib.parse
import feedparser
import pandas as pd
from flask import current_app
from gradio_client import Client
from PIL import Image
import base64
class ContentService:
"""Service for AI content generation using Hugging Face models."""
def __init__(self, hugging_key=None):
# Store the hugging_key to be used later when needed
# This avoids accessing current_app during initialization
self.hugging_key = hugging_key
# Initialize the Gradio client lazily - only when first needed
self.client = None
def _initialize_client(self):
"""Initialize the Gradio client, either with provided key or from app config."""
if self.client is None:
# If hugging_key wasn't provided at initialization, try to get it now
if not self.hugging_key:
try:
self.hugging_key = current_app.config.get('HUGGING_KEY')
except RuntimeError:
# We're outside of an application context
raise RuntimeError("Hugging Face API key not provided and not available in app config. "
"Please provide the key when initializing ContentService.")
self.client = Client("Zelyanoth/Linkedin_poster_dev", hf_token=self.hugging_key)
def validate_unicode_content(self, content):
"""Validate Unicode content while preserving original formatting and spaces."""
if not content or not isinstance(content, str):
return content
try:
# Test if content can be encoded as UTF-8
content.encode('utf-8')
return content # Return original content if it's valid UTF-8
except UnicodeEncodeError:
try:
# If encoding fails, try to preserve as much as possible
return content.encode('utf-8', errors='replace').decode('utf-8')
except:
# Ultimate fallback
return str(content)
def preserve_formatting(self, content):
"""Preserve spaces, line breaks, and paragraph formatting."""
if not content:
return content
# Preserve all whitespace characters including spaces, tabs, and newlines
# This ensures that paragraph breaks and indentation are maintained
try:
# Test encoding first
content.encode('utf-8')
return content
except UnicodeEncodeError:
# Fallback with error replacement but preserve whitespace
return content.encode('utf-8', errors='replace').decode('utf-8')
def sanitize_content_for_api(self, content):
"""Sanitize content for API calls while preserving original text, spaces, and formatting."""
if not content:
return content
# First preserve formatting and spaces
preserved = self.preserve_formatting(content)
# Only validate Unicode, don't remove spaces or formatting
validated = self.validate_unicode_content(preserved)
# Only remove null bytes that might cause issues in API calls
if '\x00' in validated:
validated = validated.replace('\x00', '')
# Ensure line breaks and spaces are preserved
validated = validated.replace('\r\n', '\n').replace('\r', '\n')
return validated
def _is_base64_image(self, data):
"""Check if the data is a base64 encoded image string."""
if not isinstance(data, str):
return False
# Check if it starts with data URL prefix
if data.startswith('data:image/'):
return True
# Try to decode as base64
try:
# Extract base64 part if it's a data URL
if ',' in data:
base64_part = data.split(',')[1]
else:
base64_part = data
# Try to decode
base64.b64decode(base64_part, validate=True)
return True
except Exception:
return False
def _base64_to_bytes(self, base64_string):
"""Convert a base64 encoded string to bytes."""
try:
# If it's a data URL, extract the base64 part
if base64_string.startswith('data:image/'):
base64_part = base64_string.split(',')[1]
else:
base64_part = base64_string
# Decode base64 to bytes
return base64.b64decode(base64_part, validate=True)
except Exception as e:
current_app.logger.error(f"Failed to decode base64 image: {str(e)}")
raise Exception(f"Failed to decode base64 image: {str(e)}")
def generate_post_content(self, user_id: str) -> tuple:
"""
Generate post content using AI.
Args:
user_id (str): User ID for personalization
Returns:
tuple: (Generated post content, Image URL or None)
"""
try:
# Ensure the client is initialized (lazy initialization)
if self.client is None:
self._initialize_client()
# Call the Hugging Face model to generate content
result = self.client.predict(
code=user_id,
api_name="/poster_linkedin"
)
# Handle the case where result might be a tuple from Gradio
# The Gradio API returns a tuple with (content, image_data)
if isinstance(result, tuple) and len(result) >= 2:
generated_content = result[0] if result[0] is not None else "Generated content will appear here..."
image_data = result[1] if result[1] is not None else None
else:
# Parse the result (assuming it returns a list with content as first element)
# First try to parse as JSON
try:
parsed_result = json.loads(result)
except json.JSONDecodeError:
# If JSON parsing fails, check if it's already a Python list/object
try:
# Try to evaluate as Python literal (safe for lists/dicts)
import ast
parsed_result = ast.literal_eval(result)
except (ValueError, SyntaxError):
# If that fails, treat the result as a plain string
parsed_result = [result]
# Extract the first element if it's a list
if isinstance(parsed_result, list):
generated_content = parsed_result[0] if parsed_result and parsed_result[0] is not None else "Generated content will appear here..."
# Extract the second element as image URL if it exists
image_data = parsed_result[1] if len(parsed_result) > 1 and parsed_result[1] is not None else None
else:
generated_content = str(parsed_result) if parsed_result is not None else "Generated content will appear here..."
image_data = None
# Validate, sanitize, and preserve formatting of the generated content
sanitized_content = self.sanitize_content_for_api(generated_content)
# Ensure paragraph breaks and formatting are preserved
final_content = self.preserve_formatting(sanitized_content)
# Handle image data - could be URL or base64
image_bytes = None
if image_data:
if self._is_base64_image(image_data):
# Convert base64 to bytes for storage
image_bytes = self._base64_to_bytes(image_data)
else:
# It's a URL, keep as string
image_bytes = image_data
return (final_content, image_bytes)
except Exception as e:
error_message = str(e)
current_app.logger.error(f"Content generation failed: {error_message}")
raise Exception(f"Content generation failed: {error_message}")
def add_rss_source(self, rss_link: str, user_id: str) -> str:
"""
Add an RSS source for content generation.
Args:
rss_link (str): RSS feed URL
user_id (str): User ID
Returns:
str: Result message
"""
try:
# Ensure the client is initialized (lazy initialization)
if self.client is None:
self._initialize_client()
# Call the Hugging Face model to add RSS source
rss_input = f"{rss_link}__thi_irrh'èçs_my_id__! {user_id}"
sanitized_rss_input = self.sanitize_content_for_api(rss_input)
result = self.client.predict(
rss_link=sanitized_rss_input,
api_name="/ajouter_rss"
)
# Sanitize and preserve formatting of the result
sanitized_result = self.sanitize_content_for_api(result)
return self.preserve_formatting(sanitized_result)
except Exception as e:
raise Exception(f"Failed to add RSS source: {str(e)}")
def analyze_keyword_frequency(self, keyword, user_id, date_range='monthly'):
"""
Analyze the frequency of new articles/links appearing in RSS feeds generated from keywords.
Args:
keyword (str): The keyword to analyze
user_id (str): User ID for filtering content
date_range (str): The date range to analyze ('daily', 'weekly', 'monthly')
Returns:
dict: Analysis data with article frequency over time
"""
try:
from flask import current_app
from datetime import datetime, timedelta
import re
# Attempt to access current_app, but handle gracefully if outside of app context
try:
# Fetch posts from the database that belong to the user
# Check if Supabase client is initialized
if not hasattr(current_app, 'supabase') or current_app.supabase is None:
raise Exception("Database connection not initialized")
# Get all RSS sources for the user to analyze
rss_response = (
current_app.supabase
.table("Source")
.select("source, categorie, created_at")
.eq("user_id", user_id)
.execute()
)
user_rss_sources = rss_response.data if rss_response.data else []
# Analyze each RSS source for frequency of new articles/links
keyword_data = []
# Create a DataFrame to store articles from RSS feeds
all_articles = []
for rss_source in user_rss_sources:
rss_link = rss_source["source"]
# Check if the source is a keyword rather than an RSS URL
# If it's a keyword, generate a Google News RSS URL
if self._is_url(rss_link):
# It's a URL, use it directly
feed_url = rss_link
else:
# It's a keyword, generate Google News RSS URL
feed_url = self._generate_google_news_rss_from_string(rss_link)
# Parse the RSS feed
feed = feedparser.parse(feed_url)
# Log some debug information
current_app.logger.info(f"Processing RSS feed: {feed_url}")
current_app.logger.info(f"Number of entries in feed: {len(feed.entries)}")
# Extract articles from the feed
for entry in feed.entries:
# Use the same date handling as in the original ai_agent.py
article_data = {
'title': entry.title,
'link': entry.link,
'summary': entry.summary,
'date': entry.get('published', entry.get('updated', None)),
'content': entry.get('summary', '') + ' ' + entry.get('title', '')
}
# Log individual article data for debugging
current_app.logger.info(f"Article title: {entry.title}")
current_app.logger.info(f"Article date: {article_data['date']}")
all_articles.append(article_data)
# Create a DataFrame from the articles
df_articles = pd.DataFrame(all_articles)
current_app.logger.info(f"Total articles collected: {len(df_articles)}")
if not df_articles.empty:
current_app.logger.info(f"DataFrame columns: {df_articles.columns.tolist()}")
current_app.logger.info(f"Sample of DataFrame:\n{df_articles.head()}")
# Convert date column to datetime if it exists
if not df_articles.empty and 'date' in df_articles.columns:
# Convert struct_time objects to datetime
df_articles['date'] = pd.to_datetime(df_articles['date'], errors='coerce', utc=True)
current_app.logger.info(f"DataFrame shape after date conversion: {df_articles.shape}")
current_app.logger.info(f"Date column after conversion:\n{df_articles['date'].head()}")
df_articles = df_articles.dropna(subset=['date']) # Remove entries with invalid dates
df_articles = df_articles.sort_values(by='date', ascending=True)
current_app.logger.info(f"DataFrame shape after dropping invalid dates: {df_articles.shape}")
# If we have articles, analyze article frequency over time
if not df_articles.empty:
# Group by date ranges and count all articles (not just those containing the keyword)
# This will show how many new articles appear in RSS feeds over time
# For the date grouping, use the appropriate pandas syntax
# Handle timezone-aware dates properly to avoid warnings
if date_range == 'daily':
# Convert to date while preserving timezone info
df_articles['date_group'] = df_articles['date'].dt.tz_localize(None).dt.date # Get date portion only
interval = 'D' # Daily frequency
elif date_range == 'weekly':
# For weekly, get the start of the week (Monday)
# First remove timezone info for proper date arithmetic
tz_naive = df_articles['date'].dt.tz_localize(None) if df_articles['date'].dt.tz is not None else df_articles['date']
# Calculate the Monday of each week (0=Monday, 6=Sunday)
df_articles['date_group'] = (tz_naive - pd.to_timedelta(tz_naive.dt.dayofweek, unit='d')).dt.date
interval = 'W-MON' # Weekly frequency starting on Monday
else: # monthly
# For monthly, get the start of the month
# Create a new datetime with day=1 for the start of the month
df_articles['date_group'] = pd.to_datetime({
'year': df_articles['date'].dt.year,
'month': df_articles['date'].dt.month,
'day': 1
}).dt.date
interval = 'MS' # Month Start frequency
# Count all articles by date group (this is the key difference - we're counting all articles, not keyword matches)
article_counts = df_articles.groupby('date_group').size().reset_index(name='count')
# Create a complete date range for the chart
if not article_counts.empty:
start_date = article_counts['date_group'].min()
end_date = article_counts['date_group'].max()
# Use the correct frequency for the date range generation
if date_range == 'daily':
freq = 'D'
elif date_range == 'weekly':
freq = 'W-MON' # Weekly on Monday
else: # monthly
freq = 'MS' # Month start frequency
# Create a complete date range
full_date_range = pd.date_range(start=start_date, end=end_date, freq=freq).to_frame(index=False, name='date_group')
full_date_range['date_group'] = full_date_range['date_group'].dt.date
# Merge with article counts
article_counts = full_date_range.merge(article_counts, on='date_group', how='left').fillna(0)
# Convert counts to integers
article_counts['count'] = article_counts['count'].astype(int)
# Format the data for the frontend chart
for _, row in article_counts.iterrows():
date_str = row['date_group'].strftime('%Y-%m-%d')
# Calculate values for different time ranges
daily_val = row['count'] if date_range == 'daily' else int(row['count'] / 7) if date_range == 'weekly' else int(row['count'] / 30)
weekly_val = daily_val * 7 if date_range == 'daily' else row['count'] if date_range == 'weekly' else int(row['count'] / 4)
monthly_val = daily_val * 30 if date_range == 'daily' else weekly_val * 4 if date_range == 'weekly' else row['count']
keyword_data.append({
'date': date_str,
'daily': daily_val,
'weekly': weekly_val,
'monthly': monthly_val
})
else:
# If no articles found, create empty data for the last 6 periods
start_date = datetime.now()
for i in range(6):
if date_range == 'daily':
date = (start_date - timedelta(days=i)).strftime('%Y-%m-%d')
elif date_range == 'weekly':
date = (start_date - timedelta(weeks=i)).strftime('%Y-%m-%d')
else: # monthly
date = (start_date - timedelta(days=30*i)).strftime('%Y-%m-%d')
keyword_data.append({
'date': date,
'daily': 0,
'weekly': 0,
'monthly': 0
})
else:
# If no RSS sources or articles, create empty data for the last 6 periods
start_date = datetime.now()
for i in range(6):
if date_range == 'daily':
date = (start_date - timedelta(days=i)).strftime('%Y-%m-%d')
elif date_range == 'weekly':
date = (start_date - timedelta(weeks=i)).strftime('%Y-%m-%d')
else: # monthly
date = (start_date - timedelta(days=30*i)).strftime('%Y-%m-%d')
keyword_data.append({
'date': date,
'daily': 0,
'weekly': 0,
'monthly': 0
})
return keyword_data
except RuntimeError:
# We're outside of application context
# Create mock data for testing purposes
# This is for testing scenarios where the full application context isn't available
start_date = datetime.now()
keyword_data = []
for i in range(6):
if date_range == 'daily':
date = (start_date - timedelta(days=i)).strftime('%Y-%m-%d')
elif date_range == 'weekly':
date = (start_date - timedelta(weeks=i)).strftime('%Y-%m-%d')
else: # monthly
date = (start_date - timedelta(days=30*i)).strftime('%Y-%m-%d')
keyword_data.append({
'date': date,
'daily': 0,
'weekly': 0,
'monthly': 0
})
return keyword_data
except Exception as e:
import logging
logging.error(f"Keyword frequency analysis failed: {str(e)}")
raise Exception(f"Keyword frequency analysis failed: {str(e)}")
def analyze_keyword_frequency_pattern(self, keyword, user_id):
"""
Analyze the frequency pattern of links generated from RSS feeds for a specific keyword over time.
Determines if the keyword follows a daily, weekly, monthly, or rare pattern based on recency and frequency.
Args:
keyword (str): The keyword to analyze
user_id (str): User ID for filtering content
Returns:
dict: Analysis data with frequency pattern classification
"""
try:
from flask import current_app
from datetime import datetime, timedelta
import re
# Create a DataFrame to store articles from RSS feeds
all_articles = []
# Attempt to access current_app, but handle gracefully if outside of app context
try:
# Fetch posts from the database that belong to the user
# Check if Supabase client is initialized
if not hasattr(current_app, 'supabase') or current_app.supabase is None:
raise Exception("Database connection not initialized")
# Get all RSS sources for the user to analyze
rss_response = (
current_app.supabase
.table("Source")
.select("source, categorie, created_at")
.eq("user_id", user_id)
.execute()
)
user_rss_sources = rss_response.data if rss_response.data else []
# Analyze each RSS source
# Check if the source matches the keyword or if it's any source
# We'll analyze any source that contains the keyword or is related to it
# Check if the source is a keyword rather than an RSS URL
# If it's a keyword, generate a Google News RSS URL
if self._is_url(keyword):
# It's a URL, use it directly
feed_url = keyword
else:
# It's a keyword, generate Google News RSS URL
feed_url = self._generate_google_news_rss_from_string(keyword)
# Parse the RSS feed
feed = feedparser.parse(feed_url)
# Log some debug information
current_app.logger.info(f"Processing RSS feed: {feed_url}")
current_app.logger.info(f"Number of entries in feed: {len(feed.entries)}")
# Extract ALL articles from the feed (without filtering by keyword again)
for entry in feed.entries:
# Use the same date handling as in the original ai_agent.py
article_data = {
'title': entry.title,
'link': entry.link,
'summary': entry.summary,
'date': entry.get('published', entry.get('updated', None)),
'content': entry.get('summary', '') + ' ' + entry.get('title', '')
}
# Log individual article data for debugging
current_app.logger.info(f"Article title: {entry.title}")
current_app.logger.info(f"Article date: {article_data['date']}")
all_articles.append(article_data)
# Create a DataFrame from the articles
df_articles = pd.DataFrame(all_articles)
current_app.logger.info(f"Total articles collected for keyword '{keyword}': {len(df_articles)}")
if not df_articles.empty:
current_app.logger.info(f"DataFrame columns: {df_articles.columns.tolist()}")
current_app.logger.info(f"Sample of DataFrame:\n{df_articles.head()}")
# Convert date column to datetime if it exists
if not df_articles.empty and 'date' in df_articles.columns:
# Convert struct_time objects to datetime
df_articles['date'] = pd.to_datetime(df_articles['date'], errors='coerce', utc=True)
current_app.logger.info(f"DataFrame shape after date conversion: {df_articles.shape}")
current_app.logger.info(f"Date column after conversion:\n{df_articles['date'].head()}")
df_articles = df_articles.dropna(subset=['date']) # Remove entries with invalid dates
df_articles = df_articles.sort_values(by='date', ascending=False) # Sort by date descending to get most recent first
current_app.logger.info(f"DataFrame shape after dropping invalid dates: {df_articles.shape}")
# Analyze frequency pattern
frequency_pattern = self._determine_frequency_pattern(df_articles)
# Prepare recent articles to return with the response
recent_articles = []
if not df_articles.empty:
# Get the 5 most recent articles
recent_df = df_articles.head(5)
for _, row in recent_df.iterrows():
# Try to format the date properly
formatted_date = None
if pd.notna(row['date']):
# Convert to string in a readable format
formatted_date = row['date'].strftime('%Y-%m-%d %H:%M:%S') if hasattr(row['date'], 'strftime') else str(row['date'])
recent_articles.append({
'title': row['title'],
'link': row['link'],
'date': formatted_date
})
# Return comprehensive analysis
return {
'keyword': keyword,
'pattern': frequency_pattern['pattern'],
'details': frequency_pattern['details'],
'total_articles': len(df_articles),
'articles': recent_articles,
'date_range': {
'start': df_articles['date'].max().strftime('%Y-%m-%d') if not df_articles.empty else None, # Most recent date first
'end': df_articles['date'].min().strftime('%Y-%m-%d') if not df_articles.empty else None # Earliest date last
}
}
except RuntimeError:
# We're outside of application context
# Return default analysis for testing purposes
return {
'keyword': keyword,
'pattern': 'rare',
'details': {
'explanation': 'Application context not available, returning default analysis',
'confidence': 0.0
},
'total_articles': 0,
'articles': [],
'date_range': {
'start': None,
'end': None
}
}
except Exception as e:
import logging
logging.error(f"Keyword frequency pattern analysis failed: {str(e)}")
raise Exception(f"Keyword frequency pattern analysis failed: {str(e)}")
def _determine_frequency_pattern(self, df_articles):
"""
Determine the frequency pattern based on the recency and frequency of articles.
Args:
df_articles: DataFrame with articles data including dates
Returns:
dict: Pattern classification and details
"""
if df_articles.empty or 'date' not in df_articles.columns:
return {
'pattern': 'rare',
'details': {
'explanation': 'No articles found',
'confidence': 1.0
}
}
# Calculate time since the latest article
latest_date = df_articles['date'].max()
current_time = pd.Timestamp.now(tz=latest_date.tz) if latest_date.tz else pd.Timestamp.now()
time_since_latest = (current_time - latest_date).days
# Calculate article frequency
total_articles = len(df_articles)
# Group articles by date to get daily counts
df_articles['date_only'] = df_articles['date'].dt.date
daily_counts = df_articles.groupby('date_only').size()
# Calculate metrics
avg_daily_frequency = daily_counts.mean() if len(daily_counts) > 0 else 0
recent_activity = daily_counts.tail(7).sum() # articles in last 7 days
# Determine pattern based on multiple factors
if total_articles == 0:
return {
'pattern': 'rare',
'details': {
'explanation': 'No articles found',
'confidence': 1.0
}
}
# Check if pattern is truly persistent by considering recency
if time_since_latest > 30:
# If no activity in the last month, it's likely not a daily/weekly pattern anymore
if total_articles > 0:
return {
'pattern': 'rare',
'details': {
'explanation': f'No recent activity in the last {time_since_latest} days, despite {total_articles} total articles',
'confidence': 0.9
}
}
# If there are many recent articles per day, it's likely daily
if recent_activity > 7 and time_since_latest <= 1:
return {
'pattern': 'daily',
'details': {
'explanation': f'Many articles per day ({recent_activity} in the last 7 days) and recent activity',
'confidence': 0.9
}
}
# If there are few articles per day but regular weekly activity
if 3 <= recent_activity <= 7 and time_since_latest <= 7:
return {
'pattern': 'weekly',
'details': {
'explanation': f'About {recent_activity} articles per week with recent activity',
'confidence': 0.8
}
}
# If there are very few articles but they are somewhat spread over time
if recent_activity < 3 and total_articles > 0 and time_since_latest <= 30:
return {
'pattern': 'monthly',
'details': {
'explanation': f'Few articles per month with recent activity in the last {time_since_latest} days',
'confidence': 0.7
}
}
# Default to rare if no clear pattern
return {
'pattern': 'rare',
'details': {
'explanation': f'Unclear pattern with {total_articles} total articles and last activity {time_since_latest} days ago',
'confidence': 0.5
}
}
def _is_url(self, s):
# Vérifie si c'est une URL valide
try:
from urllib.parse import urlparse
result = urlparse(s)
return all([result.scheme, result.netloc])
except:
return False
def _generate_google_news_rss_from_string(self, query, language="en", country="US"):
"""
Génère un lien RSS Google News à partir d'une chaîne de recherche brute.
Args:
query (str): Requête brute de recherche Google News.
language (str): Code langue, ex: "en".
country (str): Code pays, ex: "US".
Returns:
str: URL du flux RSS Google News.
"""
query_encoded = urllib.parse.quote(query)
url = (
f"https://news.google.com/rss/search?q={query_encoded}"
f"&hl={language}&gl={country}&ceid={country}:{language}"
)
return url |