Sets up a logger with a console handler and a rotating file handler. The console handler has color coding for different log levels, while th

Appends new data to an existing document in the MongoDB collection. Args: collection_name (str): The name of the MongoDB collection.

Connects to MongoDB and returns the database object. Uses environment variables for connection: MONGO_USERNAME: username for MongoDB

Fetches the content of a list of articles asynchronously, by checking if content already exists in the database, and if not, extracting the conte

Finds documents in the given MongoDB collection using the given query. Args: collection_name (str): The name of the MongoDB collecti

Fetches news articles from NewsAPI for the given query, from date and sort_by. Args: query (str): The query to search for in the New

Analyze the sentiment of a list of article IDs. Args: article_ids (List[str]): List of article IDs to analyze. Returns:

Clean a string by replacing carriage returns and newlines with spaces and then removing excessive spaces. Args: content (str): The s

Checks if the specified fields are present in the database for the given article_id. Args: article_id (str): The ID of the article t

Finds a single document in the given MongoDB collection using the given query. Args: collection_name (str): The name of the collecti

Generates a word cloud for the given list of keywords and sentiment label. Args: keywords (List[str]): List of keywords to include i

Inserts a document into the given collection. Args: collection_name (str): The name of the collection. document (dict): The

Process a list of articles by fetching content, summarizing, extracting keywords and analyzing sentiment. Args: query (str): The que

Summarizes a list of texts using a pre-trained Transformer model. Args: texts (List[str]): List of texts to summarize. max_l

Asynchronous wrapper for analyze_sentiments. Args: article_id (str): ID of the article to analyze. Returns: List[Dict[s

Creates a GIF from a list of PIL Image objects and displays it in Streamlit. Args: image_files (List[PIL.Image.Image]): List of PIL

Downloads a list of images from the given URLs and returns a list of PIL Image objects. Args: image_urls (List[str]): List of URLs o

Extracts and flattens a list of lists of keywords from a dataset. Args: data (pd.DataFrame): Pandas DataFrame containing a column na

Extracts keywords from a list of texts using KeyBERT. Args: texts (List[str]): List of texts to extract keywords from. top_n

Asynchronous wrapper for extract_keywords. Args: article_id (str): ID of the article to extract keywords from. Returns:

Fetches documents from the given MongoDB collection using the given IDs and combines them into a Pandas DataFrame. Args: collection_

Fetches the content of a single article asynchronously. Args: id (str): The ID of the article to fetch content for.

Fetches Reddit posts containing the given keyword. Args: keyword (str): The keyword to search for in Reddit posts. limit (in

Process a list of articles asynchronously, by fetching content, summarizing, extracting keywords and analyzing sentiment. Args: quer

Process a single article asynchronously, by fetching content, summarizing, extracting keywords and analyzing sentiment. Args: articl

Asynchronous wrapper for summarize_texts. Args: article_id (str): ID of the article to summarize. Returns: str: Summari

Tests the fetch_article_content function by fetching content for a list of article IDs. Args: article_ids (List[str]): A list of art

Extracts keywords from a list of texts using KeyBERT. Args: texts (List[str]): List of texts to extract keywords from. top_n

Classify the sentiment of multiple texts. Args: texts (List[str]): List of text to classify sentiment for. Returns: Dic

Generates a spiderweb chart using Streamlit's ECharts component. Args: data (dict): Dictionary where the keys are the topic names an

Loads a CSS file and injects it into the Streamlit app. Args: file_name (str): Path to the CSS file to load. Returns: N

Preprocesses a given text by tokenizing it and removing stopwords. Args: text (str): The text to preprocess. Returns: L