src/application/content_generation.py

import json
import os

import openai
from dotenv import load_dotenv

load_dotenv()
AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
AZURE_OPENAI_API_VERSION = os.getenv("AZURE_OPENAI_API_VERSION")

client = openai.AzureOpenAI(
    api_version=AZURE_OPENAI_API_VERSION,
    api_key=AZURE_OPENAI_API_KEY,
    azure_endpoint=AZURE_OPENAI_ENDPOINT,
)


def generate_fake_text(text_generation_model, title, content):
    # Generate text using the selected models
    prompt = """Generate a random fake news tittle in this format:
    ---
    # Title: [Fake Title]
    # Content:
    [Fake Content]
    ---
    """
    if title and content:
        prompt += """base on the following context:
        # Title: {news_title}:\n# Content: {news_content}"""
    elif title:
        prompt += """base on the following context:
        # Title: {news_title}:\n"""
    elif content:
        prompt += """base on the following context:
        # Content: {news_content}"""

    # Generate text using the text generation model
    # Generate text using the selected model
    try:
        response = client.chat.completions.create(
            model=text_generation_model,
            messages=[{"role": "system", "content": prompt}],
        )

        print(
            "Response from OpenAI API: ",
            response.choices[0].message.content,
        )
        fake_text = response.choices[0].message.content

    except openai.OpenAIError as e:
        print(f"Error interacting with OpenAI API: {e}")
        fake_text = ""

    if fake_text != "":
        fake_title, fake_content = extract_title_content(fake_text)
    return fake_title, fake_content


def extract_title_content(fake_news):
    """
    Extracts the title and content from the generated fake news string.

    This function parses a string containing fake news, which is expected
    to have a specific format with a title and content section marked by
    '# Title:' and '# Content:' respectively.

    Args:
        fake_news (str): A string containing the generated fake news.

    Returns:
        tuple: A tuple containing two elements:
            - title (str): The extracted title of the fake news.
            - content (str): The extracted content of the fake news.

    Note:
        The function assumes that the input string follows the expected format.
        If the format is not as expected, it may return unexpected results.
    """
    # Extract the title and content from the generated fake news
    title_start_index = fake_news.find("# Title: ") + len("# Title: ")
    title_end_index = fake_news.find("\n", title_start_index)
    title = fake_news[title_start_index:title_end_index].strip()

    content_start_index = fake_news.find("\n# Content: ") + len(
        "\n# Content: ",
    )
    content = fake_news[content_start_index:].strip()

    return title, content


def generate_fake_image(model, title):
    if len(title) > 0:
        IMAGE_PROMPT = f"Generate a random image about {title}"
    else:
        IMAGE_PROMPT = "Generate a random image"
    result = client.images.generate(
        model="dall-e-3",  # the name of your DALL-E 3 deployment
        prompt=IMAGE_PROMPT,
        n=1,
    )
    image_url = json.loads(result.model_dump_json())["data"][0]["url"]
    return image_url


def replace_text(news_title, news_content, replace_df):
    """
    Replaces occurrences in the input text based on the provided DataFrame.

    Args:
        text: The input text.
        replace_df: A DF with 2 columns: "find_what" & "replace_with".

    Returns:
        The text after all replacements have been made.
    """
    for _, row in replace_df.iterrows():
        find_what = row["Find what:"]
        replace_with = row["Replace with:"]
        news_content = news_content.replace(find_what, replace_with)
        news_title = news_title.replace(find_what, replace_with)
    return news_title, news_content