src/application/content_generation.py

import json
from typing import Optional

import openai
import pandas as pd

from src.application.config import (
    AZUREOPENAI_CLIENT,
    GPT_IMAGE_MODEL,
)


def generate_fake_text(
    text_generation_model: str,
    title: str = None,
    content: str = None,
) -> tuple[str, str]:
    """
    Generates fake news title and content using an Azure OpenAI model.

    Args:
        text_generation_model: The name of the Azure OpenAI model to use.
        title: Optional title to use as context for fake text generation.
        content: Optional content to use as context for fake text generation.

    Returns:
        A tuple containing the generated fake title and content (both strings).
        Returns empty strings if generation fails.
    """
    # 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 = AZUREOPENAI_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: str) -> tuple[str, str]:
    """
    Extracts the title and content from the generated fake text.

    Args:
        fake_news: The generated fake text string.

    Returns:
        A tuple containing the extracted title and content.
    """
    title = ""
    content = ""

    try:
        # Extract the title and content from the generated fake news
        title_start = fake_news.find("# Title: ") + len("# Title: ")
        title_end = fake_news.find("\n", title_start)
        if title_start != -1 and title_end != -1:
            title = fake_news[title_start:title_end]  # .strip()

        title_start = fake_news.find("\n# Content: ") + len(
            "\n# Content: ",
        )
        content = fake_news[title_start:].strip()
    except Exception as e:
        print(f"Error extracting title and content: {e}")

    return title, content


def generate_fake_image(
    title: str,
    model: str = GPT_IMAGE_MODEL,
) -> Optional[str]:
    """
    Generates a fake image URL using Azure OpenAI's image generation API.

    Args:
        title: The title to use as a prompt for image generation.
        model: The name of the Azure OpenAI image generation model to use.

    Returns:
        The URL of the generated image, or None if an error occurs.
    """
    try:
        if title:
            image_prompt = f"Generate a random image about {title}"
        else:
            image_prompt = "Generate a random image"

        result = AZUREOPENAI_CLIENT.images.generate(
            model=model,
            prompt=image_prompt,
            n=1,
        )

        image_url = json.loads(result.model_dump_json())["data"][0]["url"]
        return image_url

    except Exception as e:
        print(f"Error generating fake image: {e}")
        return None  # Return None if an error occurs


def replace_text(
    news_title: str,
    news_content: str,
    replace_df: pd.DataFrame,
) -> tuple[str, str]:
    """
    Replaces occurrences in the input title and content
        based on the provided DataFrame.

    Args:
        news_title: The input news title.
        news_content: The input news content.
        replace_df: A DataFrame with two columns:
            "Find what:" and "Replace with:".

    Returns:
        A tuple containing the modified news title and content.
    """
    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