import json
import openai
from dotenv import load_dotenv
import os

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 in the expected format.

    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 pandas DataFrame with two columns: "find_what" and "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