post_generator.py

import re  # For handling hashtags
from llm_helper import llm
from few_shot import FewShotPosts

few_shot = FewShotPosts()


def get_length_str(length):
    if length == "Short":
        return "1 to 5 lines"
    if length == "Medium":
        return "6 to 10 lines"
    if length == "Long":
        return "11 to 15 lines"


def generate_closing_line(language, tag, tone):
    """
    Generate a closing line using the LLM based on language, tag, and tone.
    """
    closing_prompt = f"""
    You are writing a LinkedIn post. Create a concise and engaging closing line.
    - The closing line should reflect the topic: "{tag}".
    - Use the tone/style: "{tone}".
    - Add hashtags only at the end, i.e., after the closing line and not before it.
    - The closing line must encourage engagement or provide a call to action, relevant to the topic.
    - Use the language: "{language}" (Hinglish means Hindi phrases written in English script).
    Examples:
    - Topic: "Job Search", Tone: "Motivational", Language: "English"
      Closing Line: "Your dream job is closer than you think. Stay determined! 🚀"
    - Topic: "Mental Health", Tone: "Professional", Language: "English"
      Closing Line: "Your mental well-being is essential. Let’s discuss ways to manage stress. 💡"
    - Topic: "Dating", Tone: "Informal", Language: "Hinglish"
      Closing Line: "Apka perfect date idea kya hai? Neeche share karein! 😄"
    Now, write a relevant closing line for the following inputs:
    Topic: "{tag}"
    Tone: "{tone}"
    Language: "{language}"
    """
    response = llm.invoke(closing_prompt)
    return response.content.strip()


def extract_hashtags(content):
    """
    Extract all hashtags from the given content.
    """
    return re.findall(r"#\w+", content)


def remove_hashtags(content):
    """
    Remove all hashtags from the given content.
    """
    return re.sub(r"#\w+", "", content).strip()


def generate_post(length, language, tag, selected_tone=None):
    """
    Generate a LinkedIn post dynamically with LLM, ensuring hashtags are added only at the end.
    """
    # Generate the main content
    prompt = get_prompt(length, language, tag)
    response = llm.invoke(prompt)
    post_content = response.content.strip()  # Clean up whitespace

    # Extract and remove hashtags from the main content
    hashtags = extract_hashtags(post_content)
    post_content = remove_hashtags(post_content)

    # Generate the closing line
    closing_line = ""
    if selected_tone and tag:
        try:
            closing_line = generate_closing_line(language, tag, selected_tone)
            # Extract and remove hashtags from the closing line
            hashtags += extract_hashtags(closing_line)
            closing_line = remove_hashtags(closing_line).strip()
        except Exception as e:
            closing_line = "Thank you for reading. Your feedback is valued! 🙌"

    # Combine the cleaned main content and closing line
    full_post = f"{post_content}\n\n{closing_line}"

    # Add unique hashtags at the end of the post
    if hashtags:
        unique_hashtags = " ".join(set(hashtags))  # Remove duplicates
        full_post += f"\n\n{unique_hashtags}"

    return full_post


def get_prompt(length, language, tag):
    length_str = get_length_str(length)

    prompt = f'''
    Write a professional, engaging LinkedIn post.
    1. Topic: "{tag}"
    2. Post Length: "{length_str}"
    3. Language: "{language}" (Hinglish means Hindi phrases written in English script).
    4. Incorporate creativity, enthusiasm, emotional appeal, and actionable advice.
    '''

    examples = few_shot.get_filtered_posts(length, language, tag)
    if examples:
        prompt += "\nExamples of great posts:\n"
        for i, post in enumerate(examples[:2]):  # Limit to 2 examples
            post_text = post['text']
            prompt += f"Example {i + 1}: {post_text}\n"
    
    prompt += "\nNow write the post."
    return prompt


if __name__ == "__main__":
    print(generate_post("Medium", "English", "Mental Health"))