post_generator.py

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. Ensure that hashtags are placed one line below the closing line, leaving a decent space for better readability and structure.
    - 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! 🚀 #DreamJob #StayMotivated"
    - Topic: "Mental Health", Tone: "Professional", Language: "English"
      Closing Line: "Your mental well-being is essential. Let’s discuss ways to manage stress. 💡 #MentalHealth #StressManagement"
    - Topic: "Dating", Tone: "Informal", Language: "Hinglish"
      Closing Line: "Apka perfect date idea kya hai? Neeche share karein! 😄 #DatingTips #FunDates"
    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 generate_post(length, language, tag, selected_tone=None):
    """
    Generate a LinkedIn post dynamically with LLM including a generated closing line.
    """
    prompt = get_prompt(length, language, tag)
    response = llm.invoke(prompt)
    post_content = response.content.strip()

    # Generate a dynamic closing line using LLM
    if selected_tone and tag:
        try:
            closing_line = generate_closing_line(language, tag, selected_tone)
            # Append the closing line with hashtags
            post_content += f"\n\n{closing_line}\n\n"
        except Exception as e:
            # Fallback in case of LLM failure
            post_content += f"\n\nThank you for reading. Your feedback is valued! 🙌"

    return post_content


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.
    5. Do not include hashtags in the main content.
    '''

    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']
            # Remove hashtags from examples
            cleaned_post_text = " ".join(word for word in post_text.split() if not word.startswith("#"))
            prompt += f"Example {i + 1}: {cleaned_post_text}\n"
    
    prompt += "\nNow write the post."
    return prompt


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