chatbot_prompts.py

# chatbot_prompts.py
import logging

def get_initial_insight_prompt_and_suggestions(plot_id: str, plot_label: str, plot_data_summary: str = None):
    """
    Generates an initial prompt for the LLM to provide insights on a plot and suggested questions.
    Args:
        plot_id (str): The unique identifier for the plot.
        plot_label (str): The display label for the plot.
        plot_data_summary (str, optional): A textual summary of the data for the plot.
    Returns:
        tuple: (prompt_for_llm_str, list_of_suggestion_strings)
    """
    logging.info(f"Generating initial insight prompt for plot_id: {plot_id}, label: {plot_label}")

    base_persona_prompt = "You are an expert in Employer Branding and LinkedIn social media strategy. Analyze the following data for the chart '{plot_label}' and provide key insights and actionable advice. Focus on interpreting the provided data snapshot."
    prompt_text = f"{base_persona_prompt.format(plot_label=plot_label)}\n\n"

    if plot_data_summary and plot_data_summary.strip() and \
       "No data summary available" not in plot_data_summary and \
       "Error generating data summary" not in plot_data_summary and \
       "Accesso negato" not in plot_data_summary and \
       f"Nessun sommario dati specifico disponibile per '{plot_label}'" not in plot_data_summary : # Added check for this specific no data message
        prompt_text += f"Data Snapshot for '{plot_label}':\n```text\n{plot_data_summary}\n```\n\n"
        prompt_text += "Based on this data and your expertise, what are the most important observations and what steps can be taken to improve or capitalize on these trends? Please provide a concise initial analysis."
    else:
        prompt_text += f"No specific data snapshot is available for '{plot_label}'. Provide general insights and advice for improving performance related to '{plot_label}' on LinkedIn, assuming typical scenarios. Please provide a concise initial analysis."

    # Default suggestions
    suggestions = [
        f"What are the key drivers for {plot_label.lower()} based on the data?",
        f"How can I improve my {plot_label.lower()} according to these trends?",
        f"What does good performance look like for {plot_label.lower()}?"
    ]

    # Customize suggestions per plot_id
    if plot_id == "followers_count":
        suggestions = [
            "Based on the follower data, what was our peak growth period?",
            "How often should I post to maximize follower growth?",
            "What content typically resonates most with potential followers?"
        ]
    elif plot_id == "engagement_rate":
        suggestions = [
            "What does the engagement trend tell us about recent content performance?",
            "What types of posts typically get the highest engagement?",
            "Can you give me examples of strong calls to action?"
        ]
    elif plot_id == "reach_over_time":
        suggestions = [
            "What does the reach data suggest about our content visibility?",
            "What are effective organic strategies to increase post reach?",
            "How do hashtags and tagging strategies impact reach?"
        ]
    elif plot_id == "impressions_over_time":
        suggestions = [
            "How do current impressions compare to previous periods based on the data?",
            "What's the difference between reach and impressions?",
            "Does LinkedIn's algorithm favor certain content for impressions?"
        ]
    elif plot_id == "comments_sentiment":
        suggestions = [
            "What does the sentiment breakdown indicate about audience perception?",
            "How can I encourage more positive comments?",
            "What's the best way to respond to negative comments?"
        ]
    # Add more plot_id specific suggestions if needed

    # Ensure exactly 3 suggestions
    while len(suggestions) < 3:
        suggestions.append(f"Tell me more about the trends in the {plot_label.lower()} data.")
    if len(suggestions) > 3:
        suggestions = suggestions[:3]

    return prompt_text, suggestions