services/linkedin_analyzer.py

from services.utils import run_analysis

SYSTEM_PROMPT = """You are a LinkedIn content strategist. Return ONLY a valid JSON object (not an array). Create high-engagement LinkedIn posts with industry insights."""

DEFAULTS = {
    "post_drafts": [
        {"headline": "Key Insights on This Topic", "body": "Here are my thoughts on this important subject. It's changing how we approach problems in 2026.", "hook": "Stop scrolling if you care about this", "post_type": "text", "call_to_action": "Share your thoughts below", "media_suggestion": "Infographic with key stats"}
    ],
    "hashtags": ["#industry", "#leadership"],
    "article_topics": ["The Future of This Industry"],
    "thought_leadership_angles": ["Unique perspective on current trends"],
    "engagement_prompts": ["What has your experience been with this topic?"],
    "target_audience_tags": ["#TechLeaders", "#IndustryPros"],
    "best_posting_time": "7-9 AM EST Tue-Thu",
    "industry_insights": "This is a growing space with significant potential."
}

def analyze_linkedin(content: str) -> dict:
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": f'Topic: "{content[:1000]}"\nReturn a JSON object (not an array) with these keys:\n- post_drafts (2 items, each with "headline" + "body" + "hook" + "post_type" ["text"|"article"|"poll"] + "call_to_action" + "media_suggestion")\n- hashtags (4 items)\n- article_topics (2 items)\n- thought_leadership_angles (2 items)\n- engagement_prompts (2 items, questions to spark discussion)\n- target_audience_tags (2 items, industry tags)\n- best_posting_time (string)\n- industry_insights (string)'}
    ]
    return run_analysis(messages, defaults=DEFAULTS, temperature=0.35, max_new_tokens=1000)