Initial commit: Leopost Full — merge di Leopost, Post Generator e Autopilot OS
- Backend FastAPI con multi-LLM (Claude/OpenAI/Gemini) - Publishing su Facebook, Instagram, YouTube, TikTok - Calendario editoriale con awareness levels (PAS, AIDA, BAB...) - Design system Editorial Fresh (Fraunces + DM Sans) - Scheduler automatico, gestione commenti AI, affiliate links Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Michele
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backend/app/services/content.py
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backend/app/services/content.py
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"""
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Content generation logic for social media posts.
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Handles text generation, hashtag creation, and affiliate link injection
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using LLM providers and character profiles.
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"""
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from __future__ import annotations
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from .llm import LLMProvider
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def generate_post_text(
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character: dict,
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llm_provider: LLMProvider,
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platform: str,
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topic_hint: str | None = None,
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) -> str:
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"""Generate social media post text based on a character profile.
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Args:
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character: Dict with keys: name, niche, topics (list), tone (str).
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topic_hint: Optional topic suggestion to guide generation.
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llm_provider: LLM provider instance for text generation.
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platform: Target platform (e.g. 'instagram', 'facebook', 'tiktok', 'youtube').
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Returns:
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Generated post text as a string.
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"""
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name = character.get("name", "Creator")
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niche = character.get("niche", "general")
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topics = character.get("topics", [])
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tone = character.get("tone", "professional")
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topics_str = ", ".join(topics) if topics else "general topics"
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system_prompt = (
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f"You are {name}, a social media content creator in the {niche} niche. "
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f"Your expertise covers: {topics_str}. "
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f"Your communication style is {tone}. "
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f"You create authentic, engaging content that resonates with your audience. "
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f"Never reveal you are an AI. Write as {name} would naturally write."
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)
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# Platform-specific instructions
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platform_guidance = {
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"instagram": (
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"Write an Instagram caption. Keep it engaging, use line breaks for readability. "
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"Aim for 150-300 characters for the main hook, then expand. "
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"Do NOT include hashtags (they will be added separately)."
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),
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"facebook": (
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"Write a Facebook post. Can be longer and more conversational. "
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"Encourage engagement with a question or call to action at the end. "
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"Do NOT include hashtags."
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),
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"tiktok": (
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"Write a TikTok caption. Keep it very short and punchy (under 150 characters). "
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"Use a hook that grabs attention. Do NOT include hashtags."
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),
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"youtube": (
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"Write a YouTube video description. Include a compelling opening paragraph, "
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"key points covered in the video, and a call to action to subscribe. "
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"Do NOT include hashtags."
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),
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"twitter": (
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"Write a tweet. Maximum 280 characters. Be concise and impactful. "
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"Do NOT include hashtags."
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),
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}
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guidance = platform_guidance.get(
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platform.lower(),
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f"Write a social media post for {platform}. Do NOT include hashtags.",
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)
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topic_instruction = ""
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if topic_hint:
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topic_instruction = f" The post should be about: {topic_hint}."
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prompt = (
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f"{guidance}{topic_instruction}\n\n"
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f"Write the post now. Output ONLY the post text, nothing else."
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)
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return llm_provider.generate(prompt, system=system_prompt)
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def generate_hashtags(
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text: str,
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llm_provider: LLMProvider,
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platform: str,
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count: int = 12,
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) -> list[str]:
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"""Generate relevant hashtags for a given text.
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Args:
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text: The post text to generate hashtags for.
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llm_provider: LLM provider instance.
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platform: Target platform.
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count: Number of hashtags to generate.
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Returns:
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List of hashtag strings (each prefixed with #).
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"""
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platform_limits = {
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"instagram": 30,
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"tiktok": 5,
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"twitter": 3,
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"facebook": 5,
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"youtube": 15,
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}
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max_tags = min(count, platform_limits.get(platform.lower(), count))
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system_prompt = (
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"You are a social media hashtag strategist. You generate relevant, "
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"effective hashtags that maximize reach and engagement."
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)
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prompt = (
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f"Generate exactly {max_tags} hashtags for the following {platform} post.\n\n"
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f"Post text:\n{text}\n\n"
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f"Rules:\n"
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f"- Mix popular (high reach) and niche (targeted) hashtags\n"
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f"- Each hashtag must start with #\n"
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f"- No spaces within hashtags, use CamelCase for multi-word\n"
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f"- Output ONLY the hashtags, one per line, nothing else"
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)
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result = llm_provider.generate(prompt, system=system_prompt)
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# Parse hashtags from the response
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hashtags: list[str] = []
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for line in result.strip().splitlines():
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tag = line.strip()
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if not tag:
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continue
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# Ensure it starts with #
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if not tag.startswith("#"):
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tag = f"#{tag}"
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# Remove any trailing punctuation or spaces
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tag = tag.split()[0] # Take only the first word if extra text
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hashtags.append(tag)
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return hashtags[:max_tags]
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def inject_affiliate_links(
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text: str,
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affiliate_links: list[dict],
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topics: list[str],
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) -> tuple[str, list[dict]]:
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"""Find relevant affiliate links and append them to the post text.
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Matches affiliate links based on topic overlap. Links whose keywords
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overlap with the provided topics are appended naturally at the end.
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Args:
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text: Original post text.
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affiliate_links: List of dicts, each with keys:
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- url (str): The affiliate URL
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- label (str): Display text for the link
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- keywords (list[str]): Topic keywords this link is relevant for
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topics: Current post topics to match against.
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Returns:
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Tuple of (modified_text, links_used) where links_used is the list
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of affiliate link dicts that were injected.
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"""
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if not affiliate_links or not topics:
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return text, []
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# Normalize topics to lowercase for matching
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topics_lower = {t.lower() for t in topics}
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# Score each link by keyword overlap
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scored_links: list[tuple[int, dict]] = []
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for link in affiliate_links:
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keywords = link.get("keywords", [])
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keywords_lower = {k.lower() for k in keywords}
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overlap = len(topics_lower & keywords_lower)
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if overlap > 0:
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scored_links.append((overlap, link))
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if not scored_links:
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return text, []
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# Sort by relevance (most overlap first), take top 2
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scored_links.sort(key=lambda x: x[0], reverse=True)
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top_links = [link for _, link in scored_links[:2]]
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# Build the links section
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links_section_parts: list[str] = []
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for link in top_links:
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label = link.get("label", "Check this out")
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url = link.get("url", "")
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links_section_parts.append(f"{label}: {url}")
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links_text = "\n".join(links_section_parts)
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modified_text = f"{text}\n\n{links_text}"
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return modified_text, top_links
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