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Complete AI-powered personal brand automation for Sami Assiri.\n\n7 agents: LinkedIn, Email, Social Media, WhatsApp, CV Optimizer, Content Strategist, Opportunity Scout.\nInfra: FastAPI + APScheduler + Docker + Ollama/Groq LLM + GitHub Pages landing page.\n83 files, ~10K lines. Cost: $0-5/month.
225 lines
7.5 KiB
Python
225 lines
7.5 KiB
Python
"""Trend analyzer -- RSS feeds + LLM to identify relevant trending topics."""
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from __future__ import annotations
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import json
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import logging
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from typing import Any
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from xml.etree import ElementTree
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import httpx
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# RSS feeds relevant to Sami's brand pillars
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# ---------------------------------------------------------------------------
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_RSS_FEEDS: list[dict[str, str]] = [
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{
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"name": "Aviation Security International",
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"url": "https://www.asi-mag.com/feed/",
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"category": "aviation_security",
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},
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{
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"name": "Airport Technology",
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"url": "https://www.airport-technology.com/feed/",
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"category": "airport_tech",
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},
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{
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"name": "Security Today",
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"url": "https://securitytoday.com/rss-feeds/news.aspx",
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"category": "security_industry",
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},
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{
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"name": "GACA News (Saudi)",
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"url": "https://gaca.gov.sa/web/en/rss",
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"category": "gaca",
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},
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{
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"name": "ICAO Newsroom",
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"url": "https://www.icao.int/Newsroom/Pages/RSS.aspx",
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"category": "icao",
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},
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]
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# System prompt for the trend-analysis LLM call
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_SYSTEM_PROMPT = """\
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You are a content strategist for a Field Services Engineer specializing in airport
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security equipment (Smiths Detection). Analyze the provided news headlines and identify
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trending topics that are relevant for LinkedIn content creation.
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For each trend, return a JSON array of objects with:
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- "topic": concise topic title
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- "relevance": "high" | "medium" | "low"
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- "pillar": one of "tech_insights", "field_life", "professional_growth", "industry_news"
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- "angle": a brief suggestion for how to turn this into engaging LinkedIn content
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- "source": the feed or keyword that surfaced it
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Return ONLY a valid JSON array. Limit to the top 10 most relevant trends.
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"""
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async def analyze_trends(
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llm_client: Any,
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keywords: list[str],
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brand_profile: dict,
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) -> list[dict]:
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"""Scan RSS feeds and use the LLM to identify relevant trending topics.
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Parameters
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----------
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llm_client:
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An :class:`LLMClient` instance.
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keywords:
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Search terms aligned with the brand pillars.
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brand_profile:
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Parsed ``brand_profile.yaml`` dict.
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Returns
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-------
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list[dict]
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Each dict contains ``topic``, ``relevance``, ``pillar``, ``angle``, ``source``.
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"""
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# Step 1: Fetch RSS headlines
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headlines = await _fetch_rss_headlines()
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# Step 2: Build LLM prompt
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personal = brand_profile.get("personal", {})
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user_prompt = f"""\
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Professional context:
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- Name: {personal.get('name_en', '')}
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- Role: {personal.get('title_en', '')}
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- Specialization: Smiths Detection airport security equipment (HI-SCAN, IONSCAN 600, CTX)
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- Keywords of interest: {', '.join(keywords)}
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Recent industry headlines:
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{_format_headlines(headlines)}
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Identify the top trending topics relevant to this professional's LinkedIn brand.
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Return ONLY a valid JSON array.
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"""
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response = await llm_client.generate(
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prompt=user_prompt,
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system_prompt=_SYSTEM_PROMPT,
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temperature=0.5,
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max_tokens=2000,
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)
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trends = _parse_trends_response(response.text)
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logger.info("Identified %d trends from %d headlines", len(trends), len(headlines))
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return trends
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# ---------------------------------------------------------------------------
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# RSS fetching
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# ---------------------------------------------------------------------------
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async def _fetch_rss_headlines(timeout: float = 15.0) -> list[dict]:
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"""Fetch headlines from all configured RSS feeds.
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Returns a list of dicts with ``title``, ``link``, ``source``, ``published``.
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Feeds that fail to load are silently skipped.
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"""
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headlines: list[dict] = []
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async with httpx.AsyncClient(timeout=timeout, follow_redirects=True) as client:
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for feed in _RSS_FEEDS:
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try:
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resp = await client.get(feed["url"])
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resp.raise_for_status()
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items = _parse_rss_xml(resp.text, source=feed["name"])
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headlines.extend(items)
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logger.debug("Fetched %d items from %s", len(items), feed["name"])
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except Exception as exc:
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logger.warning("RSS fetch failed for %s: %s", feed["name"], exc)
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return headlines
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def _parse_rss_xml(xml_text: str, source: str) -> list[dict]:
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"""Parse RSS/Atom XML and extract headline items."""
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items: list[dict] = []
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try:
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root = ElementTree.fromstring(xml_text)
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except ElementTree.ParseError:
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logger.warning("Failed to parse XML from %s", source)
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return items
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# Standard RSS 2.0
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for item in root.iter("item"):
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title_el = item.find("title")
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link_el = item.find("link")
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pub_el = item.find("pubDate")
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if title_el is not None and title_el.text:
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items.append(
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{
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"title": title_el.text.strip(),
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"link": link_el.text.strip() if link_el is not None and link_el.text else "",
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"source": source,
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"published": pub_el.text.strip() if pub_el is not None and pub_el.text else "",
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}
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)
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# Atom feeds (namespace-aware)
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atom_ns = "{http://www.w3.org/2005/Atom}"
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for entry in root.iter(f"{atom_ns}entry"):
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title_el = entry.find(f"{atom_ns}title")
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link_el = entry.find(f"{atom_ns}link")
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pub_el = entry.find(f"{atom_ns}published") or entry.find(f"{atom_ns}updated")
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if title_el is not None and title_el.text:
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link_href = ""
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if link_el is not None:
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link_href = link_el.get("href", link_el.text or "")
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items.append(
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{
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"title": title_el.text.strip(),
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"link": link_href.strip() if link_href else "",
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"source": source,
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"published": pub_el.text.strip() if pub_el is not None and pub_el.text else "",
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}
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)
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return items[:20] # Cap per feed to keep prompt manageable
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# ---------------------------------------------------------------------------
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# Formatting & parsing
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# ---------------------------------------------------------------------------
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def _format_headlines(headlines: list[dict]) -> str:
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"""Format headlines into a numbered list for the LLM prompt."""
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if not headlines:
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return "(No headlines fetched -- generate trends based on domain knowledge.)"
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lines: list[str] = []
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for i, h in enumerate(headlines[:50], start=1): # Cap at 50 total
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lines.append(f"{i}. [{h['source']}] {h['title']}")
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return "\n".join(lines)
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def _parse_trends_response(text: str) -> list[dict]:
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"""Extract a JSON array of trends from the LLM response."""
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cleaned = text.strip()
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# Strip markdown code fences
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if cleaned.startswith("```"):
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first_newline = cleaned.index("\n")
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cleaned = cleaned[first_newline + 1 :]
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if cleaned.endswith("```"):
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cleaned = cleaned[: -len("```")].rstrip()
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try:
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parsed = json.loads(cleaned)
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if isinstance(parsed, list):
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return parsed
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# Some models wrap in an object
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if isinstance(parsed, dict) and "trends" in parsed:
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return parsed["trends"]
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return [parsed]
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except json.JSONDecodeError:
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logger.warning("Failed to parse trend analysis JSON; returning empty list")
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return []
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