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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.
108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
"""LLM-powered email classifier for Sami Assiri's inbox."""
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from __future__ import annotations
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from utils.logger import get_logger
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logger = get_logger(__name__)
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_VALID_CLASSIFICATIONS = {"urgent", "reply_needed", "spam", "info"}
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_SYSTEM_PROMPT = """\
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You are an email classification assistant for Sami Assiri, a Field Services Engineer \
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at METCO stationed at King Khalid International Airport, Riyadh. Sami is also a \
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Mechanical Engineer with experience in Python/data analytics and leadership roles \
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(SPE Alasala Chapter President, Elite Engineers Club Founder).
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Classify the incoming email into exactly ONE of these categories:
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- urgent: Job offers, interview invitations, meeting requests from colleagues or \
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managers, professional inquiries about engineering services, messages from Aramco / \
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METCO / Samsung E&A, messages from SPE or university contacts requiring action, \
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security-related operational emails, time-sensitive requests.
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- reply_needed: Professional networking messages, follow-up questions, LinkedIn \
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connection requests forwarded by email, general collaboration proposals, non-urgent \
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questions, event invitations with upcoming deadlines.
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- info: Newsletters, promotional offers, subscription updates, platform notifications \
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(LinkedIn, GitHub, etc.), informational digests, automated reports, order confirmations, \
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shipping updates.
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- spam: Unsolicited commercial messages, phishing attempts, scam emails, irrelevant \
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mass marketing, suspicious links, fake prize notifications.
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Respond with ONLY the classification label (one word, lowercase). Nothing else.\
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"""
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async def classify_email(
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llm_client,
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subject: str,
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body: str,
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from_addr: str,
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) -> str:
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"""Classify an email using the LLM.
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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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subject:
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The email subject line.
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body:
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The email body text (may be truncated).
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from_addr:
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The sender's email address / display name.
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Returns
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-------
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str
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One of ``urgent``, ``reply_needed``, ``spam``, or ``info``.
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"""
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prompt = (
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f"From: {from_addr}\n"
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f"Subject: {subject}\n\n"
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f"Body:\n{body[:2000]}\n\n"
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"Classification:"
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)
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try:
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response = await llm_client.generate(
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prompt=prompt,
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system_prompt=_SYSTEM_PROMPT,
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temperature=0.1,
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max_tokens=10,
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)
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classification = response.text.strip().lower().rstrip(".")
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if classification not in _VALID_CLASSIFICATIONS:
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# Attempt partial match (e.g. "urgent - this is..." -> "urgent")
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for label in _VALID_CLASSIFICATIONS:
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if label in classification:
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classification = label
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break
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else:
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logger.warning(
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"email_classification_fallback",
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raw=response.text,
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message="LLM returned unrecognised label, defaulting to info",
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)
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classification = "info"
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logger.info(
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"email_classified",
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subject=subject[:80],
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classification=classification,
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)
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return classification
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except Exception as exc:
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logger.error(
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"email_classification_error",
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error=str(exc),
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subject=subject[:80],
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)
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# Fail-safe: treat as reply_needed so nothing important is missed
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return "reply_needed"
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