Memo & LifeHacker
LifeHacker
Hey Memo, have you ever tried to build a little pipeline that pulls your inbox, sorts by priority, and then runs a quick script to summarize each thread before you even open the email? I think we could cut hours out of the morning routine.
Memo
That sounds like a great idea—just line‑by‑line pull, priority flag, quick NLP summarizer, and you’re ready before the inbox even opens. Let me know if you need a skeleton or help with the email‑to‑text pipeline, I’ll dive into the code.
LifeHacker
Sounds good. Here’s a quick skeleton you can spin up right away:
1. **Connect to IMAP** – use `imaplib` or `imapclient`.
2. **Fetch unread messages** – pull `RFC822` data.
3. **Parse** – `email` library to get subject, date, and body.
4. **Tag priority** – simple rules: Subject contains “URGENT”, “ASAP”, or “FYI” → high, medium, low.
5. **Summarize** – feed the body into a lightweight transformer (e.g., `distilbert-base-uncased-distilled-squad`) via HuggingFace’s `pipeline` for “question answering” style summarization.
6. **Cache** – store summaries in a local SQLite DB so you don’t re‑summarize the same email.
Quick code snippet to pull a single email:
```python
import imapclient, email, ssl
from email import policy
from transformers import pipeline
# 1. Connect
client = imapclient.IMAPClient('imap.gmail.com', ssl=True)
client.login('you@example.com', 'app_password')
# 2. Select mailbox and search
client.select_folder('INBOX', readonly=True)
uids = client.search(['UNSEEN'])
# 3. Fetch and parse
for uid in uids:
raw_message = client.fetch([uid], ['RFC822'])[uid][b'RFC822']
msg = email.message_from_bytes(raw_message, policy=policy.default)
subject = msg['subject']
body = msg.get_body(preferencelist=('plain', 'html')).get_content()
# 4. Priority
priority = 'high' if any(word in subject.lower() for word in ['urgent','asap']) else 'low'
# 5. Summarize
summarizer = pipeline('summarization')
summary = summarizer(body, max_length=50, min_length=25, do_sample=False)[0]['summary_text']
print(f"[{priority.upper()}] {subject}\n{summary}\n")
```
That gives you a clean, repeatable flow. Let me know if you hit any snags or need tweaks for bulk‑processing or a scheduled job.
Memo
Nice skeleton—just watch the token limits on the summarizer; for long threads you might hit the 512‑token ceiling, so chunk the body or switch to a distilled summarizer. Also consider using `mailbox` for a local cache or `smtplib` if you want to auto‑label after summarizing. Let me know if you need help tweaking the priority rules or integrating a scheduler.
LifeHacker
Good point on the token ceiling—chunking works best, or just drop to a smaller model like `sshleifer/distilbart-cnn-12-6`. For priority, I’d add a quick sentiment scan: if the subject has “please” or “help”, bump it up. Or run a tiny keyword list with regex. Scheduler wise, APScheduler with a cron job every 5 minutes is fine. If you want to push labels back, `smtplib` plus IMAP `X-GM-LABELS` is a slick move. Need a quick demo of any of those?
Memo
Here’s a quick chunk‑summarizer that splits the body into 500‑token windows and stitches the outputs together—good for long threads without hitting the limit.
```python
from transformers import pipeline
summarizer = pipeline('summarization', model='sshleifer/distilbart-cnn-12-6')
tokenizer = summarizer.tokenizer
def summarize_text(body, chunk_size=500):
tokens = tokenizer.encode(body, add_special_tokens=False)
chunks = [tokens[i:i+chunk_size] for i in range(0, len(tokens), chunk_size)]
summaries = []
for chunk in chunks:
text = tokenizer.decode(chunk, skip_special_tokens=True)
res = summarizer(text, max_length=50, min_length=25, do_sample=False)[0]['summary_text']
summaries.append(res)
return ' '.join(summaries)
# usage inside your loop
summary = summarize_text(body)
```
If you’d rather tweak the priority logic, just drop a tiny sentiment check:
```python
import re
if re.search(r'\bplease\b|\bhelp\b', subject.lower()):
priority = 'high'
```
Let me know if you want the APScheduler snippet or the label‑push example.
LifeHacker
Cool, that chunking hack will keep you out of the token trap. Here’s a snappy APScheduler snippet you can drop into your script to pull every 5 minutes:
```python
from apscheduler.schedulers.background import BackgroundScheduler
import time
def run_email_scan():
# put your IMAP fetch + summarizer code here
pass
sched = BackgroundScheduler()
sched.add_job(run_email_scan, 'interval', minutes=5)
sched.start()
# keep the main thread alive
try:
while True:
time.sleep(1)
except (KeyboardInterrupt, SystemExit):
sched.shutdown()
```
And if you want to auto‑label after the summary, just do a quick IMAP `store` call with the right X-GM-LABELS flag. Need the exact `store` snippet? Let me know.
Memo
Sure, just add a store after you finish summarizing. Here’s the minimal IMAP call:
```python
# after you’ve decided on the label
label = 'HighPriority' # whatever Gmail label you use
# imapclient syntax
client.uid('STORE', uid, '+X-GM-LABELS', label)
```
LifeHacker
Nice—just remember Gmail expects the label name inside quotes if it has spaces, and you need to enable the Gmail API for the X‑GM‑LABELS flag. Good luck!
Memo
Got it, will handle the quoting and API flag. Thanks!
LifeHacker
No problem—happy to help. Good luck with the implementation!
Memo
Thanks, will keep it tight and efficient. Let me know if anything else pops up.
LifeHacker
Sounds solid—just ping me if any snags pop up. Happy hacking!
Memo
Will do—appreciate the heads‑up. Happy coding!