Summarize multiple emails using local ai powered by ollama

ollama_email-summarizer.py

import os
from ollama import chat
from ollama import ChatResponse

# set the model to use
# using the Mistral 12B model here because it runs well on a RTX 4080 with 16GB of VRAM
# and it has a relaively large context window of 128k tokens
model = "mistral-nemo:12b"

# set the directory containing the emails
# I exported the emails from Thunderbird to a directory on my desktop
# using the "ImportExportTools NG" add-on
emails_dir = "/home/david/Desktop/Email-Export/"
emails = sorted(os.scandir(emails_dir), key=lambda x: x.name)

# prompt in German, subject to change depnding on the user
prompt = "Schreibe mir eine Zusammenfassung der E-Mail. Achte darauf, dass du die wichtigsten Punkte und Informationen hervorhebst. Verwende eine klare und präzise Sprache. Halte die Zusammenfassung kurz und bündig.\n\n"

for email in emails:
    # read the email content
    with open(email.path, 'r') as f:
        email_content = f.read()

    # create a message for the chat model
    messages = [
        {
            'role': 'user',
            'content': prompt + email_content,
        },
    ]

    # get the response from the chat model
    response: ChatResponse = chat(model, messages=messages)

    print(f"{'\n' * 3}{'#' * 80}")
    print(f"Zusammenfassung von {email.path}\n")
    print(response.message.content)