poshmarkfilter 0.0.2

Use AI to create custom filters on Poshmark

poshmarkfilter

A Python library for creating custom filters on Poshmark. Use AI to extract (virtually) any information you want from a Poshmark listing.

Requirements

• Python 3.11+
• An OpenAI API key

Installation

pip3 install git+https://github.com/samuel-poon/poshmarkfilter-py

Quick start

Include OPENAI_API_KEY in your environment variables. See .env.example for reference.

It is highly recommended that you store your API keys in a .env file and load them through python-dotenv.

from typing import Literal
import json

from dotenv import load_dotenv
from poshmarkfilter import get_poshmark_listings, scan_listing, Filter

filters = [
    Filter(
        name='lapel_style',
        description='The style of the lapel on the jacket. Options include "notch", "peak", and "shawl".',
        type_annotation=Literal['notch', 'peak', 'shawl']
        ),
    Filter(
        name='material',
        description='The material of the garment. Options include "cotton", "wool", "polyester", "silk", and "other".',
        type_annotation=Literal['cotton', 'wool', 'polyester', 'silk', 'other']
        ),
    Filter(
        name='pit_to_pit',
        description='The pit-to-pit measurement of the garment in inches, rounded to the nearest integer. Prioritize the measurement in the description over the images. If it is not provided, return 0.',
        type_annotation=int
    )
]

listings = get_poshmark_listings('https://poshmark.ca/category/Men-Suits_&_Blazers-Sport_Coats_&_Blazers?size%5B%5D=40R', count=10)

results = {}
for listing in listings:
    chat_completion = scan_listing(listing, filters=filters) # Returns a ParsedChatCompletion object
    filtered_result = json.loads(chat_completion.choices[0].message.content)
    
    results[listing.url] = filtered_result

print(json.dumps(results, indent=4)) # Pretty print results

A closer look

Filter

The core of poshmarkfilter is the Filter object. This is how you define the criteria you want to filter for. In the example above, I filter for lapel style, material, and the pit-to-pit measurement of the jacket.

Filter takes a name, description, and a type_annotation. Any type_annotation compatible with pydantic should work.

get_poshmark_listings

get_poshmark_listings gets the first count listings from a Poshmark feed in a url. Returns a list of Listing objects.

poshmark_listings = get_poshmark_listings(
    url='https://poshmark.ca/link/to/feed', # Mandatory (str), the URL of the Poshmark feed to retrieve listings from
    count=100,                              # Optional (int, default=100), the number of listings to retrieve
    starting_max_id=1,                      # Optional (int, default=1), the starting page to retrieve listings from
    delay_seconds=2,                        # Optional (int, default=2), the delay between each page request
)

Recommendations:

• For best results, use get_poshmark_listings in conjunction with the existing filters on Poshmark. In the quick start example, I have filtered for Men's Suits & Blazers, Sports Coats & Blazers, in a size 40R.
• This library does not have access to your account. You must manually filter for your size as "My Size" will not work.

• get_poshmark_listings returns the first count listings. If you are not scanning every listing, consider how you sort the results.

scan_listing

scan_listing takes a Listing and filters them based on filters. Returns a ParsedChatCompletion object whose message content is {"filter_name_1":"filter_1_result", "filter_name_2":"filter_2_result",...}.

chat_completion = scan_listing(
    listing=listing,            # Mandatory (poshmarkfilter.Listing), the Listing object to scan
    filters=filters,            # Mandatory (Iterable[poshmarkfilter.Filter]), the filters to include in the chat completion
    include_cover_shot=True,    # Optional (bool, default=True), if True, includes the cover shot in the prompt.
    include_all_pictures=False, # Optional (bool, default=False), if True, includes all pictures in the prompt.
    openai_client=None,         # Optional (openai.Client), the OpenAI client to use. If None, creates an OpenAI client using the OPENAI_API_KEY environment variable.
    model='gpt-4o-mini',        # Optional (str, default='gpt-4o-mini'), the OpenAI model to use.
    detail='low',               # Optional (str, default='low'), the detail level of the OpenAI model. Options include 'low', 'medium', and 'high'.
    max_tokens=None             # Optional (int | None, default=None), the maximum number of tokens to generate.
)

Recommendations:

• At a minimum, all listings scanned through scan_listing will include the title and description of the listing. By default, this function will also provide the cover shot to the model, but if this is unnecessary, I recommmend setting include_cover_shot to False.
• Unless you absolutely need to include all pictures in the prompt (e.g. for specific measurements), I recommend keeping include_all_pictures to False. Setting this to True can be expensive.

Estimating costs

Based on my personal usage, scanning a listing using gpt-4o-mini with include_cover_shot=True and include_all_pictures=False at detail=low (the default options) consumes roughly 3k input tokens. Assuming that each completion takes 20 output tokens, this means each call costs ~$0.0005 or $0.46/1000 calls.

If the filter can be processed using the title and description of the listing alone, you can drastically reduce the cost by excluding all photos from the scan.