aexol-studio/newsletter_samarite | Run with an API on Replicate

aexol-studio / newsletter_samarite

Recommendation System for samarite (newsletter) (Updated 4 months, 3 weeks ago)

Cold

Public
77 runs
CPU

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

url

*secret

Samarite API URL

*secret

Samarite API Login

password

*secret

Samarite API Password

num_last_orders

integer

Number of last orders to retrieve, more than 10000 and less than 200000

Default: 0

start_date

string

Shift + Return to add a new line

Start date for the data retrieval e.g. 2024-01-01T07:25:18.724Z

end_date

string

Shift + Return to add a new line

End date for the data retrieval e.g. 2024-03-01T07:25:18.724Z (the period should be at least one week)

product

string

Product name

custom_product

string

Shift + Return to add a new line

Custom product name

predict_type

string

Prediction type

Default: "rfm-score"

show_metrics

boolean

Whether to show the evaluation metrics

Default: false

Run this model in Node.js with one line of code:

npx create-replicate --model=aexol-studio/newsletter_samarite

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run aexol-studio/newsletter_samarite using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "aexol-studio/newsletter_samarite:c7b936da00683fd25cd798c83ab34371ced69f71566a863701aa786e93afe123",
  {
    input: {
      product: "GRAND DIVINE CREAM",
      predict_type: "rfm-score",
      show_metrics: false,
      num_last_orders: 0
    }
  }
);

console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run aexol-studio/newsletter_samarite using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "aexol-studio/newsletter_samarite:c7b936da00683fd25cd798c83ab34371ced69f71566a863701aa786e93afe123",
    input={
        "product": "GRAND DIVINE CREAM",
        "predict_type": "rfm-score",
        "show_metrics": False,
        "num_last_orders": 0
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run aexol-studio/newsletter_samarite using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "aexol-studio/newsletter_samarite:c7b936da00683fd25cd798c83ab34371ced69f71566a863701aa786e93afe123",
    "input": {
      "product": "GRAND DIVINE CREAM",
      "predict_type": "rfm-score",
      "show_metrics": false,
      "num_last_orders": 0
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

No output yet! Press "Submit" to start a prediction.

Run time and cost

This model runs on CPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

Recommendations: 1. Don’t use both num_last_orders and start_date/end_date parameters at the same time. If you use num_last_orders parameter, leave start_date and end_date empty. Otherwise, if you use start_date and end_date parameters, leave num_last_orders with 0. 2. Make sure the date is in the correct format (ISO 8601). 3. Provided num_last_orders parameter must be between 10000 and, 200000. 4. Minimum time range is 1 week. 5. If product parameter is set to ALL, the model will predict for all products. 6. If product are not in the dataset or only in a small amount of orders, the model will return an error. 7. If you want to enter a custom product name, set product to CUSTOM and enter your product name in the custom_product parameter. 8. Provided more orders or longer time range will result in better predictions.