PhotoshopGPT: Describe photo edits in natural language (via a prompt) and automatically apply corresponding enhancements using a combination of Instagram filters and PIL functions.

photoshop_gpt.py

from opendream import opendream
from opendream.layer import Layer, ImageLayer, MaskLayer

import openai
import os
import json
from PIL import Image, ImageEnhance, ImageFilter
import pilgram

@opendream.define_op
def gpt4_editor(image_layer: ImageLayer, prompt: str):
    openai.api_key = os.environ["OPENAI_API_KEY"]

    system_prompt = """You are a photo editor. Create a set of instructions in Python that edit an image to closely match a prompt in the order they should be applied. The result should be just a list of methods with their parameters.

You can use the following PIL modules: ImageEnhance, ImageFilter
You can also use pilgram with the following filters: _1977, aden, brannan, brooklyn, clarendon, earlybird, gingham, hudson, inkwell, kelvin, lark, lofi, maven, mayfair, moon, nashville, perpetua, reyes, rise, sierra, skyline, slumber, stinson, sutro, toaster, valencia, walden, willow, xpro2
Do not add a blur unless it is asked for.

prompt: make it dark and moody
result: ["img = ImageEnhance.Contrast(img).enhance(1.5)", "img = ImageEnhance.Brightness(img).enhance(0.5)"]"""

    # call openai api
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": "prompt: " + prompt + "\nresult: "},
        ]
    )

    # parse response as array
    print(response.choices[0].message["content"])
    new_operations = json.loads(response.choices[0].message["content"])
    new_operations = [n.strip() for n in new_operations]

    # apply operations to image
    img = image_layer.get_image()
    ldict = {'img': img, "Image": Image, "ImageEnhance": ImageEnhance, "ImageFilter": ImageFilter, "pilgram": pilgram}
    for operation in new_operations:

        exec(operation, globals(), ldict)

        # show image
        img = ldict["img"]

    return Layer(image=img)