app.py

import os
import random
import string
from django.apps import AppConfig
from django.conf import settings
from jinja2 import Environment, FileSystemLoader
import openai

class AppNameConfig(AppConfig):
    default_auto_field = 'django.db.models.BigAutoField'
    name = 'app_name'

    def ready(self):
        # import models, functions, and libraries here
        from . import models, functions, libraries

        # idea-to-app logic
        if settings.IDEA_TO_APP:
            # generate a unique name for the new model
            model_name = ''.join(random.choices(string.ascii_lowercase + string.digits, k=10))
            # merge the selected models and functions here
            # and save it as a new GGuf model
            gguf_model = functions.merge_models(models.CodeModel, models.ImageModel, 'function1', 'function3')
            # save the GGuf model under the unique name
            gguf_model.save(model_name)
            # return the merged model for preview/demo
            settings.IDEA_TO_APP_PREVIEW = gguf_model

requirements.txt:

Django
huggingface_models
jinja2
openai

models.py:

from django.db import models

class CodeModel(models.Model):
    name = models.CharField(max_length=100)
    code = models.TextField()

class ImageModel(models.Model):
    name = models.CharField(max_length=100)
    image = models.ImageField(upload_to='images/')

functions.py:

def merge_models(model1, model2, function1, function3):
    # merge the selected models and functions here
    model1_objects = model1.objects.all()
    model2_objects = model2.objects.all()
    merged_objects = []
    for obj1 in model1_objects:
        obj2 = model2_objects.filter(name=obj1.name).first()
        if obj2:
            merged_obj = {
                'name': obj1.name,
                'code': function1(obj1.code),
                'image': function3(obj2.image),
            }
            merged_objects.append(merged_obj)
    return merged_objects

libraries.py:

import openai
import jinja2

def function1(code):
    # translate natural language to executable code here
    # using the OpenAI API
    openai.api_key = 'YOUR_OPENAI_API_KEY'
    response = openai.Completion.create(
        engine='code-davinci-002',
        prompt=f'Translate this Python code to executable code: {code}',
        temperature=0.5,
        max_tokens=512,
        top_p=1,
        frequency_penalty=0.5,
        presence_penalty=0,
        stop=['