api/index.py

import os
import base64
import numpy as np
from PIL import Image
import io
import requests

import replicate
from flask import Flask, request
import gradio as gr

import openai
from openai import OpenAI


from dotenv import load_dotenv, find_dotenv

import json


# Locate the .env file
dotenv_path = find_dotenv()

load_dotenv(dotenv_path)

OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
REPLICATE_API_TOKEN = os.getenv('REPLICATE_API_TOKEN')



client = OpenAI()


def call_openai(pil_image):
    # Save the PIL image to a bytes buffer
    buffered = io.BytesIO()
    pil_image.save(buffered, format="JPEG")
    
    # Encode the image to base64
    image_data = base64.b64encode(buffered.getvalue()).decode('utf-8')
    
    try:
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": "You are a product designer. I've attached a moodboard here. In one sentence, what do all of these elements have in common? Answer from a design language perspective, if you were telling another designer to create something similar, including any repeating colors and materials and shapes and textures"},
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": "data:image/jpeg;base64," + image_data,
                            },
                        },
                    ],
                }
            ],
            max_tokens=300,
        )
        return response.choices[0].message.content
    except openai.BadRequestError as e:
        print(e)
        print("e type")
        print(type(e))
        raise gr.Error(f"Please retry with a different moodboard file (below 20 MB in size and is of one the following formats: ['png', 'jpeg', 'gif', 'webp'])")
    except Exception as e:
        raise gr.Error("Unknown Error")


def image_classifier(moodboard, prompt):

    if moodboard is not None:
        pil_image = Image.fromarray(moodboard.astype('uint8'))

        openai_response = call_openai(pil_image)
        openai_response = openai_response.replace('moodboard', '')
        openai_response = openai_response.replace('share', '')
        openai_response = openai_response.replace('unified', '')
    else:
        raise gr.Error(f"Please upload a moodboard to control image generation style")

    input = {
        "prompt": "high quality render of " + prompt + ", " + openai_response[12:],
        "negative_prompt": "worst quality, low quality, illustration, 2d, painting, cartoons, sketch",
        "output_format": "jpg"
    }
    
    try:
        output = replicate.run(
            "stability-ai/stable-diffusion-3",
            input=input
        )
    except Exception as e:
        raise gr.Error(f"Error: {e}")

    try:
        image_url = output[0]
        response = requests.get(image_url)
        img1 = Image.open(io.BytesIO(response.content))
    except Exception as e:
        raise gr.Error(f"Image download failed: {e}")

    input["aspect_ratio"] = "3:2"
    input["cfg"] = 6
    
    try:
        output = replicate.run(
            "stability-ai/stable-diffusion-3",
            input=input
        )
        image_url = output[0]
        response = requests.get(image_url)
        img2 = Image.open(io.BytesIO(response.content))
    except Exception as e:
        raise gr.Error(f"Second image download failed: {e}")

    # Call Stable Diffusion API with the response from OpenAI
    input = {
        "width": 768,
        "height": 768,
        "prompt": "high quality render of " + prompt + ", " + openai_response[12:],
        "negative_prompt": "worst quality, low quality, illustration, 2d, painting, cartoons, sketch",
        "refine": "expert_ensemble_refiner",
        "apply_watermark": False,
        "num_inference_steps": 25,
        "num_outputs": 2
    }
    
    output = replicate.run(
        "stability-ai/sdxl:7762fd07cf82c948538e41f63f77d685e02b063e37e496e96eefd46c929f9bdc",
        input=input
    )

    images = [img1, img2]

    for i in range(min(len(output), 2)):
        image_url = output[i]
        response = requests.get(image_url)
        images.append(Image.open(io.BytesIO(response.content)))
    
    # Add empty images if fewer than 3 were returned
    while len(images) < 4:
        images.append(Image.new('RGB', (768, 768), 'gray'))

    images.reverse()
    return images

demo = gr.Interface(fn=image_classifier, inputs=["image", "text"], outputs=["image", "image", "image", "image"])
demo.launch(share=True)