app.py

from openai import OpenAI
import base64
import requests
import re

from diffusers import DiffusionPipeline
import torch
from PIL import Image
import os
import argparse

import gradio as gr


def encode_image(image_path):
  with open(image_path, "rb") as image_file:
    return base64.b64encode(image_file.read()).decode('utf-8')

def vision_gpt(prompt, image_url, api_key):
    client = OpenAI(api_key=api_key)
    response = client.chat.completions.create(
        model="gpt-4-vision-preview",
        messages=[
            {
              "role": "user",
              "content": [
                  {"type": "text",
                   "text": prompt},
                  {
                      "type": "image_url",
                      "image_url": {
                          "url": f"data:image/jpeg;base64,{image_url}", },
                  },
              ],
            }
        ],
        max_tokens=600,
    )
    return response.choices[0].message.content


def generate_images(oai_key, input_path, mistaken_class, ground_truth_class, num_generations):
    
    output_path = "out/"
    base64_image = encode_image(input_path)
    
    prompt = """
    List key features of the {} itself in this image that make it distinct from a {}? Then, write a very short and 
    concise visual midjourney prompt of the {} that includes the above features of {} (prompt should start 
    with '4K SLR photo,') and put it inside square brackets []. Do no mention {} in your prompt, also do not mention
    non-essential background scenes like "calm waters, mountains" and sub-components like "paddle of canoe" in the prompt.
    """.format(ground_truth_class, mistaken_class, ground_truth_class, ground_truth_class, mistaken_class, mistaken_class)
    
    
    print("--------------gpt prompt--------------: \n", prompt, "\n\n")
    response = vision_gpt(prompt, base64_image, oai_key)
    print("--------------GPT response--------------: \n", response, "\n\n")
    stable_diffusion_prompt =  re.search(r'\[(.*?)\]', response).group(1)
    print("--------------stable_diffusion_prompt-------------- \n", stable_diffusion_prompt, "\n\n")
    
    
    SD_pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
    SD_pipe.to("cuda")

    RF_pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
    RF_pipe.to("cuda")
    
    for i in range(num_generations):
        generated_images = SD_pipe(prompt=stable_diffusion_prompt, num_inference_steps=75).images
        refined_image = RF_pipe(prompt=stable_diffusion_prompt, image=generated_images).images[0]
        refined_image = RF_pipe(prompt=stable_diffusion_prompt, image=refined_image).images[0]
        refined_image = RF_pipe(prompt=stable_diffusion_prompt, image=refined_image).images[0]
        refined_image.save(output_path + "{}.png".format(i), 'PNG')

    return [output_path + "{}.png".format(i) for i in range(num_generations)]

iface = gr.Interface(
    fn=generate_images,
    inputs=[
        gr.Textbox(label="OpenAI API Key"),
        gr.Image(label="Input Image"),
        gr.Textbox(label="Mistaken Class"),
        gr.Textbox(label="Ground Truth Class"),
        gr.Number(label="Number of Generations")
    ],
    outputs=[
        gr.Image(label="Output Image")
    ],
    title="Image Generation and Refinement",
    description="Generates and refines images based on input classes and parameters."
)

if __name__ == "__main__":
    iface.launch()