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import os
import base64
import numpy as np
from PIL import Image
import io
import requests
import gradio as gr
import replicate
from dotenv import load_dotenv, find_dotenv
# Locate the .env file
dotenv_path = find_dotenv()
load_dotenv(dotenv_path)
REPLICATE_API_TOKEN = os.getenv('REPLICATE_API_TOKEN')
def image_classifier(prompt, starter_image, image_strength):
if starter_image is not None:
starter_image_pil = Image.fromarray(starter_image.astype('uint8'))
# Resize the starter image if either dimension is larger than 768 pixels
if starter_image_pil.size[0] > 512 or starter_image_pil.size[1] > 512:
# Calculate the new size while maintaining the aspect ratio
if starter_image_pil.size[0] > starter_image_pil.size[1]:
# Width is larger than height
new_width = 512
new_height = int((512 / starter_image_pil.size[0]) * starter_image_pil.size[1])
else:
# Height is larger than width
new_height = 512
new_width = int((512 / starter_image_pil.size[1]) * starter_image_pil.size[0])
# Resize the image
starter_image_pil = starter_image_pil.resize((new_width, new_height), Image.LANCZOS)
# Save the starter image to a bytes buffer
buffered = io.BytesIO()
starter_image_pil.save(buffered, format="JPEG")
# Encode the starter image to base64
starter_image_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
if starter_image is not None:
input = {
"prompt": prompt + " in the style of TOK",
"negative_prompt": "worst quality, low quality, illustration, 2d, painting, cartoons, sketch",
#"refine": "expert_ensemble_refiner",
"apply_watermark": False,
"num_inference_steps": 50,
"num_outputs": 3,
"lora_scale": .96,
"image": "data:image/jpeg;base64," + starter_image_base64,
"prompt_strength": 1-image_strength,
}
else:
input = {
"width": 1024,
"height": 1024,
"prompt": prompt + " in the style of TOK",
"negative_prompt": "worst quality, low quality, illustration, 2d, painting, cartoons, sketch",
#"refine": "expert_ensemble_refiner",
"apply_watermark": False,
"num_inference_steps": 50,
"num_outputs": 3,
"lora_scale": .96,
}
output = replicate.run(
# update to new trained model
"ltejedor/cmf:3af83ef60d86efbf374edb788fa4183a6067416e2fadafe709350dc1efe37d1d",
input=input
)
print(output)
images = []
for i in range(min(len(output), 3)):
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) < 3:
images.append(Image.new('RGB', (512, 512), 'gray'))
return images
demo = gr.Interface(fn=image_classifier, inputs=["text", "image", gr.Slider(0, 1, step=0.025, value=0.2, label="Image Strength")], outputs=["image", "image", "image"])
demo.launch(share=False) |