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1834cf8 8516206 1834cf8 ff5131a 1834cf8 ff5131a 1834cf8 8516206 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | import gradio as gr
import openai
from dotenv import load_dotenv
import os
import requests
from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")
model_id = "gpt-3.5-turbo"
def gpt_image(prompt, n) -> str:
response = openai.Image.create(
prompt=prompt
+ " sticker style, with templated shapes like circle, background color should be transparent",
n=n,
size="256x256",
)
return_list = []
for res in response["data"]:
image_url = res["url"]
return_list.append(
Image.open(requests.get(image_url, stream=True).raw).convert("RGB")
)
return return_list
# test embed url = https://webplayer.momenti.tv/?project_id=1380564943&moment_info_id=1844984252
def display_giv(url):
# for GIV
# url = f"https://api.momenti.tv/v4/media/moment_infos/{moment_info_id}/thumbnail"
response = requests.get(url)
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
model = BlipForConditionalGeneration.from_pretrained(
"Salesforce/blip-image-captioning-large"
)
raw_image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt")
out = model.generate(**inputs)
unconditional_caption = processor.decode(out[0], skip_special_tokens=True)
sticker_image_list = gpt_image(unconditional_caption, 5)
gallery_list = []
for sticker in sticker_image_list:
dup_image = raw_image.copy().convert("RGBA")
# Resize the smaller image to fit within the bigger image
small_image_resized = sticker.resize((256, 256))
# Convert the image to RGBA mode (adding an alpha channel for transparency)
image_rgba = small_image_resized.convert("RGBA")
# Create a new image with a transparent background
transparent_image = Image.new("RGBA", image_rgba.size, (0, 0, 0, 0))
# Identify the background color from the non-object part of the image
border_color = image_rgba.getpixel((1, 1))[:3]
# Iterate through each pixel in the image and set the background color to transparent
data = []
for pixel in image_rgba.getdata():
if (
pixel[:3] == border_color
): # Check if the pixel matches the background color
data.append(
(pixel[0], pixel[1], pixel[2], 0)
) # Set alpha to 0 (transparent)
else:
data.append(pixel) # Keep non-background pixels unchanged
# Put the modified pixel data into the transparent image
transparent_image.putdata(data)
# Get the dimensions of the bigger image
big_width, big_height = raw_image.size
# Calculate the position for placing the smaller image at the bottom
x_position = (big_width - 256) // 2 - 128
y_position = big_height - 256 - 128
# Paste the smaller image onto the bigger image
dup_image.paste(transparent_image, (x_position, y_position), transparent_image)
gallery_list.append(dup_image)
return gallery_list
if __name__ == "__main__":
url = "https://api.momenti.tv/v4/media/moment_infos/1844984252/thumbnail"
demo = gr.Interface(
display_giv,
"text",
[
gr.Image(image_mode="RGBA"),
gr.Image(image_mode="RGBA"),
gr.Image(image_mode="RGBA"),
gr.Image(image_mode="RGBA"),
gr.Image(image_mode="RGBA"),
],
examples=[[url]],
)
demo.launch()
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