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import base64
import os
import io
import gradio as gr
import requests
from PIL import Image
api_host = os.getenv('API_HOST', 'http://localhost:8005')
def assess_image(img):
pil_image = Image.fromarray(img.astype('uint8'), 'RGB')
byte_arr = io.BytesIO()
pil_image.save(byte_arr, format='PNG')
gpt4o_url = f'{api_host}/v1/qc/gpt4o'
gemini_pro_url = f'{api_host}/v1/qc/gemini-pro'
request1 = requests.post(gpt4o_url, files={'file': byte_arr.getvalue()})
request2 = requests.post(gemini_pro_url, files={'file': byte_arr.getvalue()})
return dict(request1.json()), dict(request2.json())
def assess_colour_accuracy(img1, img2):
pil_img1 = Image.fromarray(img1.astype('uint8'), 'RGB')
pil_img2 = Image.fromarray(img2.astype('uint8'), 'RGB')
byte_arr1 = io.BytesIO()
byte_arr2 = io.BytesIO()
pil_img1.save(byte_arr1, format='PNG')
pil_img2.save(byte_arr2, format='PNG')
qc_color_url = f'{api_host}/v1/qc/garment-color'
response = requests.post(qc_color_url, files={'file_1': byte_arr1.getvalue(), 'file_2': byte_arr2.getvalue()})
return str(response.text)
iface1 = gr.Interface(fn=assess_image, inputs="image", outputs=[gr.JSON(label='GPT4o'), gr.JSON(label='Gemini Pro 1.5')])
# iface2 = gr.Interface(fn=assess_colour_accuracy,
# inputs=[gr.Image(label='Image'), gr.Image(label='Reference image')],
# outputs=gr.Textbox(label='Response'))
demo = gr.TabbedInterface([iface1], ["Image Quality Assessment"])
demo.launch()
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