app.py

#%%
import gradio as gr
from PIL import Image
from torchvision import transforms
from siamese_nn import Siamese_nn
import torch, os
import torch.nn.functional as F

model = Siamese_nn()
weights = torch.load('trained_model', map_location=torch.device('cpu'))
model.load_state_dict(weights)
model.eval()

file_list = os.listdir("data")

examples = []
usersIndexes = []
for x in file_list:
    if x[0:2] not in usersIndexes:
        usersIndexes.append(x[0:2])
    
for user in usersIndexes:
    usersImages = [x for x in file_list if str(user) in x]
    notUsersImages = [x for x in file_list if str(user) not in x]

    for userImage in usersImages:
        for userImageCopy in usersImages:
            examples.append([userImage, userImageCopy, 0])
            for notUser in notUsersImages:
                examples.append([userImage, notUser, 1])

def predict(input1, input2, label=None):
    img1_PIL = Image.open(f'data/{input1}')
    img2_PIL = Image.open(f'data/{input2}')
    img1 = transforms.ToTensor()(img1_PIL).unsqueeze(0)
    img2 = transforms.ToTensor()(img2_PIL).unsqueeze(0)

    for el in examples:
        if input1 == el[0] and input2 == el[1] and el[2] == 0:
            label = 'Scans of the same finger'
            break
        if input1 == el[0] and input2 == el[1] and el[2] == 1:
            label = 'Scans of different fingers'        
    
    with torch.no_grad():
        out1, out2 = model(img1, img2)
        pred = F.pairwise_distance(out1, out2)
        if pred < 0.6:
            decision = f'Access granted, confidence: {pred.item():4f}'
        else:
            decision = f'Access denied, confidence: {pred.item():4f}'
    
    return img1_PIL, img2_PIL, decision, label 
#%%
css = """
.gradio-container {
    height: 100vh;
    max-width: 1024px !important;
}

.my_img {
    max-height: 288px !important;
    object-fit: cover !important;
}

.img-select div.secondary-wrap {
    position: relative;
}
.img-select div.icon-wrap {
    position: absolute;
    pointer-events: none;
    right: 0;
}
#res div h2 { color: #07ef03; }
"""

js = """
() => {
    
    label = document.querySelector("#res div h2");
    txt = label.textContent.split(",")[0]
    if (txt === 'Access granted') {
        label.style.color = "#07ef03";
        }
    if (txt === 'Access denied') {
        label.style.color = "red";
        }
    }
"""

dropdowns = """
() => {
    input_el = document.querySelectorAll(".img-select input");
    input_el[0].type = "button";
    input_el[1].type = "button";
    
    /*
    svg = document.querySelectorAll(".img-select div.icon-wrap");
    ul = document.querySelectorAll(".img-select ul.options);
    for (let i = 0; i < input_el.length; i++){
        input_el[i].addEventListener("click", () => {
            svg[i].style.transform = "rotate(180deg)";
        })
    }*/
}
"""
def refresh():
    image = Image.open(f'data/{file_list[0]}')
    return image, image

with gr.Blocks(css=css, js=dropdowns, elem_classes=['container']) as demo:
    md = gr.Markdown(value="""# Follow the steps
                - To check model performance choose first and second image from available examples.
                - You can pair up images of the same or different fingerprints. The result from model will be automatically calculated.
                - Additionally displayed confidence shows the similarity between images. The closer to 0, the more similar - more confident model
                - Access is granted if value of confidence is below certain threshold found during model testing.""")
    with gr.Row():
        
        with gr.Row():
            drop1 =  gr.Dropdown(value=file_list[0],
                                choices=file_list,
                                label='Select first image',
                                scale=1,
                                elem_classes='img-select',
                                
                                
            )
            
            drop2 = gr.Dropdown(value=file_list[0],
                                choices=file_list,
                                label='Select second image',
                                scale=1,
                                elem_classes='img-select',
                                
            )
            label = gr.Label(value='Scans of the same finger', show_label=False)        
                                           
        with gr.Row():
            img1 = gr.Image(height=288, # unfortunately value doesn't work properly
                            width=256, 
                            interactive=False, 
                            scale=1, 
                            label='first image', 
                            show_download_button=False,
                            show_share_button=False,
                            elem_classes=['my-img'])
             
            img2 = gr.Image(height=288, 
                            width=256, 
                            interactive=False, 
                            scale=1, 
                            label='second image',
                            show_download_button=False,
                            show_share_button=False,
                            elem_classes=['my-img'])
                     
    output = gr.Label(value=predict(*examples[0])[2], elem_id='res', show_label=False)

    drop1.change(fn=predict, inputs=[drop1, drop2], outputs=[img1, img2, output, label])
    drop2.change(fn=predict, inputs=[drop1, drop2], outputs=[img1, img2, output, label])
    output.change(fn=None, inputs=None, js=js)
    # initial img load workaround
    demo.load(fn=refresh, inputs=None, outputs=[img1, img2])
demo.launch()

# %%