app.py

import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image
import numpy as np
import gradio as gr
from facenet_pytorch import InceptionResnetV1, MTCNN
from datasets import load_dataset
from tqdm import tqdm
import wikipedia
dataset = load_dataset("tonyassi/celebrity-1000", split="train")
device = 'cuda' if torch.cuda.is_available() else 'cpu'

transform = transforms.Compose([
    transforms.Resize((160, 160)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5,0.5,0.5], std=[0.5,0.5,0.5])
])
class HF_CelebDataset(Dataset):
    def __init__(self, hf_dataset, transform=None):
        self.dataset = hf_dataset
        self.transform = transform

    def __len__(self):
        return len(self.dataset)

    def __getitem__(self, idx):
        img = self.dataset[idx]['image']
        label = self.dataset[idx]['label']
        if img.mode != 'RGB':
            img = img.convert('RGB')
        if self.transform:
            img = self.transform(img)
        return img, label

subset_dataset = HF_CelebDataset(dataset, transform)
loader = DataLoader(subset_dataset, batch_size=64, shuffle=False, num_workers=2)
facenet = InceptionResnetV1(pretrained='vggface2').eval().to(device)
mtcnn = MTCNN(image_size=160, margin=20, device=device)
embeddings, labels = [], []
with torch.no_grad():
    for imgs, lbls in tqdm(loader):
        imgs = imgs.to(device)
        emb = facenet(imgs)
        emb = F.normalize(emb, p=2, dim=1)
        embeddings.append(emb.cpu())
        labels.extend(lbls.numpy())

embeddings = torch.cat(embeddings)
labels = np.array(labels)
unique_labels = np.unique(labels)
avg_embs, celeb_labels, celeb_names = [], [], []
for lbl in unique_labels:
    idxs = np.where(labels == lbl)[0]
    mean_emb = embeddings[idxs].mean(dim=0)
    mean_emb = F.normalize(mean_emb, p=2, dim=0)
    avg_embs.append(mean_emb)
    celeb_labels.append(lbl)
    celeb_names.append(dataset.features['label'].names[lbl])

celeb_embeddings = torch.stack(avg_embs).to(device)
def find_most_similar(user_img, model, celeb_embeddings, celeb_names, top_k=1):
    face = mtcnn(user_img)
    if face is None:
        face = transform(user_img)
    img_tensor = face.unsqueeze(0).to(device)
    with torch.no_grad():
        user_emb = model(img_tensor)
        user_emb = F.normalize(user_emb, p=2, dim=1)
    similarity = torch.matmul(user_emb, celeb_embeddings.T)
    topk_vals, topk_idx = torch.topk(similarity, top_k, dim=1)
    top_labels = [celeb_names[i] for i in topk_idx[0]]
    top_scores = [float(topk_vals[0][i]) for i in range(top_k)]
    return list(zip(top_labels, top_scores))
def get_wikipedia_info(name):
    try:
        summary = wikipedia.summary(name, sentences=3, auto_suggest=False, redirect=True)
        return summary
    except wikipedia.exceptions.DisambiguationError as e:
        return f"Multiple results found for {name}: {e.options[:3]}"
    except wikipedia.exceptions.PageError:
        return f"No information found for {name}."
    except Exception as e:
        return f"Error fetching info: {e}"
def gradio_find(user_img):
    top_result = find_most_similar(user_img, facenet, celeb_embeddings, celeb_names, top_k=1)[0]
    name, score = top_result
    lbl = celeb_labels[celeb_names.index(name)]
    idxs = np.where(labels == lbl)[0]

    result_imgs = []
    for i in idxs[:6]:
        img = dataset[int(i)]['image'].resize((128, 128))
        caption = f"{name}"
        result_imgs.append((img, caption))
    wiki_text = get_wikipedia_info(name)
    return result_imgs, wiki_text
with gr.Blocks() as demo:
    gr.Markdown("## Which Celebrity Do You Look Like?")
    with gr.Row():
        img_input = gr.Image(type="pil", label="Upload your face")
        gallery = gr.Gallery(label="Similar celebrity", columns=3)
    info_box = gr.Textbox(label="Wikipedia Info", lines=10)
    btn = gr.Button("Find Similar")
    btn.click(fn=gradio_find, inputs=img_input, outputs=[gallery, info_box])

gr.close_all()
demo.launch(share=True, inbrowser=True, prevent_thread_lock=True)
if __name__ == "__main__":
    demo.launch()