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bfb7b0d cc2a7fc 277afd2 bfb7b0d cc2a7fc bfb7b0d cc2a7fc bfb7b0d | 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 | import torch
import torchvision.transforms as transforms
from PIL import Image
import gradio as gr
from timm import create_model
import torch.nn as nn
import os
class VisionTransformer(nn.Module):
def __init__(self, num_classes, model_name):
super(VisionTransformer, self).__init__()
self.model = create_model(model_name, pretrained=False, num_classes=num_classes)
self.model.head = nn.Sequential(
nn.Linear(self.model.num_features, 512),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(512, num_classes)
)
def forward(self, x):
return self.model(x)
model_path = "./models/vit_small_patch16_224_final.pth"
device = torch.device("cpu")
model = VisionTransformer(num_classes=2, model_name="vit_small_patch16_224")
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5]*3, std=[0.5]*3)
])
# ✅ Define classify_image first
def classify_image(img: Image.Image):
img_tensor = transform(img).unsqueeze(0)
with torch.no_grad():
outputs = model(img_tensor)
_, predicted = torch.max(outputs, 1)
label = 'Fake' if predicted.item() == 0 else 'Real'
return label
# ✅ Then wrap it in predict()
def predict(img: Image.Image):
return classify_image(img)
# ✅ All set to go
gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs="label",
title="Luxury Item Authenticity Detector",
description="Upload an image to check if it's a real or fake item."
).launch()
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