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import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image
import timm
import os
class EfficientNetB0Alpha(nn.Module):
def __init__(self, num_classes=26):
super().__init__()
self.model = timm.create_model('efficientnet_b0', pretrained=False, in_chans=1, num_classes=num_classes)
def forward(self, x):
return self.model(x)
# Load model and class names once
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
checkpoint_path = 'saved_models/best_model.pth'
num_classes = 26
transform = transforms.Compose([
transforms.Grayscale(num_output_channels=1),
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5], std=[0.5])
])
model = EfficientNetB0Alpha(num_classes=num_classes).to(device)
if not os.path.exists(checkpoint_path):
raise FileNotFoundError(f"Checkpoint not found at {checkpoint_path}")
checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=True)
model.load_state_dict(checkpoint['model_state_dict'])
class_names = checkpoint['class_names']
def predict_from_image(image_path):
img = Image.open(image_path).convert('L')
img = transform(img)
img = img.unsqueeze(0).to(device)
model.eval()
with torch.no_grad():
outputs = model(img)
probabilities = torch.softmax(outputs, dim=1)
confidence, predicted = torch.max(probabilities, 1)
predicted_class = class_names[predicted.item()]
confidence = confidence.item()
return predicted_class, confidence |