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import torch
import torchvision.transforms as transforms
from torchvision import models
import torch.nn as nn
from PIL import Image
import json
# Load class names
with open('class_names.json', 'r') as f:
class_names = json.load(f)
# Define model
def load_model():
model = models.resnet50(pretrained=False)
model.fc = nn.Linear(model.fc.in_features, len(class_names))
checkpoint = torch.load('reptile_classifier.pth', map_location=torch.device('cpu'))
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
return model
model = load_model()
# Image preprocessing
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# Prediction function
def predict(image: Image.Image):
image = transform(image).unsqueeze(0)
with torch.no_grad():
outputs = model(image)
probabilities = torch.nn.functional.softmax(outputs[0], dim=0)
top3_prob, top3_indices = torch.topk(probabilities, 3)
return {class_names[idx]: float(prob) for idx, prob in zip(top3_indices, top3_prob)}
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