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import timm
import torch
from PIL import Image
from torchvision import transforms
import requests
from io import BytesIO
def load_model():
"""Load the pre-trained model."""
model = timm.create_model("hf_hub:timm/vit_large_patch14_clip_336.laion2b_ft_in12k_in1k_inat21", pretrained=True)
model.eval()
return model
def get_label_names():
"""Fetch the class labels from the Hugging Face Hub."""
config_url = "https://huggingface.co/timm/vit_large_patch14_clip_336.laion2b_ft_in12k_in1k_inat21/resolve/main/config.json"
response = requests.get(config_url)
response.raise_for_status()
config = response.json()
return config["label_names"]
def preprocess_image(image_url):
"""Fetch and preprocess the image."""
preprocess = transforms.Compose([
transforms.Resize(336),
transforms.CenterCrop(336),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
response = requests.get(image_url)
response.raise_for_status()
image = Image.open(BytesIO(response.content))
input_tensor = preprocess(image).unsqueeze(0) # Add a batch dimension
return input_tensor
def predict_species(model, image_url, label_names):
"""Make a prediction using the model."""
input_tensor = preprocess_image(image_url)
# Move to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
input_tensor = input_tensor.to(device)
# Make prediction
with torch.no_grad():
output = model(input_tensor)
_, predicted_class = torch.max(output, 1)
# Map prediction to species
predicted_species = label_names[predicted_class.item()]
return predicted_species
#finish |