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import gradio as gr
import torch
import torchvision.transforms as transforms
from PIL import Image
import os
class CatDogClassifier:
def __init__(self, model_path="model.pt"):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Load the traced model
self.model = torch.jit.load(model_path)
self.model = self.model.to(self.device)
self.model.eval()
# Define the same transforms used during training/testing
self.transform = transforms.Compose([
transforms.Resize((160, 160)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
# Class labels
self.labels = ['Dog', 'Cat']
@torch.no_grad()
def predict(self, image):
if image is None:
return None
# Convert to PIL Image if needed
if not isinstance(image, Image.Image):
image = Image.fromarray(image).convert('RGB')
# Preprocess image
img_tensor = self.transform(image).unsqueeze(0).to(self.device)
# Get prediction
output = self.model(img_tensor)
probabilities = torch.nn.functional.softmax(output[0], dim=0)
# Create prediction dictionary
return {
self.labels[idx]: float(prob)
for idx, prob in enumerate(probabilities)
}
# Create classifier instance
classifier = CatDogClassifier()
# Create Gradio interface
demo = gr.Interface(
fn=classifier.predict,
inputs=gr.Image(),
outputs=gr.Label(num_top_classes=2),
title="Cat vs Dog Classifier",
description="Upload an image to classify whether it's a cat or a dog",
examples=[
["examples/cat.jpg"],
["examples/dog.jpg"]
]
)
if __name__ == "__main__":
demo.launch()
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