app.py

import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image
import gradio as gr

# Load your resnet18 model from Hugging Face
model = models.resnet18()
model.fc = nn.Linear(model.fc.in_features, 4)  # Assuming 4 classes
checkpoint = torch.hub.load_state_dict_from_url(
    'https://huggingface.co/wandikafp/resnet18-tom-and-jerry-classifier/resolve/main/pytorch_model.bin',
    map_location=torch.device('cpu')
)
model.load_state_dict(checkpoint)
model.eval()

# Define image transformations
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# Define a prediction function
def classify_image(image):
    image = Image.fromarray(image)  # Convert to PIL image
    image = transform(image).unsqueeze(0)  # Preprocess the image

    with torch.no_grad():
        outputs = model(image)
        _, predicted = torch.max(outputs, 1)

    labels = ['tom', 'jerry', 'tom_jerry_0', 'tom_jerry_1']
    return labels[predicted.item()]

# Create Gradio interface
interface = gr.Interface(
    fn=classify_image, 
    inputs="image", 
    outputs="label",
    title="Tom and Jerry Classifier",
    description="Classify images as 'tom', 'jerry', 'tom_jerry_0', or 'tom_jerry_1'."
)

# Launch the Gradio app
interface.launch()