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import torch
import gradio as gr
from torchvision import transforms
from PIL import Image
# Load model
class MyModel(torch.nn.Module):
def __init__(self):
super().__init__()
# Define layers here
def forward(self, x):
# Forward pass
return x
model = MyModel()
model.load_state_dict(torch.load("model.pth"))
model.eval()
# Define image preprocessing
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
])
# Define prediction function
def predict(image):
image = transform(image).unsqueeze(0) # Add batch dimension
with torch.no_grad():
output = model(image)
return output.numpy().tolist()
# Create Gradio interface
iface = gr.Interface(fn=predict, inputs=gr.Image(), outputs="json")
iface.launch()
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