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import torch
from torchvision import transforms
import gradio as gr
import torch
from efficientnet_pytorch import EfficientNet
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = EfficientNet.from_name('efficientnet-b0')
in_features = model._fc.in_features
model._fc = torch.nn.Linear(in_features, 2)
model.load_state_dict(torch.load('model_transfer.pt', map_location=torch.device('cpu')))
model.to(device)
model.eval()
labels = ["Organic Waste","Recyclable Waste"]
def predict(inp):
inp = transforms.ToTensor()(inp).unsqueeze(0)
inp = inp.to(device)
with torch.no_grad():
prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
confidences = {labels[i]: float(prediction[i]) for i in range(len(prediction))}
return confidences
gr.Interface(
fn=predict,
inputs=gr.components.Image(type="pil"),
outputs=gr.components.Label(num_top_classes=2),
examples=["tissue.jpg", "carrots.jpg"],
theme="default",
css=".footer{display:none !important}"
).launch()
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