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c1d1ecf 8ac53ea c1d1ecf 8d17e1e c1d1ecf 8ac53ea c1d1ecf 8ac53ea c1d1ecf 8ac53ea c1d1ecf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | import numpy as np
import gradio as gr
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
class LargeNet(nn.Module):
def __init__(self):
super(LargeNet, self).__init__()
self.name = "large"
self.conv1 = nn.Conv2d(3, 5, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(5, 10, 5)
self.fc1 = nn.Linear(10 * 29 * 29, 32)
self.fc2 = nn.Linear(32, 7)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 10 * 29 * 29)
x = F.relu(self.fc1(x))
x = self.fc2(x)
x = x.squeeze(1) # Flatten to [batch_size]
return x
LABELS = ['battery', 'biological', 'cardboard', 'glass', 'metal', 'paper', 'plastic']
# Load best model
net = LargeNet()
net.load_state_dict(torch.load("net.pth", map_location=torch.device('cpu')))
net.eval()
# Transform uploaded images
transform = transforms.Compose([
transforms.Resize((128, 128)), # Resize to 128x128
transforms.ToTensor(), # Convert to Tensor
transforms.Normalize((0.5,), (0.5,)) # Normalize to [-1, 1]
])
# Make predictions on uploaded images
def predict(img):
img_t = transform(img).unsqueeze(0)
with torch.no_grad():
output = net(img_t)
probs = F.softmax(output[0], dim=0)
return {label: float(prob) for label, prob in zip(LABELS, probs)}
# Create interface for uploading image and showing predictions
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=7),
title="Garbage Classifier",
description="Classify garbage images into Battery, Biological, Cardboard, Glass', Metal, Paper, or Plastic"
)
if __name__ == "__main__":
iface.launch(share=True)
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