app.py

import torch
import torchvision.transforms as transforms
import gradio as gr
from PIL import Image
import torch.nn as nn
import torch.nn.functional as F

def get_model_name(name, batch_size, learning_rate, epoch):
    """ Generate a name for the model consisting of all the hyperparameter values

    Args:
        config: Configuration object containing the hyperparameters
    Returns:
        path: A string with the hyperparameter name and value concatenated
    """
    path = "model_{0}_bs{1}_lr{2}_epoch{3}".format(name,
                                                   batch_size,
                                                   learning_rate,
                                                   epoch)
    return path

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, 8)

    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

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]
])

def load_model():
    net = LargeNet() #small or large network
    model_path = get_model_name(net.name, batch_size=128, learning_rate=0.001, epoch=29)
    state = torch.load(model_path)
    net.load_state_dict(state)
    
    net.eval()
    return net

class_names = ["Gasoline_Can", "Pebbels", "pliers", "Screw_Driver", "Toolbox", "Wrench", "other"]


def predict(image):
    model = load_model()
    image = transform(image).unsqueeze(0)  
    with torch.no_grad():
        output = model(image)
        _, pred = torch.max(output, 1)
    return class_names[pred.item()]

interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs="label",
    title="Mechanical Tools Classifier",
    description="Upload an image to classify it as one of the mechanical tools."
)

if __name__ == "__main__":
    interface.launch()