app.py

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

transform_test = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

class_names = [
    'Auto Rickshaws', 'Bikes', 'Cars', 'Motorcycles',
    'Planes', 'Ships', 'Trains'
]
class VehicleClassifier(nn.Module):
    def __init__(self):
        super(VehicleClassifier, self).__init__()
        
        # Convolutional Layers
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        
        # Pooling Layer
        self.pool = nn.MaxPool2d(2, 2)
        
        # FC Layers
        self.fc1 = nn.Linear(256 * 14 * 14, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, 7)  # 7 classes for the 7 vehicle categories
        
        self.dropout = nn.Dropout(0.5)
        
    def forward(self, x):
        # Apply Convolutional Layers with ReLU activation and Pooling
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = self.pool(F.relu(self.conv3(x)))
        x = self.pool(F.relu(self.conv4(x)))
        
        # Flatten the tensor before feeding into the FCL
        x = x.view(-1, 256 * 14 * 14)
        x = F.relu(self.fc1(x))
        x = self.dropout(x)
        x = F.relu(self.fc2(x))
        x = self.dropout(x)
        x = self.fc3(x)
        return x
model = VehicleClassifier().to('cpu') 
model.load_state_dict(torch.load('vehicle_classifier.pth', map_location=torch.device('cpu')))
model.eval()  

def predict(image):
    try:
        image = Image.open(image).convert('RGB')
        image = transform_test(image).unsqueeze(0)  # Add batch dimension
        
        print(f"Image shape after transformation: {image.shape}")
        
        with torch.no_grad():
            outputs = model(image)
            print(f"Model output: {outputs}")
            _, predicted = torch.max(outputs, 1)
        
        prediction = class_names[predicted.item()]
        print(f"Predicted class: {prediction}")
        
        return prediction
    except Exception as e:
        print(f"Error during prediction: {e}")
        traceback.print_exc()
        return "An error occurred during prediction."


interface = gr.Interface(
    fn=predict, 
    inputs=gr.Image(type='filepath'),  
    outputs=gr.Label(num_top_classes=1),  
    title="Vehicle Classification",
    description="Upload an image of a vehicle, and the model will predict its type."
)

# Launch the interface
interface.launch(share=True)