import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image
import cv2
import os

# Define LeNet-5 CNN architecture
class LeNet5(nn.Module):
    def __init__(self):
        super(LeNet5, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=2)
        self.relu = nn.ReLU()
        self.pool = nn.AvgPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = self.pool(self.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# ImageClassifier wrapper
class ImageClassifier:
    def __init__(self, model_path):
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.model = LeNet5().to(self.device)
        self.model.load_state_dict(torch.load(model_path, map_location=self.device))
        self.model.eval()

        self.transform = transforms.Compose([
            transforms.Grayscale(num_output_channels=1),
            transforms.Resize((28, 28)),
            transforms.ToTensor(),
            transforms.Normalize((0.5,), (0.5,))
        ])

    def preprocess_image(self, image_path):
        image = Image.open(image_path).convert("RGB")
        image = self.transform(image)
        image = image.unsqueeze(0)  # Add batch dimension
        return image.to(self.device)

    def predict(self, image_path):
        image_tensor = self.preprocess_image(image_path)
        with torch.no_grad():
            output = self.model(image_tensor)
            _, predicted = output.max(1)
        return predicted.item(), image_path