import os
import csv
import torch
import torchvision.transforms as transforms
from PIL import Image
import math
from collections import OrderedDict
import torch.nn as nn

# Custom ResNet class based on provided training script
def conv3x3(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)

class BasicBlock(nn.Module):
    expansion = 1
    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        m = OrderedDict()
        m['conv1'] = conv3x3(inplanes, planes, stride)
        m['bn1'] = nn.BatchNorm2d(planes)
        m['relu1'] = nn.ReLU(inplace=True)
        m['conv2'] = conv3x3(planes, planes)
        m['bn2'] = nn.BatchNorm2d(planes)
        self.group1 = nn.Sequential(m)
        self.relu = nn.Sequential(nn.ReLU(inplace=True))
        self.downsample = downsample

    def forward(self, x):
        if self.downsample is not None:
            residual = self.downsample(x)
        else:
            residual = x
        out = self.group1(x) + residual
        out = self.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1):
        self.inplanes = 64
        super(ResNet, self).__init__()
        m = OrderedDict()
        m['conv1'] = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        m['bn1'] = nn.BatchNorm2d(64)
        m['relu1'] = nn.ReLU(inplace=True)
        m['maxpool'] = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.group1 = nn.Sequential(m)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.Sequential(nn.AvgPool2d(7))
        self.group2 = nn.Sequential(
            OrderedDict([
                ('fullyconnected', nn.Linear(512 * block.expansion, num_classes))
            ])
        )
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                torch.nn.init.xavier_uniform_(m.weight.data)
                torch.nn.init.constant_(m.bias.data, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )
        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.group1(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.group2(x)
        return x

# Define the path to the model and the images
model_path = './pytorch-models/resnet18.pth'
image_dir = './face_seq'

# Define the transform to be applied to the images
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# Load the custom model
model = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=1)

# Load the checkpoint
checkpoint = torch.load(model_path, map_location=torch.device('cpu'), encoding='latin1')
state_dict = checkpoint['state_dict']

# Adjust the keys in state_dict if necessary
new_state_dict = {}
for k, v in state_dict.items():
    if k.startswith('module.'):
        new_state_dict[k[7:]] = v  # Remove 'module.' prefix
    else:
        new_state_dict[k] = v

# Load the state dict into the model
model.load_state_dict(new_state_dict)
model.eval()

# Function to process a single image and get the model's output
def process_image(image_path):
    image = Image.open(image_path).convert('RGB')
    image = transform(image)
    image = image.unsqueeze(0)  # Add batch dimension
    with torch.no_grad():
        output = model(image)
    return output.item()

# List to store the results
results = []

# Process each image in the directory
for filename in os.listdir(image_dir):
    if filename.endswith('.jpg') or filename.endswith('.png'):
        image_path = os.path.join(image_dir, filename)
        attractiveness_score = process_image(image_path)
        results.append([filename, attractiveness_score])

# Save the results to a CSV file
output_csv = 'attractiveness_scores.csv'
with open(output_csv, mode='w', newline='') as file:
    writer = csv.writer(file)
    writer.writerow(['filename', 'attractiveness_score'])
    writer.writerows(results)

print(f"Results saved to {output_csv}")