main.py

from flask import Flask,request, send_file
import os
import io
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torchvision.datasets import ImageFolder
import torchvision.transforms as transforms
from PIL import Image
import matplotlib.pyplot as plt
from datetime import datetime

app = Flask(__name__)
 
@app.route('/', methods=['GET'])
def dummy_get():
        return "Welcome to Flask App"

@app.route('/upload', methods=['POST'])
def upload_file():
        class CNN_Stage3(nn.Module):
            def __init__(self, in_channels, out_channels):
                super(CNN_Stage3, self).__init__()
                self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=5, stride=1, dilation=2, padding=1)
                self.relu = nn.ReLU()
                self.pool = nn.MaxPool2d(kernel_size=2, stride=1)

            def forward(self, x):
                x = self.conv1(x)
                x = self.relu(x)
                x = self.pool(x)
                x = self.relu(x)
                return x

        class CNN_Stage1(nn.Module):
            def __init__(self, in_channels, out_channels):
                super(CNN_Stage1, self).__init__()
                self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=5, stride=1, padding=1)
                self.relu = nn.ReLU()
                self.pool = nn.MaxPool2d(kernel_size=2, stride=1)

            def forward(self, x):
                x = self.conv1(x)
                x = self.relu(x)
                x = self.pool(x)
                x = self.relu(x)
                return x

        class CNN(nn.Module):
            def __init__(self, num_classes):
                super(CNN, self).__init__()
                self.cnn_stage_1 = CNN_Stage1(3, 6)
                self.cnn_stage_2 = CNN_Stage1(6, 12)
                self.cnn_stage_3 = CNN_Stage3(12, 24)
                self.cnn_stage_4 = CNN_Stage1(24, 48)
                self.cnn_stage_5 = CNN_Stage1(48, 96)
                self.fc1 = nn.Linear(96 * 3 * 3, 64)
                self.fc2 = nn.Linear(64, num_classes)
                self.relu = nn.ReLU()

            def forward(self, x):
                x = self.cnn_stage_1(x)
                x = self.cnn_stage_2(x)
                x = self.cnn_stage_3(x)
                x = self.cnn_stage_4(x)
                x = self.cnn_stage_5(x)
                x = x.view(x.size(0), -1)
                x = self.fc1(x)
                x = self.relu(x)
                x = self.fc2(x)
                return x

        class CustomDataset(Dataset):
            def __init__(self, root_dir, transform=None):
                self.dataset = ImageFolder(root_dir, transform=transform)
                self.classes = self.dataset.classes

            def __len__(self):
                return len(self.dataset)

            def __getitem__(self, idx):
                image, label = self.dataset[idx]
                return image, label

        # Example usage:
        dataset_path = 'aug_data'


        transform = transforms.Compose([
            transforms.Resize((22, 22)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
        ])

        custom_dataset = CustomDataset(root_dir=dataset_path, transform=transform)

        num_classes = len(custom_dataset.classes)
        batch_size = 32

        data_loader = DataLoader(custom_dataset, batch_size=batch_size, shuffle=True)

        model = CNN(num_classes)

        optimizer = optim.Adam(model.parameters(), lr=0.001)

        # Load the model
        
        checkpoint = torch.load("model_cnn_final.pth")
        model.load_state_dict(checkpoint['model_state_dict'])

        # Assuming optimizer was saved in the checkpoint
        optimizer = optim.Adam(model.parameters(), lr=0.001)
        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
        epoch = checkpoint['epoch']
        loss = checkpoint['loss']


        # Print model's parameter names
        for name, param in model.named_parameters():
            print(name)

        if 'file' not in request.files:
            return 'No file part'
        
        file = request.files['file']

           # Generate a unique filename using a timestamp
        timestamp = datetime.now().strftime('%Y%m%d%H%M%S')
        unique_filename = f"{timestamp}_{file.filename}"

        
        file.save(f'uploads/{unique_filename}')
        

        input_image = Image.open(f'uploads/{unique_filename}')
        input_tensor = transform(input_image)
        input_batch = input_tensor.unsqueeze(0) 

        # Use the loaded model to make predictions
        with torch.no_grad():
            output = model(input_batch)

             
        # If the user does not select a file, the browser submits an empty file without a filename
        if file.filename == '':
            return 'No selected file'
        else:
              # Interpret the predictions
            class_names = ['cancer', 'no- cancer']
            _, predicted_class = torch.max(output, 1)
            predicted_label = class_names[predicted_class.item()]
            print(f'The image is classified as: {predicted_label}')

            plt.imshow(input_image)
            # print(f'The image is classified as: {predicted_label}')
            return f'The image is classified as: {predicted_label}'
 
         
 
if __name__ == "__main__":
    app.run(host='0.0.0.0',debug=True, port=5000)