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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)
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