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from flask import Flask, request, jsonify, render_template, send_file
from werkzeug.utils import secure_filename
import torch
from torchvision import transforms
import tensorflow as tf
from PIL import Image
import numpy as np
import io
import base64
import cv2
from model import tumor_model
# Initialize Flask app
app = Flask(__name__)
# Define the model paths
CLASSIFICATION_MODEL_PATH = 'models\\tumor_model_statedict_f.pth'
SEGMENTATION_MODEL_PATH = 'models\\unet_model.h5'
# Load the models
class MultiTaskModelWrapper:
def __init__(self):
self.segmentation_model = self.load_segmentation_model()
self.classification_model = self.load_classification_model()
def load_segmentation_model(self):
# Load the pre-trained U-Net model
model = tf.keras.models.load_model(SEGMENTATION_MODEL_PATH, custom_objects={'conv2d_transpose': tf.keras.layers.Conv2DTranspose})
return model
def load_classification_model(self):
# Load the pre-trained Classification model
tumor_model.load_state_dict(torch.load(CLASSIFICATION_MODEL_PATH, map_location=torch.device('cpu')))
tumor_model.eval()
return tumor_model
def predict(self, image):
# Classification prediction
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor()
])
img_tensor = transform(image).unsqueeze(0)
with torch.no_grad():
classification_output = self.classification_model(img_tensor)
class_probabilities = torch.nn.functional.softmax(classification_output, dim=1)
class_label = torch.argmax(class_probabilities).item()
probability = class_probabilities[0, class_label].item()
class_names = {
0: 'Glioma Tumor',
1: 'Meningioma Tumor',
2: 'No Tumor',
3: 'Pituitary Tumor'
}
# Segmentation prediction
img_array = np.array(image.resize((128, 128)))
img_array = np.expand_dims(img_array, axis=0) / 255.0
segmentation_output = self.segmentation_model.predict(img_array)
segmentation_mask = (segmentation_output > 0.5).astype(np.uint8)[0, :, :, 0] * 255
# Convert segmentation mask to base64
mask_image = Image.fromarray(segmentation_mask.astype(np.uint8))
buffer = io.BytesIO()
mask_image.save(buffer, format='PNG')
mask_image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
# Convert input image to base64
input_image_buffer = io.BytesIO()
image.save(input_image_buffer, format='PNG')
input_image_base64 = base64.b64encode(input_image_buffer.getvalue()).decode('utf-8')
return input_image_base64, mask_image_base64, class_names[class_label], probability
# Initialize the model wrapper
model_wrapper = MultiTaskModelWrapper()
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
if 'file' not in request.files:
return jsonify({'error': 'No file part'})
file = request.files['file']
if file.filename == '':
return jsonify({'error': 'No selected file'})
try:
image = Image.open(file.stream).convert('RGB')
input_image_base64, mask_image_base64, class_label, probability = model_wrapper.predict(image)
return jsonify({
'input_image': input_image_base64,
'mask_image': mask_image_base64,
'class_label': class_label,
'probability': probability
})
except Exception as e:
return jsonify({'error': str(e)})
if __name__ == '__main__':
app.run(debug=True)
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