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from flask import Flask, render_template, request, jsonify
import torch
import torch.nn as nn
from torchvision import transforms
from torchvision.models import vgg16
from PIL import Image
import io
app = Flask(__name__, static_folder='assets')
# Load the model
model = torch.load('pneumonAI_model.pth', map_location=torch.device('cpu'))
model.eval()
# Define class labels
class_to_label = {0: 'Normal', 1: 'Pneumonia'}
# Define the transformation
preprocess = transforms.Compose([
transforms.Grayscale(num_output_channels=3),
transforms.Resize((224, 224)), # Adjusted to 224 for the model
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
@app.route('/')
def home():
return render_template('home.html')
@app.route('/about')
def about():
return render_template('about.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'})
# Load the image
img = Image.open(io.BytesIO(file.read()))
# Preprocess the image
img_tensor = preprocess(img).unsqueeze(0) # Add batch dimension
# Make the prediction
with torch.no_grad():
output = model(img_tensor)
# Apply softmax to get confidence scores (probabilities)
probabilities = torch.nn.functional.softmax(output, dim=1)
# Get the predicted class label and its confidence
predicted_class = torch.argmax(probabilities, 1).item()
confidence = probabilities[0][predicted_class].item()
return jsonify({'prediction': class_to_label[predicted_class], 'confidence': confidence * 100})
if __name__ == '__main__':
app.run(debug=True)
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