appf.py

from flask import Flask, render_template, request, jsonify
from PIL import Image
import torch
import io
import base64
from torchvision import transforms
from face_mask_detection import FaceMaskDetectionModel
import numpy as np

app = Flask(__name__)

# Load the model
model = FaceMaskDetectionModel()
# Load the state dictionary
model_state_dict = torch.load("models\\facemask_model_statedict1_f.pth", map_location=torch.device('cpu'))
# Load the state dictionary into the model
model.load_state_dict(model_state_dict)
# Set the model to evaluation mode
model.eval()


# Define the pre-processing transform
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor()
])

# Define class labels
class_labels = ['without mask', 'with mask']


@app.route('/')
def index():
    return render_template('index.html')


@app.route('/predict', methods=['POST'])
def predict():
    try:
        # Get the image from the request
        image = request.files['image']
        
        # Pre-process the image
        image_tensor = transform(Image.open(io.BytesIO(image.read())).convert('RGB')).unsqueeze(0)

        # Set the model to evaluation mode
        model.eval()

        # Make a prediction
        with torch.no_grad():
            output = model(image_tensor)
        print("Output: ", output)

        # Convert the output to probabilities using softmax
        probabilities = torch.nn.functional.softmax(output[0], dim=0)
        print("Probabilities: ", probabilities)

        # Get the predicted class
        predicted_class = torch.argmax(probabilities).item()
        print("Predicted: ", predicted_class)

        # Get the probability for the predicted class
        predicted_probability = probabilities[predicted_class].item()

        # Define class labels
        class_labels = ['without mask', 'with mask']

        print(f"Predicted Class: {class_labels[predicted_class]}")
        print(f"Probability: {predicted_probability:.4f}")

        # Return the prediction along with the uploaded image
        image_base64 = base64.b64encode(image.read()).decode('utf-8')
        return jsonify({'prediction': predicted_class, 'image': image_base64})
    except Exception as e:
        return jsonify({'error': str(e)}), 500



if __name__ == '__main__':
    app.run(debug=True)