Upload 3 files

appf.py

@@ -0,0 +1,80 @@
+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)

facemask_detection_model_f1.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7258119cb55a3a49bf18f5d4034690009205313cf309810c8a51cf109d5af1ce
+size 51776804

requirements.txt

@@ -0,0 +1,4 @@
+torch
+torchvision
+flask
+numpy