Create app.py

app.py

@@ -0,0 +1,76 @@
+import io
+from flask import Flask, render_template, request, jsonify
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+import torch.nn.functional as F
+import torch.nn as nn
+
+num_classes = 10
+
+# Class definition for the model (same as in your code)
+class FingerprintRecognitionModel(nn.Module):
+    def __init__(self, num_classes):
+        super(FingerprintRecognitionModel, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
+        self.fc1 = nn.Linear(128 * 28 * 28, 256)
+        self.fc2 = nn.Linear(256, num_classes)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = self.pool(F.relu(self.conv3(x)))
+        x = x.view(-1, 128 * 28 * 28)
+        x = F.relu(self.fc1(x))
+        x = F.softmax(self.fc2(x), dim=1)
+        return x
+
+app = Flask(__name__)
+
+# Load the model
+model_path = 'fingerprint_recognition_model_bs32_lr0.001_opt_Adam.pt'
+model = FingerprintRecognitionModel(num_classes)
+model.load_state_dict(torch.load(model_path))
+model.eval()
+
+def preprocess_image(image_bytes):
+    # Convert bytes to PIL Image
+    image = Image.open(io.BytesIO(image_bytes)).convert('L')  # Convert to grayscale
+
+    # Resize to 224x224
+    img_resized = image.resize((224, 224))
+
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.5,), (0.5,))
+    ])
+
+    # Apply transforms and add batch dimension
+    img_tensor = transform(img_resized).unsqueeze(0)
+
+    return img_tensor
+
+def predict_class(image_bytes):
+    img_tensor = preprocess_image(image_bytes)
+    with torch.no_grad():
+        outputs = model(img_tensor)
+        _, predicted = torch.max(outputs.data, 1)
+        predicted_class = int(predicted.item())
+    return predicted_class
+
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    if request.method == 'POST':
+        file = request.files['file']
+        if file:
+            contents = file.read()
+            predicted_class = predict_class(contents)
+            class_labels = {0:'left_index_fingers',1:'left_little_fingers',2:'left_middle_fingers',3: 'left_ring_fingers', 4:'left_thumb_fingers',5:'right_index_fingers',6:'right_little_fingers',7:'right_middle_fingers',8:'right_ring_fingers',9: 'right_thumb_fingers'}
+            return jsonify({'predicted_class': predicted_class, 'class_label': class_labels[predicted_class]})
+    return render_template('index.html')
+
+if __name__ == '__main__':
+    app.run(debug=True)