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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(host="0.0.0.0", port=7860)
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