import io from fastapi import FastAPI, File, UploadFile 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 = FastAPI() # Load the model model_path = 'fingerprint_recognition_model.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.post("/predict/") async def predict_endpoint(file: UploadFile = File(...)): contents = await file.read() predicted_class = predict_class(contents) class_labels = {0: 'Left_ring_fingers', 1: 'Left_thumb_fingers', 2: 'Right_index_fingers', 3: 'Right_little_fingers', 4: 'Right_middle_fingers', 5: 'Right_ring_fingers', 6: 'Right_thumb_fingers', 7: 'left_index_fingers', 8: 'left_little_fingers', 9: 'left_middle_fingers'} return {"predicted_class": predicted_class, "class_label": class_labels[predicted_class]}