Create main.py

main.py

@@ -0,0 +1,71 @@
+import io
+from fastapi import FastAPI, File, UploadFile
+import torch
+import torchvision.transforms as transforms
+import cv2
+import numpy as np
+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]}
+