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okeowo1014
commited on
Commit
•
4597f1c
1
Parent(s):
d09aad8
Create main.py
Browse files
main.py
ADDED
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import io
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from fastapi import FastAPI, File, UploadFile
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import torch
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import torchvision.transforms as transforms
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import cv2
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import numpy as np
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from PIL import Image
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import torch.nn.functional as F
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import torch.nn as nn
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num_classes = 10
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# Class definition for the model (same as in your code)
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class FingerprintRecognitionModel(nn.Module):
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def __init__(self, num_classes):
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super(FingerprintRecognitionModel, self).__init__()
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self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
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self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
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self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
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self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
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self.fc1 = nn.Linear(128 * 28 * 28, 256)
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self.fc2 = nn.Linear(256, num_classes)
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def forward(self, x):
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x = self.pool(F.relu(self.conv1(x)))
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x = self.pool(F.relu(self.conv2(x)))
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x = self.pool(F.relu(self.conv3(x)))
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x = x.view(-1, 128 * 28 * 28)
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x = F.relu(self.fc1(x))
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x = F.softmax(self.fc2(x), dim=1)
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return x
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app = FastAPI()
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# Load the model
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model_path = 'fingerprint_recognition_model.pt'
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model = FingerprintRecognitionModel(num_classes)
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model.load_state_dict(torch.load(model_path))
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model.eval()
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def preprocess_image(image_bytes):
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# Convert bytes to PIL Image
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image = Image.open(io.BytesIO(image_bytes)).convert('L') # Convert to grayscale
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# Resize to 224x224
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img_resized = image.resize((224, 224))
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transform = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.5,), (0.5,))
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])
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# Apply transforms and add batch dimension
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img_tensor = transform(img_resized).unsqueeze(0)
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return img_tensor
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def predict_class(image_bytes):
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img_tensor = preprocess_image(image_bytes)
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with torch.no_grad():
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outputs = model(img_tensor)
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_, predicted = torch.max(outputs.data, 1)
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predicted_class = int(predicted.item())
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return predicted_class
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@app.post("/predict/")
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async def predict_endpoint(file: UploadFile = File(...)):
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contents = await file.read()
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predicted_class = predict_class(contents)
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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'}
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return {"predicted_class": predicted_class, "class_label": class_labels[predicted_class]}
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