File size: 1,596 Bytes
ad4072b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | import fastapi
from fastapi import FastAPI
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image
import io
import base64
app = FastAPI()
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(6 * 14 * 14, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 3)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = x.view(-1, 6 * 14 * 14)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
model = CNN()
model.load_state_dict(torch.load("best_model.pt", map_location=torch.device("cpu")))
model.eval()
data_transforms = transforms.Compose([
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
@app.post("/predict")
async def predict(image_base64: str):
image_bytes = base64.b64decode(image_base64)
image = Image.open(io.BytesIO(image_bytes))
image = data_transforms(image)
image = image.unsqueeze(0)
output = model(image)
_, predicted = torch.max(output, 1)
confidence = torch.nn.functional.softmax(output, dim=1)
return {
"predicted_class": predicted.item(),
"confidence": confidence.tolist()[0]
}
@app.get("/health")
async def health():
return {"status": "ok"}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000) |