75 lines
1.7 KiB
Python
75 lines
1.7 KiB
Python
from enum import Enum
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import os
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import torch
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from torchvision import transforms
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from fastapi import FastAPI, File, UploadFile
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from fastapi.middleware.cors import CORSMiddleware
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from PIL import Image
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import io
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import torch.nn.functional as F
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from bird_cnn import Bird_CNN
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import threading
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BUILD_PATH = "./build_models"
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IMAGE_SIZE = 64
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sem = threading.Semaphore(1) #adjust to performance
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class bird_species(Enum):
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Common_Kingfisher = 0
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Common_Myna = 1
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House_Crow = 2
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Indian_Peacock = 3
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Indian_Pitta = 4
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Ruddy_Shelduck = 5
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Sarus_Crane = 6
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transform = transforms.Compose([
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transforms.Resize(IMAGE_SIZE),
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transforms.CenterCrop(IMAGE_SIZE),
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transforms.ToTensor()
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])
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = Bird_CNN(c_in=3, c_hidden=16, c_out=7)
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full_path = os.path.join(BUILD_PATH, "bird_cnn")
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model.load_state_dict(torch.load(full_path, map_location=torch.device(device)))
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model.to(device)
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model.eval()
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app = FastAPI()
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origins = [
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"https://marvinkrausser.com",
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"https://api.marvinkrausser.com",
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]
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app.add_middleware(
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CORSMiddleware,
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allow_origins=origins,
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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@app.post("/predict")
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async def predict(file: UploadFile = File(...)):
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with sem:
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image_bytes = await file.read()
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image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
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image = transform(image).unsqueeze(0).to(device)
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with torch.no_grad():
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pred = model(image)
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probs = F.softmax(pred, dim=1)
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confidence, cls = torch.max(probs, dim=1)
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return {
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"class": bird_species(cls.item()).name,
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"confidence": confidence.item()
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} |