app.py

from fastapi import FastAPI, UploadFile, File
import torch
from torchvision import models, transforms
from safetensors.torch import load_file
from huggingface_hub import hf_hub_download
from PIL import Image
import io

app = FastAPI()

LABELS = [
 "No Finding","Enlarged Cardiomediastinum","Cardiomegaly","Lung Opacity",
 "Lung Lesion","Edema","Consolidation","Pneumonia","Atelectasis",
 "Pneumothorax","Pleural Effusion","Pleural Other","Fracture","Support Devices"
]

class DenseNet121_CheXpert(torch.nn.Module):
    def __init__(self, num_labels=14):
        super().__init__()
        self.densenet = models.densenet121(pretrained=False)
        self.densenet.classifier = torch.nn.Linear(
            self.densenet.classifier.in_features, num_labels
        )
    def forward(self, x):
        return self.densenet(x)

# download weights
model_path = hf_hub_download(
    repo_id="itsomk/chexpert-densenet121",
    filename="pytorch_model.safetensors"
)

model = DenseNet121_CheXpert()
state = load_file(model_path)
model.load_state_dict(state, strict=False)
model.eval()

preprocess = transforms.Compose([
    transforms.Resize((224,224)),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485,0.456,0.406],
        std=[0.229,0.224,0.225]
    )
])

@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    img = Image.open(io.BytesIO(await file.read())).convert("RGB")
    x = preprocess(img).unsqueeze(0)

    with torch.no_grad():
        probs = torch.sigmoid(model(x)).squeeze().tolist()

    return dict(zip(LABELS, probs))