Spaces:
Runtime error
Runtime error
File size: 3,389 Bytes
b04d91a ff0053b e29a08c ff0053b b04d91a | 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 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | import os, sys, torch, gradio as gr
from PIL import Image
from torchvision import transforms
from huggingface_hub import hf_hub_download
# Clone PanDerm source saat startup (sekali saja di container)
PANDERM_DIR = "/tmp/PanDerm"
if not os.path.exists(PANDERM_DIR):
os.system(f"git clone https://github.com/SiyuanYan1/PanDerm.git {PANDERM_DIR}")
sys.path.insert(0, os.path.join(PANDERM_DIR, "classification"))
sys.path.insert(0, os.path.join(PANDERM_DIR, "classification", "models"))
import importlib.util
_spec = importlib.util.spec_from_file_location(
"modeling_finetune",
os.path.join(PANDERM_DIR, "classification", "models", "modeling_finetune.py"),
)
_mod = importlib.util.module_from_spec(_spec)
sys.modules["modeling_finetune"] = _mod # ← TAMBAHKAN baris ini
_spec.loader.exec_module(_mod)
from timm.models import create_model
CLASSES = ["akiec", "bcc", "bkl", "df", "mel", "nv", "vasc"]
LABELS = {
"akiec": "Actinic Keratosis (suspicious)",
"bcc": "Basal Cell Carcinoma (malignant)",
"bkl": "Benign Keratosis (low risk)",
"df": "Dermatofibroma (low risk)",
"mel": "Melanoma (malignant)",
"nv": "Melanocytic Nevus (low risk)",
"vasc": "Vascular Lesion (low risk)",
}
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
# Download checkpoint dari model repo
ckpt_path = hf_hub_download(
repo_id="farelfebryan/panderm-ham10000",
filename="checkpoint-best.pth",
)
# Build model persis seperti training Anda
model = create_model(
"PanDerm_Large_FT",
pretrained=False,
num_classes=7,
drop_path_rate=0.2,
use_mean_pooling=True,
init_scale=0.001,
use_rel_pos_bias=False,
use_abs_pos_emb=True,
init_values=0.1,
sin_pos_emb=True,
)
ckpt = torch.load(ckpt_path, map_location=device, weights_only=False)
state = ckpt.get("model", ckpt.get("state_dict", ckpt))
state = {k.replace("module.", ""): v for k, v in state.items()}
missing, unexpected = model.load_state_dict(state, strict=False)
print(f"Loaded checkpoint — missing: {len(missing)}, unexpected: {len(unexpected)}")
if missing: print(" missing examples:", missing[:5])
if unexpected: print(" unexpected examples:", unexpected[:5])
model.to(device).eval()
# Transform sesuai training: img_size=224, imagenet mean/std
tfm = transforms.Compose([
transforms.Resize(256, interpolation=transforms.InterpolationMode.BICUBIC),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
@torch.no_grad()
def predict(img: Image.Image):
if img is None:
return {}
x = tfm(img.convert("RGB")).unsqueeze(0).to(device)
logits = model(x)
probs = torch.softmax(logits, dim=1)[0].cpu().tolist()
return {LABELS[CLASSES[i]]: float(probs[i]) for i in range(7)}
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil", label="Upload dermoscopy image"),
outputs=gr.Label(num_top_classes=3, label="Prediction"),
title="PanDerm Skin Lesion Classifier (HAM10000)",
description=(
"7-class skin lesion classifier based on PanDerm-Large, fine-tuned on "
"HAM10000 with SDXL DoRA synthetic augmentation. "
"Classes: akiec, bcc, bkl, df, mel, nv, vasc."
),
examples=None,
flagging_mode="never",
)
if __name__ == "__main__":
demo.launch() |