gradio app

app.py

@@ -1,55 +0,0 @@
-import torch
-from torchvision.transforms import v2
-import gradio
-from PIL import Image
-from mae_model.models.linear_predictor import Predictor
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-LABELS = [
-    "adipose",
-    "background",
-    "debris",
-    "lymphocytes",
-    "mucus",
-    "smooth muscle",
-    "normal colon mucosa",
-    "cancer-associated stroma",
-    "colorectal adenocarcinoma epithelium",
-]
-
-
-model = Predictor(n_labels=len(LABELS))
-model.load_state_dict(torch.load("checkpoints/model_linear_v2_epoch_100.pt"))
-model.to(device)
-model.eval()
-
-
-tf = v2.Compose([
-    v2.ToImage(),
-    v2.ToDtype(torch.float32, scale=True),
-])
-
-def predict(image):
-    if image is None:
-        return None
-
-    pil_img = Image.fromarray(image.astype('uint8'), 'RGB')
-    img_tensor = tf(pil_img).unsqueeze(0).to(device)
-
-    with torch.no_grad():
-        outputs = model(img_tensor)
-        probabilities = torch.nn.functional.softmax(outputs[0], dim=0)
-    
-    return {LABELS[i]: float(probabilities[i]) for i in range(len(LABELS))}
-
-demo = gradio.Interface(
-    fn=predict,
-    inputs=gradio.Image(), 
-    outputs=gradio.Label(num_top_classes=3),
-    title="OrganMNIST Image Classification",
-    description="Upload a tissue patch image for classification"
-)
-
-if __name__ == "__main__":
-    demo.launch()

requirements.txt

@@ -1,5 +0,0 @@
-gradio
-torch
-torchvision
-pillow
-git+https://github.com/HasanAli5/MAE-Model-MedMNIST-Predictor.git#egg=mae_model