New app.py

app.py

@@ -0,0 +1,54 @@
+import torch
+import gradio as gr
+from PIL import Image
+from src.model import get_model, apply_weights, copy_weight
+from src.transform import crop, pad, gpu_crop
+from torchvision.transforms import Normalize, ToTensor
+from pathlib import Path
+
+vocab = [
+    "Actinic Keratosis",
+    "Basal Cell Carcinoma",
+    "Benign Keratosis",
+    "Dermatofibroma",
+    "Melanoma",
+    "Melanocytic Nevus",
+    "Vascular Lesion",
+]
+
+
+model = get_model()
+state = torch.load("exported_model.pth", map_location="cpu")
+apply_weights(model, state, copy_weight)
+
+to_tensor = ToTensor()
+norm = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+
+def classify_image(inp):
+    inp = Image.fromarray(inp)
+    transformed_input = pad(crop(inp, (460, 460)), (460, 460))
+    transformed_input = to_tensor(transformed_input).unsqueeze(0)
+    transformed_input = gpu_crop(transformed_input, (224, 224))
+    transformed_input = norm(transformed_input)
+    model.eval()
+    with torch.no_grad():
+        pred = model(transformed_input)
+    prob = torch.softmax(pred[0], dim=0)
+    confidences = {vocab[i]: float(prob[i]) for i in range(7)}
+    return confidences
+
+
+iface = gr.Interface(
+    fn=classify_image,
+    inputs="image",
+    outputs=gr.Label(),
+    examples=[
+        ["ISIC_0024634_00.jpg"],
+        ["ISIC_0032932_00.jpg"],
+    ],
+    title="Skin Lesion Recognition using fast.ai",
+    description="Adapted from https://domingomery.ing.puc.cl/",
+    article="<p style='text-align: center'><a href='https://evertoncolombo.github.io/blog/posts/skin-lesion/Skin%20Lesion%20Recognition%20using%20fastai.html'>More info | <a href='https://www.dropbox.com/s/nzrvuoos7sgl5dh/exp4val.zip' >Dataset</a> <center><img src='https://visitor-badge.glitch.me/badge?page_id=e_colombo_skin_lesion' alt='visitor badge'></center></p>",
+    allow_flagging="never",
+).launch()