Delete src/app.py

src/app.py

@@ -1,54 +0,0 @@
-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()