Spaces:
Build error
Build error
File size: 2,154 Bytes
76fcfbc ad0d661 76fcfbc ad0d661 76fcfbc ad0d661 76fcfbc d260a1c eee1b26 76fcfbc ad0d661 76fcfbc fc938e9 76fcfbc d260a1c 602f825 76fcfbc fc938e9 76fcfbc 602f825 76fcfbc ad0d661 a5f83aa eee1b26 ad0d661 76fcfbc d260a1c ad0d661 eee1b26 76fcfbc ad0d661 |
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 |
import os
import codecs
import tensorflow as tf
import keras.backend.tensorflow_backend as tb
import numpy as np
import gradio as gr
import cv2
from PIL import Image
from tensorflow.keras.models import load_model
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
tb._SYMBOLIC_SCOPE.value = True
# Get model weights
os.system(
"wget https://github.com/hasibzunair/adversarial-lesions/releases/latest/download/MelaNet.h5"
)
# Load model
model = None
model = load_model("MelaNet.h5", compile=False)
model.summary()
# Path to examples and class label list
examples = ["benign.png", "malignant.png"]
labels = ["Benign", "Malignant"]
# Helpers
def preprocess_image(img_array):
# Normalize to [0,1]
img_array = img_array.astype("float32")
img_array /= 255
# Check that images are 2D arrays
if len(img_array.shape) > 2:
img_array = img_array[:, :, 0]
# Convert to 3-channel
img_array = np.stack((img_array, img_array, img_array), axis=-1)
# Convert to array
img_array = cv2.resize(img_array, (256, 256))
return img_array
# Main inference function
def inference(img):
img = preprocess_image(img)
img = np.expand_dims(img, 0)
preds = model.predict(img)
# Predict
preds = model.predict(img)[0]
labels_probs = {labels[i]: float(preds[i]) for i, _ in enumerate(labels)}
return labels_probs
title = "Melanoma Detection using Adversarial Training and Deep Transfer Learning"
description = "This model predicts if the given image has benign or malignant symptoms. To use it, simply upload a skin lesion image, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2004.06824' target='_blank'>Melanoma Detection using Adversarial Training and Deep Transfer Learning</a> | <a href='https://github.com/hasibzunair/adversarial-lesions' target='_blank'>Github</a></p>"
demo = gr.Interface(
fn=inference,
title=title,
description=description,
article=article,
inputs="image",
outputs="label",
examples=examples,
)
demo.launch(
# debug=True,
# enable_queue=True
)
|