|
import gradio as gr |
|
from tensorflow.keras.models import load_model |
|
import numpy as np |
|
import tensorflow as tf |
|
|
|
model = load_model("skin_cancer_detection_model.h5") |
|
|
|
def predict_image(image): |
|
img = tf.image.resize(image, (224, 224)) |
|
img = np.expand_dims(img, axis=0) |
|
img = img / 255.0 |
|
|
|
prediction = model.predict(img) |
|
predicted_class = np.argmax(prediction, axis=1)[0] |
|
|
|
class_names = ['akiec', 'bcc', 'bkl', 'df', 'nv', 'vasc', 'mel'] |
|
disease_info = { |
|
'akiec': "Actinic Keratoses and Intraepithelial Carcinoma (pre-cancerous lesion)", |
|
'bcc': "Basal Cell Carcinoma (a common type of skin cancer)", |
|
'bkl': "Benign Keratosis (non-cancerous lesion)", |
|
'df': "Dermatofibroma (benign skin lesion)", |
|
'nv': "Melanocytic Nevus (a common mole)", |
|
'vasc': "Vascular Lesions (benign lesion of blood vessels)", |
|
'mel': "Melanoma (most dangerous type of skin cancer)" |
|
} |
|
|
|
return f"{class_names[predicted_class]}: {disease_info[class_names[predicted_class]]}" |
|
|
|
|
|
iface = gr.Interface(fn=predict_image, inputs="image", outputs="text") |
|
iface.launch() |
|
|
