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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]]}"
# Gradio Interface
iface = gr.Interface(fn=predict_image, inputs="image", outputs="text")
iface.launch()
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