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import gradio as gr
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import load_model
import numpy as np
def predict(image_file):
width, height = 224, 224
img = image.img_to_array(image_file)
img = np.expand_dims(img, axis=0)
img = img / 255
model = load_model("/vgg16_model.h5")
result = model.predict(img)
if result[0][0] >= 0.5:
prediction = "Malignant"
else:
prediction = "Benign"
return prediction
iface = gr.Interface(
fn=predict,
inputs=gr.Image(label="Upload Image"),
outputs=gr.Textbox(label="Predicted Results"),
title="Skin Cancer Prediction",
description="Upload an image containing skin lesion to predict if it is malignant or benign.",
theme="huggingface",
allow_flagging=False,
examples=[
["example.jpg"], # Replace "example.jpg" with actual example image file path
]
)
iface.launch(share=True)
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