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import numpy as np
import tensorflow as tf
import gradio as gr
from keras.models import load_model
from tensorflow.keras.preprocessing.image import load_img, img_to_array
# Load the model
model = load_model('brain_tumor.keras')
labels = ['glioma', 'meningioma', 'notumor', 'pituitary']
def load_and_prepare_image(image_path, target_size=(224, 224)):
"""Load and prepare the image for prediction."""
img = load_img(image_path, target_size=target_size)
img_array = img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array /= 255.0 # Normalize the image
return img_array
def predict(image_path):
if image_path is None:
return "Please upload an image.", "ii.PNG"
# Prepare the image
img = load_and_prepare_image(image_path)
# Make the prediction
prediction = model.predict(img)
predicted_class = np.argmax(prediction, axis=1)[0]
predicted_label = labels[predicted_class]
# Creative prediction message
prediction_message = f'This MRI scan suggests the presence of a {predicted_label}.'
return prediction_message, 'ii.PNG' # Returning the image path for display
# Gradio interface setup with enhancements
iface = gr.Interface(
theme=gr.themes.Soft(),
fn=predict,
inputs=gr.Image(type="filepath", label="Upload MRI Image"),
outputs=[
gr.Textbox(label="Prediction", interactive=False, lines=2),
gr.Image(value="ii.PNG", label="Always ready to assist!")
],
title="🧠 Brain Tumor Classification",
description="Upload a brain MRI image for analysis.",
css="""
.gradio-container {
font-family: 'Arial', sans-serif;
background-color: #ddebf7;
border-radius: 10px;
padding: 20px;
}
.gr-button {
background-color: #4CAF50;
color: white;
}
.gr-image {
border: 2px solid #ddd;
border-radius: 10px;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
}
.output-textbox {
font-size: 1.2em;
text-align: center;
color: #333;
}
""",
examples=[["Te-noTr_0002.jpg"], ["Te-meTr_0009.jpg"]],
)
# Launch the interface
iface.launch(share=True) |