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import gradio as gr
import tensorflow as tf
import numpy as np
import os
# Load the model directly from the file path
model = tf.keras.models.load_model("alzimers_model.h5")
class_names = ['MildDemented', 'ModerateDemented', 'NonDemented', 'VeryMildDemented']
class_colors = {
'MildDemented': '#FFD700',
'ModerateDemented': '#FF8C00',
'NonDemented': '#32CD32',
'VeryMildDemented': '#1E90FF'
}
def stage_badges():
badges = ""
for cls in class_names:
badges += f'<span style="display:inline-block;padding:0.4em 1em;margin:0.2em;border-radius:20px;background:{class_colors[cls]};color:white;font-weight:bold;">{cls.replace("Demented", " Demented")}</span> '
return badges
def predict_image(img):
if img is None:
return "Please upload an image first."
img = img.resize((64, 64))
img_array = np.array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
preds = model.predict(img_array)
pred_idx = np.argmax(preds)
pred_class = class_names[pred_idx]
confidence = float(np.max(preds))
color = class_colors[pred_class]
md = f"""
<div style="max-width:480px;margin:auto;">
<div style="background:linear-gradient(135deg,{color} 80%,#222 100%);color:white;padding:1.2em 1.5em;border-radius:18px 18px 18px 6px;box-shadow:0 4px 24px rgba(0,0,0,0.12);font-size:1.2em;text-align:left;">
<div style="font-size:1.7em;font-weight:700;letter-spacing:1px;">{pred_class.replace("Demented", " Demented")}</div>
<div style="margin-top:0.5em;font-size:1.1em;">Confidence: <b>{confidence:.2f}</b></div>
</div>
</div>
"""
return md
description_md = f"""
<div style="text-align:center;">
<h1 style="margin-bottom:0.2em;font-size:2.2em;font-weight:800;letter-spacing:1px;background:linear-gradient(90deg,#1E90FF,#FFD700);-webkit-background-clip:text;-webkit-text-fill-color:transparent;">
Alzheimer's Detection AI Model
</h1>
<div style="font-size:1.1em;margin-bottom:0.7em;">
<b>Deep learning model for Alzheimer's stage detection from MRI scans.</b>
</div>
<div style="margin-bottom:0.7em;">
<span style="background:#e0e7ff;color:#222;padding:0.3em 1em;border-radius:12px;font-weight:500;">
Upload an MRI image to predict the stage of Alzheimer's disease.
</span>
</div>
<div style="margin-bottom:0.7em;">
<b>Stages:</b><br>
{stage_badges()}
</div>
<div style="margin-bottom:0.7em;">
<b>Instructions:</b>
<ul style="text-align:left;display:inline-block;">
<li>Click 'Upload' or drag an MRI image.</li>
<li>Click 'Submit' to see prediction.</li>
<li>The model analyzes brain MRI scans to detect Alzheimer's progression.</li>
</ul>
</div>
</div>
"""
footer_md = """
---
<div style="text-align:center;font-size:1.1em;color:#888;">
Powered by <b>TensorFlow</b> & <b>Gradio</b>
</div>
"""
with gr.Blocks(theme=gr.themes.Monochrome(primary_hue="blue", secondary_hue="purple")) as demo:
gr.Markdown(description_md)
with gr.Row():
image_input = gr.Image(type="pil", label="Upload MRI Image", elem_id="centered_image", show_label=True)
gr.Markdown("## Prediction Result")
output = gr.Markdown(label="", elem_id="prediction_block")
gr.Markdown(footer_md)
submit_btn = gr.Button("Submit", elem_id="submit_btn", variant="primary")
submit_btn.click(fn=predict_image, inputs=image_input, outputs=output)
if __name__ == "__main__":
demo.launch() |