File size: 2,570 Bytes
aecfe47 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
import cv2
# Load your trained model
model = tf.keras.models.load_model('brain_tumor_model.h5') # or .keras
# Define class labels (adjust based on your model's classes)
CLASS_LABELS = [
'glioma_tumor',
'meningioma_tumor',
'no_tumor',
'pituitary_tumor'
]
def preprocess_image(image):
"""Preprocess image for Xception model"""
# Convert PIL to numpy array
img_array = np.array(image)
# Resize to 299x299 (Xception input size)
img_resized = cv2.resize(img_array, (299, 299))
# Ensure RGB format
if len(img_resized.shape) == 3 and img_resized.shape[2] == 3:
pass # Already RGB
elif len(img_resized.shape) == 2:
img_resized = cv2.cvtColor(img_resized, cv2.COLOR_GRAY2RGB)
# Normalize pixel values to [0, 1]
img_normalized = img_resized.astype('float32') / 255.0
# Add batch dimension
img_batch = np.expand_dims(img_normalized, axis=0)
return img_batch
def predict(image):
"""Make prediction on uploaded image"""
if image is None:
return "Please upload an image"
try:
# Preprocess the image
processed_image = preprocess_image(image)
# Make prediction
predictions = model.predict(processed_image)
# Get probabilities
probabilities = tf.nn.softmax(predictions[0]).numpy()
# Create results dictionary
results = {}
for i, label in enumerate(CLASS_LABELS):
results[label.replace('_', ' ').title()] = float(probabilities[i])
return results
except Exception as e:
return f"Error processing image: {str(e)}"
# Create Gradio interface
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil", label="Upload Brain MRI Scan"),
outputs=gr.Label(num_top_classes=4, label="Prediction"),
title="🧠 Brain Tumor Classification - Xception Model",
description="""
Upload an MRI brain scan image to classify tumor types.
**Model:** Sequential Xception Architecture
**Accuracy:** 99% (on validation set)
**Classes:**
- Glioma Tumor
- Meningioma Tumor
- No Tumor
- Pituitary Tumor
⚠️ **Disclaimer:** For research/educational purposes only. Not for medical diagnosis.
""",
examples=[
# Add example images if you have them
],
theme=gr.themes.Soft(),
analytics_enabled=False
)
if __name__ == "__main__":
demo.launch() |