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| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| from keras.models import load_model | |
| import json | |
| # Load your pre-trained model | |
| model = load_model('brain_tumor_model.h5') | |
| # Load examples from JSON file | |
| with open('examples.json', 'r') as f: | |
| examples_data = json.load(f) | |
| examples = [[example['image']] for example in examples_data] | |
| def predict_image(image): | |
| try: | |
| # Resize and preprocess the image | |
| img = image.resize((128, 128)) | |
| img = np.array(img) | |
| # Check and convert grayscale to RGB | |
| if img.shape == (128, 128): # Grayscale | |
| img = np.stack((img,) * 3, axis=-1) | |
| # Normalize the image | |
| img = img / 255.0 | |
| img = np.expand_dims(img, axis=0) | |
| # Make the prediction | |
| prediction = model.predict(img) | |
| predicted_class = np.argmax(prediction) | |
| confidence = np.max(prediction) | |
| return f'{"No tumor detected" if predicted_class == 0 else "Tumor detected"}. Confidence: {confidence:.2f}' | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=predict_image, | |
| inputs=gr.Image(type="pil"), | |
| outputs=gr.Textbox(), | |
| title="Brain Tumor Detection AI App", | |
| description="Upload an image to detect brain tumors.", | |
| theme="monochrome", | |
| flagging_options=["Incorrect Diagnosis", "Image Not Clear", "Model Error"], | |
| examples=examples | |
| ) | |
| # Launch the interface | |
| iface.launch(share=True, debug=True) | |