| import gradio as gr |
| from tensorflow import keras |
| import numpy as np |
| from PIL import Image |
| import cv2 |
| import tensorflow as tf |
| import base64 |
| import io |
|
|
| model = keras.models.load_model('my_model (2).h5') |
|
|
| CLASS_NAMES = ['Non-Tumor', 'Non-Viable-Tumor', 'Viable', 'viable: non-viable'] |
|
|
| def make_gradcam_heatmap(img_array, model): |
| last_conv_layer = model.get_layer('last_conv_layer') |
| grad_model = tf.keras.models.Model( |
| [model.inputs], |
| [last_conv_layer.output, model.output] |
| ) |
| |
| with tf.GradientTape() as tape: |
| conv_outputs, predictions = grad_model(img_array) |
| pred_index = tf.argmax(predictions[0]) |
| class_channel = predictions[:, pred_index] |
| |
| grads = tape.gradient(class_channel, conv_outputs) |
| pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) |
| |
| conv_outputs = conv_outputs[0] |
| heatmap = conv_outputs @ pooled_grads[..., tf.newaxis] |
| heatmap = tf.squeeze(heatmap) |
| heatmap = tf.maximum(heatmap, 0) / (tf.math.reduce_max(heatmap) + 1e-8) |
| |
| return heatmap.numpy() |
|
|
| def predict_from_base64(base64_string): |
| try: |
| if ',' in base64_string: |
| base64_string = base64_string.split(',')[1] |
| |
| image_bytes = base64.b64decode(base64_string) |
| img = Image.open(io.BytesIO(image_bytes)).convert('RGB') |
| |
| img = img.resize((224, 224)) |
| img_array = np.array(img) / 255.0 |
| img_array = np.expand_dims(img_array, axis=0) |
| |
| predictions = model.predict(img_array) |
| pred_class = CLASS_NAMES[np.argmax(predictions[0])] |
| confidence = float(np.max(predictions[0])) * 100 |
| |
| result_text = f"Prediction: {pred_class}\nConfidence: {confidence:.2f}%\n\n" |
| result_text += "All Probabilities:\n" |
| for i, name in enumerate(CLASS_NAMES): |
| result_text += f" {name}: {predictions[0][i]*100:.2f}%\n" |
| |
| try: |
| heatmap = make_gradcam_heatmap(img_array, model) |
| heatmap = cv2.resize(heatmap, (224, 224)) |
| heatmap = np.uint8(255 * heatmap) |
| heatmap_colored = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) |
| original = np.array(img) |
| superimposed = cv2.addWeighted(original, 0.6, heatmap_colored, 0.4, 0) |
| output_image = superimposed |
| except Exception as e: |
| output_image = np.array(img) |
| result_text += f"\n(Grad-CAM unavailable: {str(e)})" |
| |
| return output_image, result_text |
| |
| except Exception as e: |
| return None, f"Error: {str(e)}" |
|
|
| def predict_from_image(input_image): |
| img = Image.fromarray(input_image).convert('RGB') |
| img = img.resize((224, 224)) |
| img_array = np.array(img) / 255.0 |
| img_array = np.expand_dims(img_array, axis=0) |
| |
| predictions = model.predict(img_array) |
| pred_class = CLASS_NAMES[np.argmax(predictions[0])] |
| confidence = float(np.max(predictions[0])) * 100 |
| |
| result_text = f"Prediction: {pred_class}\nConfidence: {confidence:.2f}%\n\n" |
| result_text += "All Probabilities:\n" |
| for i, name in enumerate(CLASS_NAMES): |
| result_text += f" {name}: {predictions[0][i]*100:.2f}%\n" |
| |
| try: |
| heatmap = make_gradcam_heatmap(img_array, model) |
| heatmap = cv2.resize(heatmap, (224, 224)) |
| heatmap = np.uint8(255 * heatmap) |
| heatmap_colored = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) |
| original = np.array(img) |
| superimposed = cv2.addWeighted(original, 0.6, heatmap_colored, 0.4, 0) |
| output_image = superimposed |
| except Exception as e: |
| output_image = np.array(img) |
| result_text += f"\n(Grad-CAM unavailable: {str(e)})" |
| |
| return output_image, result_text |
|
|
| image_interface = gr.Interface( |
| fn=predict_from_image, |
| inputs=gr.Image(label="Upload Histopathology Image"), |
| outputs=[ |
| gr.Image(label="Grad-CAM Visualization"), |
| gr.Textbox(label="Classification Result") |
| ], |
| title="Bone Cancer Detection (Osteosarcoma)", |
| description="Upload an H&E stained histopathology image." |
| ) |
|
|
| api_interface = gr.Interface( |
| fn=predict_from_base64, |
| inputs=gr.Textbox(label="Base64 Image String"), |
| outputs=[ |
| gr.Image(label="Grad-CAM Visualization"), |
| gr.Textbox(label="Classification Result") |
| ], |
| api_name="predict_base64" |
| ) |
|
|
| demo = gr.TabbedInterface( |
| [image_interface, api_interface], |
| ["Upload Image", "API (Base64)"] |
| ) |
|
|
| demo.launch() |
