| import os | |
| import gradio as gr | |
| import numpy as np | |
| import tensorflow as tf | |
| from tensorflow.keras import models | |
| IMG_SIZE = 300 | |
| class_names = ['none','mild','severe'] | |
| cwd = os.getcwd() | |
| outpath= os.path.join(cwd,"model") | |
| model_name = 'cross_event_ecuador_haiti_efficientnet_fine_tuned_1644086357.h5' | |
| loaded_model = models.load_model(os.path.join(outpath,model_name)) | |
| def _classifier(inp): | |
| img = np.asarray(tf.cast(inp, dtype=tf.float32)) * 1 / 255.0 | |
| img = img.reshape((-1, IMG_SIZE, IMG_SIZE, 3)) | |
| preds = loaded_model.predict(img).flatten() | |
| return {class_names[i]:float(preds[i]) for i in range(len(class_names))} | |
| iface = gr.Interface(fn=_classifier, | |
| title="Disaster damage assessment from social media image", | |
| description="This simple app allow users to load an image and assess the extent of damage caused by an earthquake", | |
| article="The severity of damage in an image is the extent of physical destruction shown in it. For this experiment we only consider three level of damages: severe damage,mild damage and no damage (none). The model was trained using data from Haiti,Ecuador,Nepal earthquakes and google images.", | |
| examples=['Haiti-Gingerbread-2.jpg','building_damage_100.jpg','building_damage_424.jpg'], | |
| inputs=gr.inputs.Image(shape=(IMG_SIZE, IMG_SIZE)), | |
| outputs=gr.outputs.Label() | |
| ) | |
| iface.launch() |