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| import gradio as gr | |
| import numpy as np | |
| import tensorflow | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.preprocessing import image | |
| MODEL_ISATRON_JEY = 'modelo_isatron_jeysshonl.h5' | |
| cnn_model = load_model(MODEL_ISATRON_JEY) | |
| def make_prediction(test_image): | |
| test_image = test_image.name | |
| test_image = image.load_img(test_image, target_size=(224, 224)) | |
| test_image = image.img_to_array(test_image) / 255. | |
| test_image = np.expand_dims(test_image, axis=0) | |
| result = cnn_model.predict(test_image) | |
| return {"Normal": str(result[0][0]), "Neumonia": str(result[0][1])} | |
| image_input = gr.inputs.Image(type="file") | |
| description = " El modelo IsaTron es una Red Neuronal Convolucional (CNN) que ayuda al personal médico a predecir si una radiografía pediátrica muestra alguna anomalía"\ | |
| " , el paciente pediátrico puede tener neumonía o no y para verificar la predicción del modelo IsaTron se crea un porcentaje." \ | |
| " Para el funcionamiento del algoritmo se agregaron algunas imágenes de ejemplos que he proporcionado." | |
| enable_queue = True | |
| examples = [ | |
| ['1normal.jpeg'], | |
| ['neumo1.jpeg'], | |
| ['image1_pneumonia_virus.jpeg'], | |
| ['image1_pneumonia_bacteria.jpeg'], | |
| ['image2_normal.jpeg'], | |
| ['image2_pneumonia_bacteria.jpeg'], | |
| ['image3_normal.jpeg'], | |
| ['image4_normal.jpeg'], | |
| ] | |
| texto_jey = "<p style='text-align: center'><span style='font-size: 15pt;'>IsaTron . Jeysshon Bustos . 2022. </span></p>" | |
| interface=gr.Interface(fn=make_prediction, | |
| inputs=image_input, | |
| outputs='label', | |
| title="Neumonia Detección IsaTron", | |
| ##interpretation = "default", | |
| description=description, | |
| theme="dark-huggingface", | |
| texto_jey=texto_jey, | |
| examples=examples, | |
| enable_queue=enable_queue | |
| ) | |
| interface.launch(share=True,debug=True) | |