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Update app.py
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app.py
CHANGED
@@ -1,23 +1,25 @@
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import gradio as gr
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import numpy as np
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import tensorflow
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from tensorflow.keras.models import load_model
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from tensorflow.keras.preprocessing import image
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MODEL_ISATRON_JEY = 'modelo_isatron_jeysshonl.h5'
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cnn_model = load_model(MODEL_ISATRON_JEY)
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def make_prediction(test_image):
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test_image = test_image.name
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test_image = image.load_img(test_image, target_size=(224, 224))
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test_image = image.img_to_array(test_image) / 255.
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test_image = np.expand_dims(test_image, axis=0)
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result = cnn_model.predict(test_image)
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return {"Normal": str(result[0][0]), "Neumonia": str(result[0][1])}
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# Actualización
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image_input = gr.Image(type="
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description = ("El modelo IsaTron es una Red Neuronal Convolucional (CNN) diseñada como un método de apoyo medico "
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"para el diagnóstico en imágenes radiológicas de neumonía pediátrica. Isatron arroja un porcentaje para "
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@@ -50,3 +52,4 @@ interface = gr.Interface(
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interface.launch(share=True)
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import gradio as gr
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import numpy as np
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import tensorflow as tf
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import cv2
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from tensorflow.keras.models import load_model
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from tensorflow.keras.preprocessing import image
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MODEL_ISATRON_JEY = 'modelo_isatron_jeysshonl.h5'
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cnn_model = load_model(MODEL_ISATRON_JEY)
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def make_prediction(test_image):
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test_image = image.load_img(test_image, target_size=(224, 224))
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test_image = image.img_to_array(test_image) / 255.
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test_image = np.expand_dims(test_image, axis=0)
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result = cnn_model.predict(test_image)
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return {"Normal": str(result[0][0]), "Neumonia": str(result[0][1])}
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# Actualización del tipo de entrada de imagen
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image_input = gr.Image(type="filepath")
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description = ("El modelo IsaTron es una Red Neuronal Convolucional (CNN) diseñada como un método de apoyo medico "
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"para el diagnóstico en imágenes radiológicas de neumonía pediátrica. Isatron arroja un porcentaje para "
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)
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interface.launch(share=True)
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