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import gradio as gr |
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import tensorflow as tf |
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import numpy as np |
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from keras.models import load_model |
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from tensorflow.keras.preprocessing.image import load_img, img_to_array |
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model = load_model('best_model.h5', compile=False) |
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model.compile( |
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optimizer='adam', |
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loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), |
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metrics=['accuracy'] |
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) |
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def format_decimal(value): |
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decimal_value = format(value, ".2f") |
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return decimal_value |
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def detect(img): |
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img = img.resize((256, 256)) |
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img = img_to_array(img) |
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img = np.expand_dims(img, axis=0) |
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img = img / 255.0 |
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prediction = model.predict(img)[0] |
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class_names = ['AMAZONE', 'BIOGUERRA', 'REVENANT', 'ZANGBETO'] |
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class_index = np.argmax(prediction) |
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texte = class_names[class_index] |
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return texte |
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title = "Orisha" |
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iface = gr.Interface( |
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fn=detect, |
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inputs=gr.Image(type="pil", image_mode='RGB'), |
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outputs=gr.Textbox(label="Classe", lines=10), |
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title=title |
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) |
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iface.launch(inline=False) |
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