Upload app.py
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app.py
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import tensorflow as tf
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from tensorflow import keras
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import numpy as np
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import gradio as gr
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model = keras.models.load_model("Model.keras")
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classnames = [
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"Acacia",
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"Adenanthera microsperma",
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"Adenium species",
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"Anacardium occidentale",
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"Annona squamosa",
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"Artocarpus altilis",
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"Artocarpus heterophyllus",
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"Barringtonia acutangula",
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"Cananga odorata",
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"Carica papaya",
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"Casuarina equisetifolia",
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"Cedrus",
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"Chrysophyllum cainino",
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"Citrus aurantiifolia",
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"Citrus grandis",
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"Cocos nucifera",
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"Dalbergia oliveri",
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"Delonix regia",
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"Dipterocarpus alatus",
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"Erythrina fusca",
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"Eucalyptus",
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"Ficus microcarpa",
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"Ficus racemosa",
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"Gmelina arborea Roxb",
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"Hevea brasiliensis",
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"Hopea",
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"Khaya senegalensis",
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"Khaya senegalensis A.Juss",
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"Lagerstroemia speciosa",
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"Magnolia alba",
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"Mangifera",
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"Melaleuca",
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"Melia azedarach",
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"Musa",
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"Nephelium lappaceum",
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"Persea",
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"Polyalthia longifolia",
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"Prunnus",
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"Prunus salicina",
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"Psidium guajava",
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"Pterocarpus macrocarpus",
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"Senna siamea",
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"Spondias mombin L",
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"Syzygium nervosum",
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"Tamarindus indica",
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"Tectona grandis",
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"Terminalia catappa",
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"Veitchia merrilli",
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"Wrightia",
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"Wrightia religiosa",
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]
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def predict(path):
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image = path.reshape((224, 224, 3))
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image = tf.keras.utils.img_to_array(image)
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image = np.expand_dims(image, axis=0)
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pred = model.predict(image, verbose=0)
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pred = pred[0]
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confidences = {classnames[i]: round(float(pred[i]), 2) for i in range(50)}
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return confidences
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gr.Interface(
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fn=predict,
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inputs=gr.Image(shape=(224, 224)),
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outputs=gr.Label(num_top_classes=5),
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examples=[
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"Dalbergia oliveri.JPG",
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"Eucalyptus.JPG",
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"Khaya senegalensis.JPG",
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"Syzygium nervosum.JPG",
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],
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).launch()
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