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a49489d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | import gradio as gr
import tensorflow as tf
import numpy as np
from tensorflow.keras import backend as K
def fbeta(y_true, y_pred, threshold_shift=0):
beta = 2
y_pred = K.clip(y_pred, 0, 1)
y_pred_bin = K.round(y_pred + threshold_shift)
tp = K.sum(K.round(y_true * y_pred_bin)) + K.epsilon()
fp = K.sum(K.round(K.clip(y_pred_bin - y_true, 0, 1)))
fn = K.sum(K.round(K.clip(y_true - y_pred, 0, 1)))
precision = tp / (tp + fp)
recall = tp / (tp + fn)
beta_squared = beta**2
return (
(beta_squared + 1)
* (precision * recall)
/ (beta_squared * precision + recall + K.epsilon())
)
def accuracy_score(y_true, y_pred, epsilon=1e-4):
y_true = tf.cast(y_true, tf.float32)
y_pred = tf.cast(
tf.greater(tf.cast(y_pred, tf.float32), tf.constant(0.5)), tf.float32
)
tp = tf.reduce_sum(y_true * y_pred, axis=1)
fp = tf.reduce_sum(y_pred, axis=1) - tp
fn = tf.reduce_sum(y_true, axis=1) - tp
y_true = tf.cast(y_true, tf.bool)
y_pred = tf.cast(y_pred, tf.bool)
tn = tf.reduce_sum(
tf.cast(tf.logical_not(y_true), tf.float32)
* tf.cast(tf.logical_not(y_pred), tf.float32),
axis=1,
)
return (tp + tn) / (tp + tn + fp + fn + epsilon)
model = tf.keras.models.load_model(
"final.h5", custom_objects={"fbeta": fbeta, "accuracy_score": accuracy_score}
)
class_names = [
"clear",
"agriculture",
"selective_logging",
"haze",
"bare_ground",
"blooming",
"habitation",
"artisinal_mine",
"blow_down",
"road",
"slash_burn",
"primary",
"cultivation",
"water",
"conventional_mine",
"cloudy",
"partly_cloudy",
]
def predict(image):
image = tf.image.resize(image, [64, 64])
image = image / 255.0
image = np.expand_dims(image, axis=0)
predictions = model.predict(image)[0]
print(predictions)
results = {class_names[i]: float(predictions[i]) for i in range(len(class_names))}
return results
test_examples = [
"./images/test_10158.jpg", # Path to Test Image 1
"./images/train_10004.jpg", # Path to Test Image 2
]
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=17),
examples=test_examples,
)
iface.launch()
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