final

.gitignore

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+venv
+.env
+.venv

app.py

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+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()

final.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:199df7c1cae0f24c23a58d21b17e286cb99ab577963013065d7866ff3c92d624
+size 78038628

requirements.txt

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+tensorflow==2.9.0
+gradio
+numpy==1.22