Create app.py

app.py

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+import tensorflow as tf
+from tensorflow.keras import models, layers
+import gradio as gr
+import numpy as np
+
+# Define and name the model
+bat_classifier_model = models.Sequential(name="BatClassifier")
+bat_classifier_model.add(layers.Conv2D(20, (5,5), activation='relu', input_shape=(232, 154, 3)))
+bat_classifier_model.add(layers.Dropout(0.2))
+bat_classifier_model.add(layers.Conv2D(20, (5,5), activation='relu'))
+bat_classifier_model.add(layers.Dropout(0.2))
+bat_classifier_model.add(layers.MaxPooling2D(3,3))
+bat_classifier_model.add(layers.Conv2D(20, (5,5), activation='relu'))
+bat_classifier_model.add(layers.Dropout(0.2))
+bat_classifier_model.add(layers.Conv2D(10, (5,5), activation='relu'))
+bat_classifier_model.add(layers.Dropout(0.2))
+bat_classifier_model.add(layers.MaxPooling2D(3,3))
+bat_classifier_model.add(layers.Flatten())
+bat_classifier_model.add(layers.Dense(4, activation='softmax'))
+
+optimizer = tf.keras.optimizers.Adam(learning_rate=0.02)
+bat_classifier_model.compile(optimizer=optimizer, loss='mse', metrics=['accuracy'])
+
+# Load the saved model
+bat_classifier_model = tf.keras.models.load_model("bat_classifier_model")
+
+# Gradio interface function
+def classify_bat(image):
+    processed_image = preprocess_image(image)
+    prediction = bat_classifier_model.predict(processed_image)[0]
+    class_names = ["Class 1", "Class 2", "Class 3", "Class 4"]
+    return {class_names[i]: float(prediction[i]) for i in range(len(class_names))}
+
+# Preprocess the image
+def preprocess_image(image):
+    # Add your preprocessing steps here
+    processed_image = image  # Placeholder for actual preprocessing
+    processed_image = np.expand_dims(processed_image, axis=0)
+    return processed_image
+
+# Gradio interface
+inputs = gr.inputs.Image()
+outputs = gr.outputs.Label(num_top_classes=4)
+gr.Interface(fn=classify_bat, inputs=inputs, outputs=outputs).launch()