for tf keras write code to show class name for text classification in python

To show the class name for text classification in TensorFlow's Keras API, you can follow these steps:

1. Import the necessary libraries:

main.py
import numpy as np
import tensorflow as tf
from tensorflow import keras
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2. Load your text classification model:

main.py
model = keras.models.load_model('path_to_your_model')
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Replace 'path_to_your_model' with the actual path to your trained model.

3. Prepare your input text for classification:

main.py
input_text = "your_text_to_classify"
preprocessed_text = preprocess_input_text(input_text)  # Preprocess your input text as required by your model
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Replace "your_text_to_classify" with the actual text you want to classify. Also, make sure to preprocess the input text based on how your model expects the text to be preprocessed for classification.

4. Perform the classification and get the predicted class label:

main.py
input_vector = np.array([preprocessed_text])  # Convert the preprocessed text to a numpy array
predictions = model.predict(input_vector)
predicted_class_index = np.argmax(predictions[0])
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5. Get the class name associated with the predicted class index:

main.py
class_names = ["class1", "class2", "class3"]  # Replace with the actual class names used in your model
predicted_class_name = class_names[predicted_class_index]
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Replace ["class1", "class2", "class3"] with the actual class names used in your model. The order of the class names list should match the order of the classes in your model's output.

6. Print the predicted class name:

main.py
print("Predicted class: ", predicted_class_name)
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Make sure you replace the preprocess_input_text function with the appropriate preprocessing steps for your specific text classification problem.

Note: This assumes you have already trained and saved a model for text classification using TensorFlow's Keras API. If you haven't trained a model yet, you'll need to do so before you can perform text classification.