To write a script for tongue classification video, you will need to follow these general steps:

1. Collect a dataset: The first step is to collect a dataset of tongue images or videos. You can either collect your own data or use publicly available datasets.
2. Preprocess the data: After collecting the dataset, you will need to preprocess the data by cleaning and resizing the images or videos.
3. Train a model: Next, you will need to train a model using the preprocessed data. You can use deep learning frameworks like TensorFlow or PyTorch to train your model.
4. Test the model: Once you have trained your model, you will need to test it on a separate dataset to evaluate its performance.
5. Integrate the model into a video: Finally, you will need to integrate the model into a video using a video processing library like OpenCV.

Here is a sample code in python that can be used for tongue classification video:

    # Import necessary libraries
    import cv2
    import numpy as np
    import tensorflow as tf

    # Load the pre-trained model
    model = tf.keras.models.load_model('tongue_classification_model.h5')

    # Define the video capture object
    cap = cv2.VideoCapture(0)

    # Define the labels for each class
    labels = ['class1', 'class2', 'class3']

    # Define the font for the label
    font = cv2.FONT_HERSHEY_SIMPLEX

    while True:
        # Capture a frame from the video
        ret, frame = cap.read()
    
        # Preprocess the frame
        frame = cv2.resize(frame, (224, 224))
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        frame = frame.astype('float32') / 255.0
    
        # Make a prediction using the pre-trained model
        prediction = model.predict(np.expand_dims(frame, axis=0))[0]
    
        # Get the index of the predicted label
        predicted_label_index = np.argmax(prediction)
    
        # Get the predicted label
        predicted_label = labels[predicted_label_index]
    
        # Add the predicted label to the frame
        cv2.putText(frame, predicted_label, (10, 30), font, 1, (0, 255, 0), 2)
    
        # Display the frame
        cv2.imshow('Tongue Classification Video', frame)
    
        # Exit on 'q' key press
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    # Release the video capture object and close all windows
    cap.release()
    cv2.destroyAllWindows()

In this sample code, we have used a pre-trained model to classify the tongue into three classes. The video is captured using the webcam and each frame is preprocessed, passed through the model to obtain a prediction, and the predicted label is added to the frame. Finally, the processed frame is displayed in a window until the 'q' key is pressed.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence