# Importing some modules
import gradio as gr
from transformers import pipeline
import torch

# Loading in the model
MODEL_AGE = pipeline('image-classification', model='nateraw/vit-age-classifier', device=0 if torch.cuda.is_available() else -1)
MODEL_EMOTION = pipeline('image-classification', model='dennisjooo/emotion_classification', device=0 if torch.cuda.is_available() else -1)

def classify_image(image, top_k):
    # Getting the classification result
    age_result = MODEL_AGE(image)
    emotion_result = MODEL_EMOTION(image)

    # Reformating the classification result into a dictionary
    age_result = {result['label']: result['score'] for result in age_result[:min(int(top_k), 8)]}
    emotion_result = {result['label']: result['score'] for result in emotion_result[:min(int(top_k), 7)]}

    # Add some text comment to it lol
    comment = text_comment(list(age_result.keys())[0])

    # Returning the classification result
    return age_result, comment, emotion_result

# Snarky comment based on age
def text_comment(pred_class):
    match pred_class:
        case "3-9":
            return "Lost your way to the playground?"
        case "10-19":
            return "But Mom, I'm not a kid anymore!"
        case "20-29":
            return "You're in your prime!"
        case "30-39":
            return "Oof, watch out for those wrinkles!"
        case "40-49":
            return "You're still young at heart!"
        case "50-59":
            return "Retirement is just around the corner!"
        case "60-69":
            return "You're a senior citizen now!"
        case "more than 70":
            return "Hey Siri, play 'My Way' by Frank Sinatra"
        

if __name__ == "__main__":
    # Definining the title of the interface
    title_text = """
        # I will guess your age and mood based on your picture!
        ---
        Totally not creepy, I promise :)
        <br>Made by [Dennis Jonathan](dennisjooo.github.io). A project for REA Mastering AI course.
        Age guessing model from [nateraw/vit-age-classifier](https://huggingface.co/nateraw/vit-age-classifier)
        <br>Mood-guessing model is a [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k)
        trained on [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis)
    """

    # Creating the Gradio interface
    with gr.Blocks() as demo:
        gr.Markdown(title_text)
        with gr.Row(equal_height=True):
            with gr.Column():
                # Creating the input block
                image = gr.Image(label="Upload a picture of yourself", type="pil", scale=2)

                # Creating the example block
                gr.Examples(examples=[
                    "./images/andrew.jpg",
                    "./images/feifei.jpg",
                    "./images/geoff.jpg",
                    "./images/ilya.jpg",
                    "./images/karpathy.jpg",
                    "./images/lex.jpg"
                    ], inputs=[image], label="Or choose an example")


            with gr.Column():
                # Getting the top k hyperparameter
                top_k = gr.Number(label="How many guesses do I get?", value=1)

                # Creating the output block
                age_label = gr.Label(label="Hey it's me, your age!")
                comment = gr.Textbox(label="Based on your age, I think you are...",
                                    placeholder="I'm still learning, so I might be wrong!")
                emotion_label = gr.Label(label="Hey it's me, your emotion!")

        with gr.Row():
            # Submit button
            btn = gr.Button("Beep boop, guess my age and emotion!")
            btn.click(classify_image, inputs=[image, top_k], outputs=[age_label, comment, emotion_label])

            # Clear button
            clear = gr.Button("Poof begone!")
            clear.click(lambda: [None, None, None, None], inputs=[], outputs=[image, age_label, comment, emotion_label])

    # Launching the interface
    demo.launch(share=True, debug=True)