# HuggingFace Spaces file to run a Gradio Interface for the ALBERT v2 Steam Review Constructiveness Classifier by Samuel Ruairí Bullard

# Package Imports
import gradio as gr
from transformers import pipeline
import torch

# Checks if CUDA is available on the machine
print("CUDA Available: ", torch.cuda.is_available())

# if not os.path.isfile("./README.md"):
# !git clone https://huggingface.co/spaces/abullard1/albert-v2-steam-review-constructiveness-classifier

# Sets the torch dtype to 16-bit half-precision floating-point format if CUDA is available, otherwise sets it to 32-bit single-precision floating-point format. (Available for GPUs with Tensor Cores like NVIDIA's Volta, Turing, Ampere Architectures have for example)
device = 0 if torch.cuda.is_available() else -1
torch_d_type = torch.float16 if torch.cuda.is_available() else torch.float32
print(f"Device: {device}")

# Defines the name of the base model, the classifier was fine-tuned from
base_model_name = "albert-base-v2"

# Defines the name of the fine-tuned model used for the steam-review constructiveness classification
finetuned_model_name = "abullard1/albert-v2-steam-review-constructiveness-classifier"

# PyTorch classifier pipeline
classifier = pipeline(
    task="text-classification",  # Defines the task
    model=finetuned_model_name,  # Defines the fine-tuned model to use
    tokenizer=base_model_name,  # Defines the tokenizer to use (same as the base model)
    device=device,  # Defines the device the classification will be run on
    top_k=None,  # Returns all scores for all labels, not just the one with the highest score
    truncation=True,  # Truncates the input text if it exceeds the maximum length
    max_length=512,  # Defines the maximum length of the input text (512 for BERT. Explicitly set here)
    torch_dtype=torch_d_type  # Sets the torch dtype to 16-bit half-precision floating-point format if CUDA is available, otherwise sets it to 32-bit single-precision floating-point format
)


# Extracts the labels and scores from the prediction result
def classify_steam_review(input_text):
    result = classifier(input_text)

    label_1, label_2 = result[0][0]["label"], result[0][1]["label"]
    score_1, score_2 = round(result[0][0]["score"], 6), round(result[0][1]["score"], 6)

    return {"label_1": label_1, "score_1": score_1, "label_2": label_2, "score_2": score_2}


# Provides a textual representation of the classification result
def get_steam_review_classification_result_text(label_1, score_1):
    if label_1 == "LABEL_1":
        return f"Constructive with a score of {score_1}. 👍🏻"
    else:
        return f"Not Constructive with a score of {score_1}. 👎🏻"


# Examples Steam Reviews to display in the Gradio Interface using the "examples" parameter
examples = [
    ["Review: I think this is a great game but it still has some room for improvement., Playtime: 12, Voted Up: True, Upvotes: 1, Votes Funny: 0"],
    ["Review: Trash game. Deleted., Playtime: 1, Voted Up: False, Upvotes: 0, Votes Funny: 0"],
    ["Review: This game is amazing., Playtime: 100, Voted Up: True, Upvotes: 1, Votes Funny: 0"],
    ["Review: Great game, but the community is toxic., Playtime: 50, Voted Up: True, Upvotes: 1, Votes Funny: 0"]
]

# Information Description about the Steam Review Format
# info_description = (
#     """
#     Format your input as follows for the best results: ***Review**: {review_text}, **Playtime**: {author_playtime_at_review}, **Voted Up**: {voted_up}, **Upvotes**: {upvotes}, **Votes Funny**: {votes_funny}.*
#     """
# )

# Labeling Criteria Information Markdown
labeling_criteria = (
    """
    <ul>
        <li>
            <b><span style="color:green;">Constructive</span></b>: 
            Reviews that provide <b>helpful feedback</b>, <b>suggestions for improvement</b>, 
            <b>constructive criticism</b>, or <b>detailed insights</b> into the game.
        </li>
        <li>
            <b><span style="color:red;">Not Constructive</span></b>: 
            Reviews that <b>do not offer useful feedback</b>, <b>lack substance</b>, 
            are <b>vague</b>, <b>off-topic</b>, <b>irrelevant</b>, or <b>trolling</b>.
        </li>
    </ul>
    """
)

# Custom CSS to create a card-like design for the Gradio Interface
custom_css = """
<style>
.card {
    border: 1px solid rgba(255, 255, 255, 0.1);
    padding: 16px;
    border-radius: 12px;
    box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.1);
    background-color: transparent;
    margin-bottom: 16px;
}
</style>
"""

# Main Gradio Interface using Gradio Blocks
# Docs: https://www.gradio.app/docs/gradio/blocks
with gr.Blocks() as steam_reviews_classifier_block:
    with gr.Column(elem_classes=["card"]):
        gr.Markdown("## Steam Review Constructiveness Classifier")
        # gr.Markdown(info_description)

        gr.HTML(custom_css)
        # Main Column
        with gr.Column():
            # Card Column to create a card-like Design for the Main Functionality Components
            with gr.Column(elem_classes=["card"]):
                # Upper Row (Input Textbox, Constructive Label, Not Constructive Label)
                with gr.Row(equal_height=True):
                    # Input Textbox Column
                    with gr.Column():
                        input_textbox = gr.Textbox(
                            lines=8,
                            label="Steam Review",
                            # info="Input Steam Review here",
                            placeholder="Review: I think this is a great game but it still has some room for improvement., Playtime: 12, Voted Up: True, Upvotes: 1, Votes Funny: 0",
                            show_copy_button=False,
                            value="Review: I think this is a great game but it still has some room for improvement., Playtime: 12, Voted Up: True, Upvotes: 1, Votes Funny: 0"
                        )

                    # Constructive and Not Constructive Labels Column
                    with gr.Column():
                        constructive_label = gr.Label(label="Constructive")
                        not_constructive_label = gr.Label(label="Not Constructive")

                # Output Textbox which shows the textual representation of the Constructiveness Prediction
                output_textbox = gr.Textbox(
                    label="Constructiveness Prediction",
                    interactive=False,
                    show_copy_button=False,
                    # info="Textual representation of the Constructiveness Prediction"
                )

                gr.Markdown("<br>")

                # Examples Component
                example_component = gr.Examples(
                    examples=examples,
                    inputs=input_textbox
                )

                # Submit Button
                submit_button = gr.Button(value="Submit")

            # Collapsable Labeling Criteria Info Accordion
            with gr.Accordion(label="See Labeling Criteria:", open=False):
                gr.Markdown(labeling_criteria)

    # Function to run when the Submit Button is clicked (Passes the input text to the classifier and displays the output text)
    def on_submit_click(input_text):
        classification_result = classify_steam_review(input_text)
        label_1 = classification_result["label_1"]
        score_1 = classification_result["score_1"]
        score_2 = classification_result["score_2"]

        output_text = get_steam_review_classification_result_text(label_1, score_1)

        # Maps the scores to the constructive and not constructive labels in the Gradio Interface
        if label_1 == "LABEL_1":
            constructive, not_constructive = score_1, score_2
        else:
            constructive, not_constructive = score_2, score_1

        return output_text, constructive, not_constructive


    # onClick event for the Submit Button
    submit_button.click(
        fn=on_submit_click,
        inputs=input_textbox,
        outputs=[output_textbox, constructive_label, not_constructive_label]
    )

# Launches the Gradio Blocks Interface
steam_reviews_classifier_block.launch()