app.py

import os
import torch
import spacy
import numpy as np
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
import gradio as gr
import matplotlib.plt as pyplot
import seaborn as sns

PATH = '/data/' # at least 150GB storage needs to be attached
os.environ['TRANSFORMERS_CACHE'] = PATH
os.environ['HF_HOME'] = PATH
os.environ['HF_DATASETS_CACHE'] = PATH
os.environ['TORCH_HOME'] = PATH

HF_TOKEN = os.environ["hf_read"]

SENTIMENT_LABEL_NAMES = {0: "Negative", 1: "No sentiment or Neutral sentiment", 2: "Positive"}
LANGUAGES = ["Czech", "English", "French", "German", "Hungarian", "Polish", "Slovakian"]

id2label = {
    0: "Anger",
    1: "Fear",
    2: "Disgust",
    3: "Sadness",
    4: "Joy",
    5: "None of Them"
}
def load_spacy_model(model_name="xx_sent_ud_sm"):
    try:
        model = spacy.load(model_name)
    except OSError:
        spacy.cli.download(model_name)
        model = spacy.load(model_name)
    return model

def split_sentences(text, model):
    # disable pipeline components not necessary for splitting
    model.disable_pipes(model.pipe_names)  # first disable all the pipes
    model.enable_pipe("senter") # then enable the sentence splitter only

    doc = model(text)
    sentences = [sent.text for sent in doc.sents]

    return sentences

def build_huggingface_path(language: str):
    if language == "Czech" or language == "Slovakian":
        return "visegradmedia-emotion/Emotion_RoBERTa_pooled_V4"
    return "poltextlab/xlm-roberta-large-pooled-MORES"

def predict(text, model_id, tokenizer_id):
    model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", offload_folder="offload", token=HF_TOKEN)
    tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)

    inputs = tokenizer(text,
                       max_length=64,
                       truncation=True,
                       padding="do_not_pad",
                       return_tensors="pt")
    model.eval()

    with torch.no_grad():
        logits = model(**inputs).logits

    probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy().flatten()
    return probs

def get_most_probable_label(probs):
    label = id2label[probs.argmax()]
    probability = f"{round(100 * probs.max(), 2)}%"
    return label, probability

def prepare_heatmap_data(data):
    heatmap_data = pd.DataFrame(0, index=range(len(data)), columns=emotion_mapping.values())
    for idx, item in enumerate(data):
        for idy, confidence in enumerate(item["emotions"]):
            emotion = emotion_mapping[idy]
            heatmap_data.at[idx, emotion] = confidence
    heatmap_data.index = [item["sentence"] for item in data]
    return heatmap_data

def plot_emotion_heatmap(data):
    heatmap_data = prepare_heatmap_data(data)
    fig = plt.figure(figsize=(10, len(data) * 0.5 + 2))
    sns.heatmap(heatmap_data, annot=True, cmap="coolwarm", cbar=True, linewidths=0.5, linecolor='gray')
    plt.title("Emotion Confidence Heatmap")
    plt.xlabel("Emotions")
    plt.ylabel("Sentences")
    plt.tight_layout()
    return fig

def predict_wrapper(text, language):
    model_id = build_huggingface_path(language)
    tokenizer_id = "xlm-roberta-large"

    spacy_model = load_spacy_model()
    sentences = split_sentences(text, spacy_model)

    results = []
    results_heatmap = []
    for sentence in sentences:
        probs = predict(sentence, model_id, tokenizer_id)
        label, probability = get_most_probable_label(probs)
        results.append([sentence, label, probability])
        results_heatmap.append({"sentence":sentence, "emotions":probs})

    figure = plot_emotion_heatmap(prepare_heatmap_data(results_heatmap))
    output_info = f'Prediction was made using the <a href="https://huggingface.co/{model_id}">{model_id}</a> model.'
    return results, figure, output_info


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            input_text = gr.Textbox(lines=6, label="Input", placeholder="Enter your text here...")
        with gr.Column():
            with gr.Row():
                language_choice = gr.Dropdown(choices=LANGUAGES, label="Language", value="English")
            with gr.Row():
                predict_button = gr.Button("Submit")

    with gr.Row():
        result_table = gr.Dataframe(
            headers=["Sentence", "Prediction", "Confidence"],
            column_widths=["65%", "25%", "10%"],
            wrap=True # important
        )
    with gr.Row():
        model_info = gr.Markdown()

    predict_button.click(
        fn=predict_wrapper,
        inputs=[input_text, language_choice],
        outputs=[result_table, "plot", model_info]
    )

if __name__ == "__main__":
    demo.launch()