app.py

import pkg_resources
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
import gradio as gr
from ferret import Benchmark

# Load models and tokenizers
sentiment_tokenizer = AutoTokenizer.from_pretrained("Birkir/electra-base-igc-is-sentiment-analysis")
sentiment_model = AutoModelForSequenceClassification.from_pretrained("Birkir/electra-base-igc-is-sentiment-analysis")
formality_tokenizer = AutoTokenizer.from_pretrained("svanhvit/formality-classification-icebert")
formality_model = AutoModelForSequenceClassification.from_pretrained("svanhvit/formality-classification-icebert")
toxicity_tokenizer = AutoTokenizer.from_pretrained("unitary/toxic-bert")
toxicity_model = AutoModelForSequenceClassification.from_pretrained("unitary/toxic-bert")
politeness_tokenizer = AutoTokenizer.from_pretrained("Genius1237/xlm-roberta-large-tydip")
politeness_model = AutoModelForSequenceClassification.from_pretrained("Genius1237/xlm-roberta-large-tydip")

# Initialize benchmarks
sentiment_bench = Benchmark(sentiment_model, sentiment_tokenizer)
formality_bench = Benchmark(formality_model, formality_tokenizer)
toxicity_bench = Benchmark(toxicity_model, toxicity_tokenizer)
politeness_bench = Benchmark(politeness_model, politeness_tokenizer)

# Initialize pipelines for translation and text classification
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-is-en")
sentiment_classifier = pipeline("text-classification", model="Birkir/electra-base-igc-is-sentiment-analysis")
formality_classifier = pipeline("text-classification", model="svanhvit/formality-classification-icebert")
detoxify_classifier = pipeline('text-classification', model='unitary/toxic-bert', tokenizer='bert-base-uncased', function_to_apply='sigmoid', top_k=None)
politeness_classifier = pipeline("text-classification", model="Genius1237/xlm-roberta-large-tydip")

def replace_encoding(tokens):
    return [token.replace('Ġ', ' ')
                 .replace('ð', 'ð')
                 .replace('é', 'é')
                 .replace('æ', 'æ')
                 .replace('ý', 'ý')
                 .replace('á', 'á')
                 .replace('ú', 'ú')
                 .replace('ÃŃ', 'í')
                 .replace('Ãö', 'ö')
                 .replace('þ', 'þ')
                 .replace('Ãģ', 'Á')
                 .replace('Ãį', 'Ú')
                 .replace('Ãĵ', 'Ó')
                 .replace('ÃĨ', 'Æ')
                 .replace('ÃIJ', 'Ð')
                 .replace('Ãĸ', 'Ö')
                 .replace('Ãī', 'É')
                 .replace('Ãļ', 'ý')
            for token in tokens[1:-1]]

def analyze_with_influence(text, bench):
    explanations = bench.explain(text, target=0)
    influential_words = []
    for explanation in explanations:
        if explanation.explainer == 'Partition SHAP':
            tokens = replace_encoding(explanation.tokens)
            token_score_pairs = zip(tokens, explanation.scores)
            influential_words.extend([(token, score) for token, score in token_score_pairs])
    influential_words_str = "; ".join([f"{token} ({score:.2f})" for token, score in influential_words])
    return influential_words_str

def analyze_text(icelandic_text):
    # Perform translations
    translated_text = translator(icelandic_text, max_length=512)[0]['translation_text']
    
    # Perform initial analysis to get scores
    sentiment_result = sentiment_classifier(icelandic_text)[0]
    formality_result = formality_classifier(icelandic_text)[0]
    # Assuming detoxify_classifier gives a list of dictionaries, we need to adjust how we process this
    # For the sake of example, let's just mock a toxicity score here. Adjust this based on actual model output
    toxicity_mock_score = 0.5  # Placeholder, replace with actual processing of detoxify_classifier output
    politeness_result = politeness_classifier(translated_text)[0]

    # Gather scores and labels
    scores_labels = {
        "Sentiment": (sentiment_result['score'], sentiment_bench),
        "Formality": (formality_result['score'], formality_bench),
        "Toxicity": (toxicity_mock_score, toxicity_bench),  # Use the mock or processed score
        "Politeness": (politeness_result['score'], politeness_bench)
    }

    # Identify the aspect with the lowest score
    lowest_aspect = min(scores_labels, key=lambda x: scores_labels[x][0])

    # Perform Ferret analysis on the aspect with the lowest score
    influential_words = analyze_with_influence(icelandic_text if lowest_aspect in ["Sentiment", "Formality"] else translated_text, scores_labels[lowest_aspect][1])

    analysis_results = f"""
    Translated Text: {translated_text}\n\n
    Lowest Score Aspect: {lowest_aspect}\n
    Influential Words in {lowest_aspect}: {influential_words}
    """
    return analysis_results.strip()


demo = gr.Interface(fn=analyze_text, 
                    inputs=gr.Textbox(lines=2, placeholder="Enter Icelandic Text Here..."), 
                    outputs=gr.Textbox(label="Analysis Results"),
                    title="Icelandic Text Analysis",
                    description="This app translates Icelandic text to English and performs analysis with influential words for the aspect with the lowest score.")

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