Update app.py

app.py

@@ -3,29 +3,28 @@ from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassifica
 import gradio as gr
 from ferret import Benchmark
 
-# Load models and tokenizers for sentiment analysis
+# 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")
-sentiment_bench = Benchmark(sentiment_model, sentiment_tokenizer)
-
-# Load models and tokenizers for formality analysis
 formality_tokenizer = AutoTokenizer.from_pretrained("svanhvit/formality-classification-icebert")
 formality_model = AutoModelForSequenceClassification.from_pretrained("svanhvit/formality-classification-icebert")
-formality_bench = Benchmark(formality_model, formality_tokenizer)
-
-# Load models and tokenizers for toxicity analysis
 toxicity_tokenizer = AutoTokenizer.from_pretrained("unitary/toxic-bert")
 toxicity_model = AutoModelForSequenceClassification.from_pretrained("unitary/toxic-bert")
-toxicity_bench = Benchmark(toxicity_model, toxicity_tokenizer)
-
-# Load models and tokenizers for politeness analysis
 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 classifiers
+# Initialize pipelines for translation and text classification
 translator = pipeline("translation", model="Helsinki-NLP/opus-mt-is-en")
-detoxify_pipeline = pipeline('text-classification', model='unitary/toxic-bert', tokenizer='bert-base-uncased', function_to_apply='sigmoid', top_k=None)
+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', ' ')
@@ -60,19 +59,33 @@ def analyze_with_influence(text, bench):
     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]
+    toxicity_result = detoxify_classifier(translated_text)[0]
+    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_result['score'], toxicity_bench),
+        "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])
 
-    sentiment_analysis = analyze_with_influence(icelandic_text, sentiment_bench)
-    formality_analysis = analyze_with_influence(icelandic_text, formality_bench)
-    toxicity_analysis = analyze_with_influence(translated_text, toxicity_bench)
-    politeness_analysis = analyze_with_influence(translated_text, politeness_bench)
+    # 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
-    Sentiment Analysis with Influential Words: {sentiment_analysis}\n
-    Formality Analysis with Influential Words: {formality_analysis}\n
-    Toxicity Analysis with Influential Words: {toxicity_analysis}\n
-    Politeness Analysis with Influential Words: {politeness_analysis}
+    Lowest Score Aspect: {lowest_aspect}\n
+    Influential Words in {lowest_aspect}: {influential_words}
     """
     return analysis_results.strip()
 
@@ -80,7 +93,7 @@ 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 sentiment, formality, toxicity, and politeness.")
+                    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()