update

visualization/roberta_visualizer.py

@@ -63,24 +63,13 @@ def create_sentiment_visualization(analysis_results):
                     
                     for model_name in models:
                         if model_name in sa_data:
-                            model_result = sa_data[model_name]
+                            model_result = sa_data.get(model_name)
                             if model_result is not None:
                                 score = model_result.get("sentiment_score", 0)
                                 label = model_result.get("label", "neutral").capitalize()
                             else:
                                 score = 0
                                 label = "Neutral"
-                            for model_name in models:
-                                if model_name in sa_data:
-                                    model_result = sa_data[model_name]
-                                    if model_result is not None:
-                                        # Now safely access model_result
-                                        score = model_result.get("sentiment_score", 0)
-                                        label = model_result.get("label", "neutral").capitalize()
-                                    else:
-                                        # Provide defaults when model_result is None
-                                        score = 0
-                                        label = "Neutral"
                             
                             # Set color based on sentiment
                             if label.lower() == "positive":
@@ -103,8 +92,10 @@ def create_sentiment_visualization(analysis_results):
                     model_scores = []
                     for model_name in models:
                         if model_name in sa_data:
-                            score = sa_data[model_name].get("sentiment_score", 0)
-                            model_scores.append((model_name, score))
+                            model_result = sa_data.get(model_name)
+                            if model_result is not None:
+                                score = model_result.get("sentiment_score", 0)
+                                model_scores.append((model_name, score))
                     
                     if len(model_scores) >= 2:
                         gauge_html = "<div style='margin: 20px 0; padding: 15px; background-color: #f8f9fa; border-radius: 5px;'>"
@@ -177,8 +168,12 @@ def create_sentiment_visualization(analysis_results):
                 model_sentences = {}
                 
                 for model_name in models:
-                    if model_name in sa_data and "sentence_scores" in sa_data[model_name] and sa_data[model_name]["sentence_scores"]:
-                        model_sentences[model_name] = sa_data[model_name]["sentence_scores"]
+                    if model_name in sa_data:
+                        model_result = sa_data.get(model_name)
+                        if model_result is not None and "sentence_scores" in model_result:
+                            sentence_scores = model_result.get("sentence_scores")
+                            if sentence_scores:
+                                model_sentences[model_name] = sentence_scores
                 
                 if model_sentences and any(len(sentences) > 0 for sentences in model_sentences.values()):
                     output_components.append(gr.Markdown("### Sentence-Level Sentiment Analysis"))