Update app.py

app.py

@@ -84,7 +84,6 @@ def get_sentiment_values(text: str):
     sentiments = pipe(text)[0]
     return {k:v for k,v in [(list(sentiment.values())[0], list(sentiment.values())[1]) for sentiment in sentiments]}
 
-# Modify the predict_subjectivity function to return additional information
 def analyze(text):
     # Extract sentiment values
     sentiment_values = get_sentiment_values(text)
@@ -120,12 +119,29 @@ def analyze(text):
     # Calculate probabilities using softmax
     prob_sentiment = torch.nn.functional.softmax(logits_sentiment, dim=1)[0]
 
-    # Format the output
-    return {
-        'Positive': f"{positive:.2%}", 'Neutral': f"{neutral:.2%}", 'Negative': f"{negative:.2%}",
-        'Sent-Subj OBJ': f"{prob_sentiment[0]:.2%}", 'Sent-Subj SUBJ': f"{prob_sentiment[1]:.2%}",
-        'TextOnly OBJ': f"{prob_base[0]:.2%}", 'TextOnly SUBJ': f"{prob_base[1]:.2%}"
-    }
+    # Prepare data for the BarPlot (numerical values)
+    chart_data = [
+        {"category": "Positive", "value": positive},
+        {"category": "Neutral", "value": neutral},
+        {"category": "Negative", "value": negative},
+        {"category": "Sent-Subj OBJ", "value": prob_sentiment[0].item()},
+        {"category": "Sent-Subj SUBJ", "value": prob_sentiment[1].item()},
+        {"category": "TextOnly OBJ", "value": prob_base[0].item()},
+        {"category": "TextOnly SUBJ", "value": prob_base[1].item()}
+    ]
+
+    # Prepare data for the Dataframe (string values)
+    table_data = [
+        ["Positive", f"{positive:.2%}"],
+        ["Neutral", f"{neutral:.2%}"],
+        ["Negative", f"{negative:.2%}"],
+        ["Sent-Subj OBJ", f"{prob_sentiment[0]:.2%}"],
+        ["Sent-Subj SUBJ", f"{prob_sentiment[1]:.2%}"],
+        ["TextOnly OBJ", f"{prob_base[0]:.2%}"],
+        ["TextOnly SUBJ", f"{prob_base[1]:.2%}"]
+    ]
+
+    return chart_data, table_data
 
 # Update the Gradio interface
 with gr.Blocks(theme=gr.themes.Soft(), css="""