Update app.py

app.py

@@ -48,11 +48,11 @@ def read_root():
 def predict_sentiment(input_data: TextInput):
     # Step 1: Split text into chunks
     chunks, chunk_texts, chunk_token_counts = split_text_into_chunks(input_data.text)
-    
+
     # Step 2: Analyze sentiment for each chunk and calculate the total token count
     results, total_token_count = analyze_sentiment_chunks(chunks, chunk_texts, chunk_token_counts)
-    
-    # Step 3: Return the aggregated sentiment analysis results along with total token count
+
+    # Step 3: Calculate total and overall sentiment scores
     total_neutral_score = 0
     total_positive_score = 0
     total_negative_score = 0
@@ -64,15 +64,27 @@ def predict_sentiment(input_data: TextInput):
                 total_positive_score += sentiment['score']
             elif sentiment['label'] == "Negative":
                 total_negative_score += sentiment['score']
-    
+
+    # Calculate overall scores (average per chunk)
+    num_chunks = len(results)
+    overall_neutral_score = total_neutral_score / num_chunks if num_chunks > 0 else 0
+    overall_positive_score = total_positive_score / num_chunks if num_chunks > 0 else 0
+    overall_negative_score = total_negative_score / num_chunks if num_chunks > 0 else 0
+
+    # Step 4: Return the aggregated sentiment analysis results
     if len(chunks) == 1:
-        return {"results": results}
-    
+        return {
+            "results": results
+        }
+
     return {
         "total_chunks": len(results),
         "total_token_count": total_token_count,  # Include total token count in the response
         "total_neutral_score": total_neutral_score,
         "total_positive_score": total_positive_score,
         "total_negative_score": total_negative_score,
+        "overall_neutral_score": overall_neutral_score,
+        "overall_positive_score": overall_positive_score,
+        "overall_negative_score": overall_negative_score,
         "results": results
     }