Update app.py

app.py

@@ -23,7 +23,7 @@ style = '''
 '''
 st.markdown(style, unsafe_allow_html=True)
 
-st.markdown('<p style="font-family:sans-serif;font-size: 1.9rem;"> HertogAI Question Answering using TAPAS</p>', unsafe_allow_html=True)
+st.markdown('<p style="font-family:sans-serif;font-size: 1.9rem;"> HertogAI Q&A using TAPAS and Model Language</p>', unsafe_allow_html=True)
 st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'>Pre-trained TAPAS model runs on max 64 rows and 32 columns data. Make sure the file data doesn't exceed these dimensions.</p>", unsafe_allow_html=True)
 
 # Initialize TAPAS pipeline
@@ -97,18 +97,15 @@ else:
                 coordinates = raw_answer.get('coordinates', [])
                 cells = raw_answer.get('cells', [])
                 
-                # Create a base sentence
-                if aggregator:
-                    base_sentence = f"The {aggregator.lower()} of the selected data is {answer}."
-                    if coordinates and cells:
-                        rows_info = [f"Row {coordinate[0]+1}, Column {df.columns[coordinate[1]]} with value {cell}" 
-                                     for coordinate, cell in zip(coordinates, cells)]
-                        rows_description = " and ".join(rows_info)
-                        base_sentence += f" This includes the following data: {rows_description}."
-                else:
-                    base_sentence = f"The answer is: {answer}"
-
-                # Construct the full input for T5 model by including the original question
+                # Construct a base sentence replacing 'SUM' with the query term
+                base_sentence = f"The {question.lower()} of the selected data is {answer}."
+                if coordinates and cells:
+                    rows_info = [f"Row {coordinate[0] + 1}, Column '{df.columns[coordinate[1]]}' with value {cell}" 
+                                 for coordinate, cell in zip(coordinates, cells)]
+                    rows_description = " and ".join(rows_info)
+                    base_sentence += f" This includes the following data: {rows_description}."
+
+                # Generate a fluent response using the T5 model, rephrasing the base sentence
                 input_text = f"Given the question: '{question}', generate a more human-readable response: {base_sentence}"
 
                 # Tokenize the input and generate a fluent response using T5