Update app.py

app.py

@@ -89,4 +89,25 @@ else:
                         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:
+                       
+                           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
+                inputs = t5_tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
+                summary_ids = t5_model.generate(inputs, max_length=150, num_beams=4, early_stopping=True)
+
+                # Decode the generated text
+                generated_text = t5_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+                # Display the final generated response
+                st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'> Final Generated Response with LLM: </p>", unsafe_allow_html=True)
+                st.success(generated_text)
+
+            except Exception as e:
+                st.warning(f"Error processing question or generating answer: {str(e)}")
+                st.warning("Please retype your question and make sure to use the column name and cell value correctly.")
+
+st.balloons()