Update app.py

app.py

@@ -16,9 +16,8 @@ def ask_llm_chunk(chunk, questions):
         st.write(f"An error occurred: {e}")
         return ["Error occurred while tokenizing"] * len(questions)
 
-    if inputs["input_ids"].shape[1] > 512:
-        st.warning("Token limit exceeded for chunk")
-        return ["Token limit exceeded for chunk"] * len(questions)
+    # Debugging line
+    st.write(f"Token shape: {inputs['input_ids'].shape[1]}")
 
     outputs = model(**inputs)
     predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
@@ -30,11 +29,13 @@ def ask_llm_chunk(chunk, questions):
     answers = []
     for coordinates in predicted_answer_coordinates:
         if len(coordinates) == 1:
+            # Debugging lines
+            st.write(f"DataFrame shape: {chunk.shape}")
+            st.write(f"DataFrame columns: {chunk.columns}")
+            st.write(f"Trying to access row {coordinates[0][0]}, col {coordinates[0][1]}")
+
             row, col = coordinates[0]
             try:
-                st.write(f"DataFrame shape: {chunk.shape}")  # Debugging line
-                st.write(f"DataFrame columns: {chunk.columns}")  # Debugging line
-                st.write(f"Trying to access row {row}, col {col}")  # Debugging line
                 value = chunk.iloc[row, col]
                 if isinstance(value, pd.Series):
                     answers.append(value.values)
@@ -42,6 +43,7 @@ def ask_llm_chunk(chunk, questions):
                     answers.append(value.item() if hasattr(value, 'item') else value)
             except Exception as e:
                 st.write(f"An error occurred: {e}")
+                answers.append("Error")
         else:
             cell_values = []
             for coordinate in coordinates:
@@ -54,6 +56,7 @@ def ask_llm_chunk(chunk, questions):
                         cell_values.append(value.item() if hasattr(value, 'item') else value)
                 except Exception as e:
                     st.write(f"An error occurred: {e}")
+                    cell_values.append("Error")
             answers.append(", ".join(map(str, cell_values)))
 
     return answers