Update app.py

app.py

@@ -1,3 +1,5 @@
+from copy import deepcopy
+
 import streamlit as st
 import pandas as pd
 from io import StringIO
@@ -29,34 +31,36 @@ def ask_llm_chunk(chunk, questions):
 
     answers = []
     for coordinates in predicted_answer_coordinates:
-        for coordinate in coordinates:
-            row, col = coordinate
+        if len(coordinates) == 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]
                 st.write(f"Value accessed: {value}")  # Debugging line
-                if isinstance(value, pd.Series):
-                    answers.append(value.values)
-                else:
-                    answers.append(value.item() if hasattr(value, 'item') else value)
+                answers.append(value)
             except Exception as e:
                 st.write(f"An error occurred: {e}")
-                st.write(f"Type of error: {type(e)}")
-                st.write(f"Arguments of error: {e.args}")
-        answers.append(", ".join(map(str, [chunk.iloc[coordinate].values for coordinate in coordinates])))
+        else:
+            cell_values = []
+            for coordinate in coordinates:
+                row, col = coordinate
+                try:
+                    value = chunk.iloc[row, col]
+                    cell_values.append(value)
+                except Exception as e:
+                    st.write(f"An error occurred: {e}")
+            answers.append(", ".join(map(str, cell_values)))
 
     return answers
 
-
-
-
-
 MAX_ROWS_PER_CHUNK = 200
 
 def summarize_map_reduce(data, questions):
     dataframe = pd.read_csv(StringIO(data))
     num_chunks = len(dataframe) // MAX_ROWS_PER_CHUNK + 1
-    dataframe_chunks = np.array_split(dataframe, num_chunks)
+    dataframe_chunks = [deepcopy(chunk) for chunk in np.array_split(dataframe, num_chunks)]
     all_answers = []
     for chunk in dataframe_chunks:
         chunk_answers = ask_llm_chunk(chunk, questions)