Update app.py

app.py

@@ -19,7 +19,7 @@ def main():
         return
 
     st.sidebar.image("https://i.ibb.co/bX6GdqG/insightly-wbg.png", use_column_width=True)
-    st.title("Chat with Columns 💬")
+    st.title("Column Analysis 💬")
 
     csv_files = st.file_uploader("Upload CSV files", type="csv", accept_multiple_files=True)
     if csv_files:
@@ -53,17 +53,19 @@ def main():
                     st.error(f"The column '{column_for_prompt}' does not exist in the CSV file: {csv_file.name}")
                     continue
 
-                # Perform any necessary data preprocessing or feature engineering here
-                # You can modify the code based on your specific requirements
+
 
                 # Example: Accessing columns from the DataFrame
                 column_data = df[column_for_prompt]
 
                 # Loop through each row in the specified column and pass the user input as prompt
                 for row_value in column_data:
-                    original_rows_list.append(row_value)
+                    # Convert the row value to a string to handle missing or NaN values
+                    row_value_str = str(row_value)
+                    original_rows_list.append(row_value_str)
+
                     # Example: Using the preprocessed data with the OpenAI API
-                    llm_response = llm.predict(row_value + " " + user_input)
+                    llm_response = llm.predict(row_value_str + " " + user_input)
                     responses_list.append(llm_response)
 
                     # Introduce a delay of 1 second between API calls to reduce the rate of requests
@@ -75,10 +77,14 @@ def main():
                     "Responses": responses_list
                 })
 
+                # Add bold formatting to the "Responses" column
+                response_df["Responses"] = response_df["Responses"].apply(lambda x: f"**{x}**")
+
                 # Offer the option to download the responses as a CSV file
                 if st.button("Download Responses as CSV"):
                     with BytesIO() as output_file:
                         response_df.to_csv(output_file, index=False)
+
                         st.download_button(
                             label="Download CSV",
                             data=output_file.getvalue(),
@@ -87,4 +93,4 @@ def main():
                         )
 
 if __name__ == "__main__":
-    main()
+    main()