Update app.py

app.py

@@ -296,7 +296,6 @@ def generate(
     for user, assistant in chat_history:
         conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
     conversation.append({"role": "user", "content": message})
-    print(f"chat history after the appending happened: {chat_history}")
 
     input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
     if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
@@ -333,9 +332,9 @@ def generate(
     # Fix bug that last answer is not recorded! 
     # Parse the outputs into a readable sentence and record them
     # Filter out empty strings and join the remaining strings with spaces
-    readable_sentence = ' '.join(filter(lambda x: x.strip(), outputs))
+    #readable_sentence = ' '.join(filter(lambda x: x.strip(), outputs))
     # Print the readable sentence
-    print(readable_sentence)
+    #print(readable_sentence)
 
     # Save chat history to .csv file on HuggingFace Hub 
     #pd.DataFrame(conversation).to_csv(DATA_FILE, index=False)
@@ -367,7 +366,7 @@ def generate(
 
     # Save chat history to .csv file on HuggingFace Hub 
     # Generate filename with bot id and session id
-    filename = f"{session_index}_{onPage}{DATA_FILENAME}"
+    filename = f"{session_index}_{onPage}_{DATA_FILENAME}"
     data_file = os.path.join(DATA_DIRECTORY, filename)
     
     # Generate timestamp
@@ -380,7 +379,7 @@ def generate(
         
         # Add timestamp column
         conversation_df = pd.DataFrame(conversation)
-        conversation_df['readable_sentence'] = readable_sentence
+        #conversation_df['readable_sentence'] = readable_sentence
         conversation_df['timestamp'] = timestamp
         
         # Append new conversation to existing data
@@ -389,7 +388,7 @@ def generate(
     else:
         # If file doesn't exist, create new file with conversation data
         conversation_df = pd.DataFrame(conversation)
-        conversation_df['readable_sentence'] = readable_sentence
+        #conversation_df['readable_sentence'] = readable_sentence
         conversation_df['timestamp'] = timestamp
         conversation_df.to_csv(data_file, index=False)