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import pandas as pd | |
from datetime import datetime | |
import os | |
def submit_conversation(messages, user_name, start_time, model_number, prompt_architecture): | |
os.makedirs("conversations", exist_ok=True) | |
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") | |
base_file_name = f"conversations/{timestamp}_convo" | |
# Save conversation as CSV | |
csv_file_name = f"{base_file_name}.csv" | |
data = [{"Role": msg["role"], "Content": msg["content"]} for msg in messages] | |
df = pd.DataFrame(data) | |
df.to_csv(csv_file_name, index=False) | |
# Calculate metadata | |
end_time = datetime.now() | |
total_time = end_time - start_time | |
num_turns = len(messages) | |
# Save metadata as TXT | |
txt_file_name = f"{base_file_name}.txt" | |
with open(txt_file_name, "w") as file: | |
file.write(f"User Name: {user_name}\n") | |
file.write(f"Conversation Date & Time: {end_time.strftime('%Y-%m-%d %H:%M:%S')}\n") | |
file.write(f"Total Turns: {num_turns}\n") | |
file.write(f"Total Conversation Time: {total_time}\n") | |
file.write(f"Model Number: {model_number}\n") | |
file.write(f"Prompt Architecture: {prompt_architecture}\n") | |
return csv_file_name, txt_file_name |