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import openai
import pandas as pd
import os
import time
from datetime import datetime
from dotenv import load_dotenv
import sys
# Load environment variables (Ensure your API key is in key.env)
load_dotenv("key.env")
api_key = os.getenv("cgpt_key") # Get API key
# Initialize OpenAI client
client = openai.OpenAI(api_key=api_key)
# Input & output file paths >> Set desired output name here
INPUT_CSV = sys.argv[1]
OUTPUT_CSV = "openai_SST_output.csv"
# Define column names for the output file
OUTPUT_COLUMNS = [
"prompt", "response", "Flagged", "Hate", "Hate/Threatening",
"Harassment", "Harassment/Threatening", "Self-Harm", "Self-Harm/Intent",
"Self-Harm/Instructions", "Violence", "Violence/Graphic", "Sexual",
"Sexual/Minors", "Illicit", "Illicit/Violent",
"Processing_Time (s)", "Timestamp", "Model", "Response_ID"
]
# Create an output CSV at the start
if not os.path.exists(OUTPUT_CSV):
pd.DataFrame(columns=OUTPUT_COLUMNS).to_csv(OUTPUT_CSV, index=False)
# Function to check moderation
def moderate_text(text):
try:
start_time = time.time() # Start timing
response = client.moderations.create(input=text, model="omni-moderation-latest")
end_time = time.time() # End timing
moderation_result = response.results[0]
return {
"Flagged": moderation_result.flagged,
"Hate": moderation_result.categories.hate,
"Hate/Threatening": moderation_result.categories.hate_threatening,
"Harassment": moderation_result.categories.harassment,
"Harassment/Threatening": moderation_result.categories.harassment_threatening,
"Self-Harm": moderation_result.categories.self_harm,
"Self-Harm/Intent": moderation_result.categories.self_harm_intent,
"Self-Harm/Instructions": moderation_result.categories.self_harm_instructions,
"Violence": moderation_result.categories.violence,
"Violence/Graphic": moderation_result.categories.violence_graphic,
"Sexual": moderation_result.categories.sexual,
"Sexual/Minors": moderation_result.categories.sexual_minors,
"Illicit": moderation_result.categories.illicit,
"Illicit/Violent": moderation_result.categories.illicit_violent,
"Processing_Time (s)": round(end_time - start_time, 4),
"Timestamp": datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S"),
"Model": response.model,
"Response_ID": response.id
}
except Exception as e:
print(f"Error processing text: {text} -> {e}")
return None # Return None if an error occurs
# Load dataset
df = pd.read_csv(INPUT_CSV)
# Process each row
for index, row in df.iterrows():
prompt = row["prompt"]
response = row["response"]
# Moderate the response
moderation_results = moderate_text(response)
if moderation_results:
row_data = {
"prompt": prompt,
"response": response,
**moderation_results # Expands the dictionary into individual columns
}
# Append to CSV
pd.DataFrame([row_data]).to_csv(OUTPUT_CSV, mode="a", header=False, index=False)
# Print progress
print(f"Processed row {index+1}/{len(df)} - Flagged: {moderation_results['Flagged']} - Time: {moderation_results['Processing_Time (s)']}s")
print(f"✅ Moderation complete. Results saved to {OUTPUT_CSV}")
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