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import openai
import pandas as pd
import random
import string
import os
from typing import List, Dict
import argparse
from dotenv import load_dotenv
starting_id = 0
def new_id() -> int:
"""Return a new ID that is the next integer in the sequence"""
global starting_id
temp = starting_id
starting_id += 1
return temp
def setup_openai_client(api_key: str):
"""Set up and return an OpenAI client."""
client = openai.OpenAI(api_key=api_key)
return client
def read_existing_descriptions(file_path: str) -> set:
"""Read and return existing descriptions from a CSV file using pandas."""
if not os.path.exists(file_path):
return set()
df = pd.read_csv(file_path)
if 'description' in df.columns:
return set(df['description'].str.lower())
return set()
def get_prompt_for_category(category: str, count: int, max_length: int, target_avg_length: int) -> str:
"""Return a specific prompt based on the category."""
if category == "landscapes":
return f"""
Generate {count} short, generic descriptions of landscapes.
Requirements:
Each landscape description should be concise, around {target_avg_length} characters on average
No description should exceed {max_length} characters
Do NOT include any brand names, trademarks, or personal names
Do NOT include any people, even generically
Descriptions should be varied and creative
Only provide the descriptions, one per line, with no numbering or additional text
Focus on natural scenes, vistas, and environments
Examples:
a purple forest at dusk
a lighthouse overlooking the ocean
a green lagoon under a cloudy sky
a snowy plain
a starlit night over snow-covered peaks
"""
elif category == "abstract":
return f"""
Generate {count} short, generic descriptions of abstract art or geometric compositions.
Requirements:
Each abstract description should be concise, around {target_avg_length} characters on average
No description should exceed {max_length} characters
Do NOT include any brand names, trademarks, or personal names
Focus on geometric shapes, patterns, and colors
Be creative with color combinations and spatial arrangements
Only provide the descriptions, one per line, with no numbering or additional text
Examples:
crimson rectangles forming a chaotic grid
purple pyramids spiraling around a bronze cone
magenta trapezoids layered on a transluscent silver sheet
khaki triangles and azure crescents
a maroon dodecahedron interwoven with teal threads
"""
elif category == "fashion":
return f"""
Generate {count} short, generic descriptions of fashion items and clothing.
Requirements:
Each fashion description should be concise, around {target_avg_length} characters on average
No description should exceed {max_length} characters
Do NOT include any brand names, trademarks, or personal names
Do NOT include any people, even generically
Focus on clothing items, accessories, fabrics, patterns, and colors
Be specific about materials, cuts, and design features
Only provide the descriptions, one per line, with no numbering or additional text
Examples:
gray wool coat with a faux fur collar
burgundy corduroy pants with patch pockets and silver buttons
orange corduroy overalls
a purple silk scarf with tassel trim
black and white checkered pants
"""
else:
# Generic prompt for additional categories
return f"""
Generate {count} short, generic descriptions of {category}.
Requirements:
- Each description should be concise, around {target_avg_length} characters on average
- No description should exceed {max_length} characters
- Do NOT include any brand names, trademarks, or personal names
- Do NOT include any people, even generically
- Descriptions should be varied and creative
- Only provide the descriptions, one per line, with no numbering or additional text
"""
def generate_descriptions(
client,
categories: List[str],
count_per_category: int,
max_length: int = 200,
target_avg_length: int = 50,
existing_descriptions: set = set()
) -> Dict[str, List[str]]:
"""Generate descriptions for each category using GPT-4o mini with separate prompts."""
results = {category: [] for category in categories}
for category in categories:
print(f"Generating {count_per_category} descriptions for category: {category}")
# Get the specific prompt for this category
system_prompt = get_prompt_for_category(
category=category,
count=count_per_category,
max_length=max_length,
target_avg_length=target_avg_length
)
unique_descriptions = set()
try:
while len(unique_descriptions) < count_per_category:
remaining = count_per_category - len(unique_descriptions)
print(f"Generating {remaining} more unique descriptions for {category}...")
# Make the API call
response = client.chat.completions.create(
model="gpt-4o-mini", # Using GPT-4o mini as requested
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": f"Generate {remaining} {category} descriptions."}
],
temperature=0.7,
max_tokens=8000
)
# Process the response
content = response.choices[0].message.content
descriptions = [line.strip() for line in content.split('\n') if line.strip()]
# Filter out any descriptions that are too long, already existing, or duplicates
for desc in descriptions:
desc_lower = desc.lower()
if (len(desc) <= max_length and
desc_lower not in {d.lower() for d in unique_descriptions} and
desc_lower not in existing_descriptions):
unique_descriptions.add(desc)
if len(unique_descriptions) >= count_per_category:
break
# Convert to list
results[category] = list(unique_descriptions)
except Exception as e:
print(f"Error generating descriptions for {category}: {e}")
return results
def write_to_csv_pandas(
descriptions_dict: Dict[str, List[str]],
output_file: str,
append: bool = False
) -> None:
"""Write or append the generated descriptions to a CSV file using pandas."""
# Create a list of dictionaries for our new data
data = []
for category, descriptions in descriptions_dict.items():
for description in descriptions:
data.append({
"id": new_id(),
"description": description,
"category": category
})
# Create a DataFrame from our data
new_df = pd.DataFrame(data)
# Append or create new file
if append and os.path.exists(output_file):
existing_df = pd.read_csv(output_file)
combined_df = pd.concat([existing_df, new_df], ignore_index=True)
combined_df.to_csv(output_file, index=False)
print(f"Appended {len(new_df)} descriptions to {output_file}")
else:
new_df.to_csv(output_file, index=False)
print(f"Wrote {len(new_df)} descriptions to {output_file}")
def main():
csv_path = "data/descriptions.csv"
output_file = "data/descriptions.csv"
append = True
count = 50
categories = ["landscapes", "abstract", "fashion"]
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
if api_key is None:
raise ValueError("OPENAI_API_KEY is not set")
# Set up the OpenAI client
client = setup_openai_client(api_key)
# If appending, read existing descriptions to avoid duplicates
existing_descriptions = set()
if append and os.path.exists(csv_path):
global starting_id
starting_id = pd.read_csv(csv_path)["id"].max() + 1
existing_descriptions = read_existing_descriptions(csv_path)
print(f"Found {len(existing_descriptions)} existing descriptions")
# Generate the descriptions
descriptions_dict = generate_descriptions(
client=client,
categories=categories,
count_per_category=count,
existing_descriptions=existing_descriptions
)
# Write to CSV using pandas
write_to_csv_pandas(
descriptions_dict=descriptions_dict,
output_file=output_file,
append=append
)
if __name__ == "__main__":
main() |