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""" | |
OpenAI Provider Integration | |
Handles API calls to OpenAI for text and image generation | |
""" | |
import os | |
import time | |
import json | |
import logging | |
from typing import Dict, Any, Optional, List | |
# Setup logging | |
logging.basicConfig(level=logging.INFO) | |
logger = logging.getLogger("openai") | |
try: | |
import openai | |
from openai import OpenAI | |
HAS_OPENAI = True | |
except ImportError: | |
logger.warning("OpenAI package not installed. Install with: pip install openai") | |
HAS_OPENAI = False | |
class OpenAIProvider: | |
"""OpenAI API provider for model inference""" | |
def __init__(self, api_key: Optional[str] = None): | |
"""Initialize the OpenAI provider with API key""" | |
if not HAS_OPENAI: | |
logger.error("OpenAI package not installed. Install with: pip install openai") | |
return | |
self.api_key = api_key or os.getenv("OPENAI_API_KEY") | |
if not self.api_key: | |
logger.warning("No OpenAI API key provided. Set OPENAI_API_KEY env variable.") | |
# Initialize client | |
self.client = OpenAI(api_key=self.api_key) | |
def generate_text(self, | |
prompt: str, | |
model: str = "gpt-3.5-turbo", | |
max_tokens: int = 1000, | |
temperature: float = 0.7, | |
system_message: str = "You are a helpful assistant.", | |
**kwargs) -> Dict[str, Any]: | |
"""Generate text using OpenAI models""" | |
if not HAS_OPENAI or not self.api_key: | |
return {"success": False, "error": "OpenAI package not installed or API key not provided"} | |
start_time = time.time() | |
try: | |
messages = [ | |
{"role": "system", "content": system_message}, | |
{"role": "user", "content": prompt} | |
] | |
response = self.client.chat.completions.create( | |
model=model, | |
messages=messages, | |
max_tokens=max_tokens, | |
temperature=temperature, | |
**kwargs | |
) | |
# Extract the generated text | |
generated_text = response.choices[0].message.content | |
return { | |
"success": True, | |
"text": generated_text, | |
"model": model, | |
"provider": "openai", | |
"response_time": time.time() - start_time, | |
"tokens": { | |
"prompt": response.usage.prompt_tokens, | |
"completion": response.usage.completion_tokens, | |
"total": response.usage.total_tokens | |
}, | |
"raw_response": response.model_dump() | |
} | |
except Exception as e: | |
logger.error(f"Error generating text with OpenAI: {e}") | |
return { | |
"success": False, | |
"error": str(e), | |
"response_time": time.time() - start_time, | |
"model": model, | |
"provider": "openai" | |
} | |
def generate_image(self, | |
prompt: str, | |
model: str = "dall-e-3", | |
size: str = "1024x1024", | |
quality: str = "standard", | |
n: int = 1, | |
**kwargs) -> Dict[str, Any]: | |
"""Generate image using OpenAI DALL-E models""" | |
if not HAS_OPENAI or not self.api_key: | |
return {"success": False, "error": "OpenAI package not installed or API key not provided"} | |
start_time = time.time() | |
try: | |
response = self.client.images.generate( | |
model=model, | |
prompt=prompt, | |
size=size, | |
quality=quality, | |
n=n, | |
**kwargs | |
) | |
return { | |
"success": True, | |
"image_url": response.data[0].url, # URL of the generated image | |
"model": model, | |
"provider": "openai", | |
"response_time": time.time() - start_time, | |
"raw_response": response.model_dump() | |
} | |
except Exception as e: | |
logger.error(f"Error generating image with OpenAI: {e}") | |
return { | |
"success": False, | |
"error": str(e), | |
"response_time": time.time() - start_time, | |
"model": model, | |
"provider": "openai" | |
} | |
def get_available_models(self) -> List[Dict[str, Any]]: | |
"""Get available OpenAI models""" | |
if not HAS_OPENAI or not self.api_key: | |
return [] | |
try: | |
response = self.client.models.list() | |
models = [ | |
{ | |
"id": model.id, | |
"name": model.id, | |
"created": model.created | |
} | |
for model in response.data | |
] | |
# Filter to only include completion and chat models | |
text_models = [ | |
model for model in models | |
if any(prefix in model["id"] for prefix in ["gpt-", "text-"]) | |
] | |
# Add DALL-E models (they don't show up in the list) | |
image_models = [ | |
{"id": "dall-e-3", "name": "DALL-E 3"}, | |
{"id": "dall-e-2", "name": "DALL-E 2"} | |
] | |
return { | |
"text_models": text_models, | |
"image_models": image_models | |
} | |
except Exception as e: | |
logger.error(f"Error fetching OpenAI models: {e}") | |
return [] | |
# Example usage | |
if __name__ == "__main__": | |
# Test the provider | |
provider = OpenAIProvider() | |
result = provider.generate_text("Write a short poem about AI.") | |
print(json.dumps(result, indent=2)) |