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import os
import PIL.Image
from dotenv import load_dotenv
from loguru import logger
from smolagents import AzureOpenAIServerModel, CodeAgent
from src.file_handler.parse import parse_file
load_dotenv()
class Agent:
def __init__(self):
model = AzureOpenAIServerModel(
model_id=os.getenv("AZURE_OPENAI_MODEL_ID"),
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
api_version=os.getenv("OPENAI_API_VERSION"),
)
self.agent = CodeAgent(
tools=[],
model=model,
add_base_tools=True, # Add any additional base tools
# planning_interval=3,
)
logger.info("BasicAgent initialized.")
def __call__(self, question: str, file_name: str) -> str:
logger.info(
f"Agent received question (first 50 chars): {question[:50]}..."
)
images = None
if file_name:
content = parse_file(task_id, file_name, api_url)
if content:
if isinstance(
content, PIL.Image.Image
): # Parse content as image
images = [content]
else: # Append content to question
question += f"\n\nAttached content:\n{content}"
logger.info(f"Question with content: {question}")
answer = self.agent.run(question, images=images)
logger.info(f"Agent returning answer: {answer}")
return answer
if __name__ == "__main__":
import requests
api_url = "https://agents-course-unit4-scoring.hf.space"
question_url = f"{api_url}/random-question"
data = requests.get(question_url).json()
agent = Agent()
task_id = data["task_id"]
question = data["question"]
file_name = data["file_name"]
logger.info(
f"Task ID: {task_id}\nQuestion: {question}\nFile Name: {file_name}\n\n"
)
answer = agent(question, file_name)
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