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Refactor app.py to import main from main_v2.py, enhancing modularity. Introduce main_v2.py with a new agent implementation, including OpenTelemetry integration and YAML-based prompt templates. Update requirements.txt to reflect the latest smolagents version. Add tasks.json for VSCode to streamline development workflows.
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import importlib | |
import logging | |
import os | |
import requests | |
import yaml | |
from dotenv import find_dotenv, load_dotenv | |
from litellm._logging import _disable_debugging | |
from openinference.instrumentation.smolagents import SmolagentsInstrumentor | |
from phoenix.otel import register | |
# from smolagents import CodeAgent, LiteLLMModel, LiteLLMRouterModel | |
from smolagents import CodeAgent, LiteLLMModel | |
from smolagents.default_tools import DuckDuckGoSearchTool, VisitWebpageTool | |
from smolagents.monitoring import LogLevel | |
from agents import create_data_analysis_agent, create_media_agent, create_web_agent | |
from utils import extract_final_answer | |
_disable_debugging() | |
# Configure OpenTelemetry with Phoenix | |
register() | |
SmolagentsInstrumentor().instrument() | |
logging.basicConfig( | |
level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" | |
) | |
logger = logging.getLogger(__name__) | |
load_dotenv(find_dotenv()) | |
API_BASE = os.getenv("API_BASE") | |
API_KEY = os.getenv("API_KEY") | |
MODEL_ID = os.getenv("MODEL_ID") | |
model = LiteLLMModel( | |
api_base=API_BASE, | |
api_key=API_KEY, | |
model_id=MODEL_ID, | |
) | |
web_agent = create_web_agent(model) | |
data_agent = create_data_analysis_agent(model) | |
media_agent = create_media_agent(model) | |
prompt_templates = yaml.safe_load( | |
importlib.resources.files("smolagents.prompts") | |
.joinpath("code_agent.yaml") | |
.read_text() | |
) | |
agent = CodeAgent( | |
# add_base_tools=True, | |
additional_authorized_imports=[ | |
"json", | |
"pandas", | |
"numpy", | |
"re", | |
# "requests" | |
# "urllib.request", | |
], | |
# max_steps=10, | |
# managed_agents=[web_agent, data_agent, media_agent], | |
model=model, | |
prompt_templates=prompt_templates, | |
tools=[ | |
# web_search, | |
# perform_calculation, | |
DuckDuckGoSearchTool(max_results=1), | |
VisitWebpageTool(max_output_length=256), | |
], | |
step_callbacks=None, | |
verbosity_level=LogLevel.ERROR, | |
) | |
agent.visualize() | |
def main(task: str): | |
result = agent.run( | |
additional_args=None, | |
images=None, | |
max_steps=3, | |
reset=True, | |
stream=False, | |
task=task, | |
) | |
logger.info(f"Result: {result}") | |
return extract_final_answer(result) | |
if __name__ == "__main__": | |
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
api_url = DEFAULT_API_URL | |
questions_url = f"{api_url}/questions" | |
submit_url = f"{api_url}/submit" | |
response = requests.get(questions_url, timeout=15) | |
response.raise_for_status() | |
questions_data = response.json() | |
for question_data in questions_data[:1]: | |
file_name = question_data["file_name"] | |
level = question_data["Level"] | |
question = question_data["question"] | |
task_id = question_data["task_id"] | |
logger.info(f"Question: {question}") | |
# logger.info(f"Level: {level}") | |
if file_name: | |
logger.info(f"File Name: {file_name}") | |
# logger.info(f"Task ID: {task_id}") | |
final_answer = main(question) | |
logger.info(f"Final Answer: {final_answer}") | |
logger.info("--------------------------------") | |