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from tools.final_answer import FinalAnswerTool as FinalAnswer
from tools.classify_topic import SimpleTool as ClassifyTopic
from tools.extract_news_article_content import SimpleTool as ExtractNewsArticleContent
from tools.summarize_news import SimpleTool as SummarizeNews
from tools.fetch_lastest_news_titles_and_urls import SimpleTool as FetchLastestNewsTitlesAndUrls
import yaml
import spaces
import os
from smolagents import CodeAgent, TransformersModel
from gradio_ui import GradioUI
import torch
def set_seed(seed: int):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
set_seed(42)
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
fetch_lastest_news_titles_and_urls = FetchLastestNewsTitlesAndUrls()
summarize_news = SummarizeNews()
extract_news_article_content = ExtractNewsArticleContent()
classify_topic = ClassifyTopic()
final_answer = FinalAnswer()
model = TransformersModel(
max_new_tokens=2000,
model_id='Qwen/Qwen2.5-Coder-3B-Instruct',
)
with open(os.path.join(CURRENT_DIR, "prompts.yaml"), 'r') as stream:
prompt_templates = yaml.safe_load(stream)
agent_news_agent = CodeAgent(
model=model,
tools=[fetch_lastest_news_titles_and_urls, summarize_news,
extract_news_article_content, classify_topic],
managed_agents=[],
max_steps=20,
verbosity_level=2,
grammar=None,
planning_interval=None,
name='news_agent',
description="This agent is a smart news aggregator that fetches, summarizes, and classifies real-time news updates.",
executor_type='local',
executor_kwargs={},
max_print_outputs_length=None,
prompt_templates=prompt_templates
)
@spaces.GPU
def run_agent():
GradioUI(agent_news_agent).launch()
if __name__ == "__main__":
run_agent()
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