from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool from typing import Dict import datetime import requests import pytz import yaml from tools.final_answer import FinalAnswerTool from Gradio_UI import GradioUI @tool def calculate_bandwidth(users: int, usage: Dict[str, int]) -> float: """Calculate the recommended internet speed based on user inputs. Always make sure to apply overhead calculation to the bandwidth to get the total_bandwidth_with_overhead Args: users: The total number of users requiring internet access. usage: A dictionary with usage categories as keys and the number of users per category as values. Expected keys are: - "browsing": Number of users browsing the web. - "video_call": Number of users on video calls. - "hd_streaming": Number of users streaming in HD. - "4k_streaming": Number of users streaming in 4K. - "gaming": Number of users gaming online. - "remote_work": Number of users working remotely. """ usage_requirements = { "browsing": 1, # Mbps per user "video_call": 2, # Mbps per user "hd_streaming": 5, # Mbps per user "4k_streaming": 25, # Mbps per user "gaming": 10, # Mbps per user "remote_work": 3 # Mbps per user } total_bandwidth = sum(usage_requirements[activity] * usage.get(activity, 0) for activity in usage_requirements) overhead = 1.2 # 20% overhead for seamless experience # Apply overhead of 1.2 total_bandwidth_with_overhead = total_bandwidth * overhead return round(total_bandwidth_with_overhead, 2) final_answer = FinalAnswerTool() #duck_duck_go_search = DuckDuckGoSearchTool() # If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder: # model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' model = HfApiModel( max_tokens=2096, temperature=0.5, model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud/',# it is possible that this model may be overloaded custom_role_conversions=None, ) # Import tool from Hub image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True) with open("prompts.yaml", 'r') as stream: prompt_templates = yaml.safe_load(stream) agent = CodeAgent( model=model, tools=[final_answer,calculate_bandwidth], ## Internet bandwidth Calculator Tool max_steps=6, verbosity_level=1, grammar=None, planning_interval=None, name=None, description=None, prompt_templates=prompt_templates ) GradioUI(agent).launch()