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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() |