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9b5b26a c19d193 fce901f 6aae614 8fe992b 9b5b26a 538e46e 9b5b26a 538e46e 9b5b26a 538e46e 9b5b26a 8c01ffb 6aae614 ae7a494 e121372 bf6d34c 29ec968 fe328e0 13d500a 8c01ffb 9b5b26a 8c01ffb 861422e 9b5b26a 8c01ffb 8fe992b 4e4e5ea 8c01ffb 861422e 8fe992b 9b5b26a 8c01ffb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
import datetime
import requests
import pytz
import yaml
import numpy as np
from tools.final_answer import FinalAnswerTool
from Gradio_UI import GradioUI
@tool
def simple_btc_trend_tool(num_points: int = 100 ) -> str:
"""A tool that calculates the current BTC trend using a simple linear regression over the latest measurements.
Args:
num_points: Number of latest data points to use for the regression. Default is 100.
Returns:
A string describing the BTC trend (rising, falling, or stable) along with the calculated slope.
"""
try:
# Fetch BTC market data for the last day from CoinGecko API
# Timeframe is 5 min
url = "https://api.coingecko.com/api/v3/coins/bitcoin/market_chart"
params = {
"vs_currency": "usd",
"days": "1",
}
response = requests.get(url, params=params)
data = response.json()
prices = data.get("prices", [])
if len(prices) < num_points:
return "Not enough data points available to calculate the trend."
# Select the last num_points measurements
recent_prices = prices[-num_points:]
print('recent_prices: ', recent_prices)
# Define the x axis as the index of the price points
indexes = [i for i in range(num_points)]
price_values = [ pt[1] for pt in recent_prices ]
# Perform a simple linear regression using numpy's polyfit (y = mx + q )
slope, intercept = np.polyfit(indexes, price_values, 1)
# Interpret the slope (m) to determine the trend
if slope > 0:
trend = "rising"
elif slope < 0:
trend = "falling"
else:
trend = "stable"
return f"Based on the last {num_points} data points, BTC is {trend} with a slope of {slope:.4f} USD per second."
except Exception as e:
return f"Error computing BTC trend: {str(e)}"
@tool
def get_current_time_in_timezone(timezone: str) -> str:
"""A tool that fetches the current local time in a specified timezone.
Args:
timezone: A string representing a valid timezone (e.g., 'America/New_York').
"""
try:
# Create timezone object
tz = pytz.timezone(timezone)
# Get current time in that timezone
local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
return f"The current local time in {timezone} is: {local_time}"
except Exception as e:
return f"Error fetching time for timezone '{timezone}': {str(e)}"
final_answer = FinalAnswerTool()
# 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='Qwen/Qwen2.5-Coder-32B-Instruct',# 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=[image_generation_tool, get_current_time_in_timezone, simple_btc_trend_tool, final_answer], ## add your tools here (don't remove final answer)
max_steps=6,
verbosity_level=1,
grammar=None,
planning_interval=None,
name=None,
description=None,
prompt_templates=prompt_templates
)
GradioUI(agent).launch() |