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| from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool | |
| from datetime import datetime, date | |
| import requests | |
| import pandas as pd | |
| import pytz | |
| import os | |
| import yaml | |
| from tools.final_answer import FinalAnswerTool | |
| from Gradio_UI import GradioUI | |
| # Below is an example of a tool that does nothing. Amaze us with your creativity ! | |
| def fetch_historical_price_data(symbol: str, start_date: str) -> list[dict]: | |
| """ | |
| Fetch daily historical stock price data for a given stock symbol from the start_date. Use | |
| the date validation logic in the tool to validate the date. | |
| Args: | |
| symbol: Stock market symbol such as 'AAPL', 'MSFT', 'NVDA' | |
| start_date: Start date in 'YYYY-MM-DD' format | |
| """ | |
| FMP_KEY = os.getenv("FMP_KEY") | |
| if not FMP_KEY: | |
| return [{"error": "Missing FMP_KEY environment variable"}] | |
| # date validation | |
| today_dt = datetime.now().date() | |
| try: | |
| start_dt = datetime.strptime(start_date, "%Y-%m-%d").date() | |
| except ValueError: | |
| return [{"error": "Invalid date format. Use 'YYYY-MM-DD'."}] | |
| if start_dt > today_dt: | |
| return [{"error": f"Start date {start_date} is in the future (after today = {today_dt})."}] | |
| url = f"https://financialmodelingprep.com/stable/historical-price-eod/non-split-adjusted?symbol={symbol}&from={start_date}&apikey={FMP_KEY}" | |
| try: | |
| response = requests.get(url) | |
| if response.status_code != 200: | |
| return [{"error": f"HTTP {response.status_code} from FMP API"}] | |
| df = pd.DataFrame(response.json()) | |
| df["symbol"] = symbol | |
| return df.to_dict(orient="records") | |
| except requests.RequestException as e: | |
| return [{"error": f"Request failed for {symbol}: {str(e)}"}] | |
| 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=[final_answer, image_generation_tool, get_current_time_in_timezone, fetch_historical_price_data], ## 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() |