app.py

from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
import datetime
import requests
import pytz
import yaml
from tools.final_answer import FinalAnswerTool
#from requests import Request, Session
from requests.exceptions import ConnectionError, Timeout, TooManyRedirects
import json
from typing import Dict, Any, Optional

from Gradio_UI import GradioUI

verbose = True
if verbose: print("Running app.py")

################### UTILITY ###############################################
def top_10_items_from_json(json_str: str) -> dict[str, int]:
    # Parse the JSON string into a dictionary
    data = json.loads(json_str)
    
    # Sort the dictionary by value in descending order
    sorted_items = sorted(data.items(), key=lambda item: item[1], reverse=True)
    
    # Get the top 10 items
    top_10 = sorted_items[:10]
    
    # Convert the list of tuples back into a dictionary
    top_10_dict = dict(top_10)
    
    return top_10_dict
################### END: UTILITY ###############################################

# Below is an example of a tool that does nothing. Amaze us with your creativity !
@tool
def my_custom_tool(arg1:str, arg2:int)-> str: #it's important to specify the return type
    #Keep this format for the description / args / args description but feel free to modify the tool
    """A tool that does nothing yet 
    Args:
        arg1: the first argument
        arg2: the second argument
    """
    return "What magic will you build ?"

@tool
def fetch_active_crypto() -> Optional[Dict[str, Any]]:
    """A tool that fetches all active crypto by market cap in USD.

    Returns:
        Optional[Dict[str, Any]]: A dictionary containing the top 10 cryptocurrencies by market cap,
                                   or None if an error occurs.
    """
    url = 'https://sandbox-api.coinmarketcap.com/v1/cryptocurrency/listings/latest'
    parameters = {
        'start': '1',
        'limit': '5000',
        'convert': 'USD'
    }
    headers = {
        'Accepts': 'application/json',
        'X-CMC_PRO_API_KEY': 'b54bcf4d-1bca-4e8e-9a24-22ff2c3d462c',
    }

    session = requests.Session()
    session.headers.update(headers)

    try:
        response = session.get(url, params=parameters)
        response.raise_for_status()  # Raise an exception for HTTP errors
        data = json.loads(response.text)

        # Extract the top 10 cryptocurrencies by market cap
        if 'data' in data:
            sorted_crypto = sorted(data['data'], key=lambda x: x['quote']['USD']['market_cap'], reverse=True)
            top_10 = sorted_crypto[:10]
            return {crypto['name']: crypto['quote']['USD'] for crypto in top_10}
        else:
            print("No data found in the response.")
            return None

    except (ConnectionError, Timeout, TooManyRedirects, requests.exceptions.HTTPError) as e:
        print(f"An error occurred: {e}")
        return None
    

@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()

############# MODEL SELECTION ################################################

# 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,
# )

MODEL_IDS = [
    #'https://wxknx1kg971u7k1n.us-east-1.aws.endpoints.huggingface.cloud/',
    #'https://jc26mwg228mkj8dw.us-east-1.aws.endpoints.huggingface.cloud/', 
    # 'https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud'
    #'meta-llama/Llama-3.2-1B-Instruct', ## Does a poor job of interpreting my questions and matching them to the tools
    'Qwen/Qwen2.5-Coder-32B-Instruct',
    'Qwen/Qwen2.5-Coder-14B-Instruct',
    'Qwen/Qwen2.5-Coder-7B-Instruct',
    'Qwen/Qwen2.5-Coder-3B-Instruct',
    'Qwen/Qwen2.5-Coder-1.5B-Instruct'
    # Add here wherever model is working for you
]

def is_model_overloaded(model_url):
    """Verify if the model is overloaded doing a test call."""
    try:
        response = requests.post(model_url, json={"inputs": "Test"})
        if verbose: 
            print(response.status_code)
        if response.status_code == 503:  # 503 Service Unavailable = Overloaded
            return True
        if response.status_code == 404:  # 404 Client Error: Not Found 
            return True
        if response.status_code == 424:  # 424 Client Error: Failed Dependency for url:
            return True
        return False
    except requests.RequestException:
        return True  # if there are an error is overloaded

def get_available_model():
    """Select the first model available from the list."""
    for model_url in MODEL_IDS:
        print("trying",model_url)
        if not is_model_overloaded(model_url):
            return model_url
    return MODEL_IDS[0]  # if all are failing, use the first model by dfault

if verbose: print("Checking available models.")

selected_model_id = get_available_model()

model = HfApiModel(
    max_tokens=1048,
    temperature=0.5,
    #model_id='meta-llama/Llama-3.2-1B-Instruct',
    #model_id='Qwen/Qwen2.5-Coder-32B-Instruct',
    #model_id = 'Qwen/Qwen2.5-Coder-1.5B-Instruct',    
    model_id = selected_model_id, # model available selected from the list automatically
    custom_role_conversions=None,
)

############# END: MODEL SELECTION ################################################



# 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_active_crypto], ## 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()