app.py

from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
import datetime
import math
import requests
import pytz
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 !
@tool
def my_custom_tool(arg1:str, arg2:int)-> str: #it's import 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 get_square_root_tool(input_number:int)-> int: #it's import 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:
        input_number: an integer whose square root is to be calculated
    """
    try:
        # get square root 
        square_root = math.sqrt(input_number)
        return f"Square root of  {input_number} is: {square_root}"
    except Exception as e:
        return f"Error fetching Square root of '{input_number}': {str(e)}" 

@tool
def get_stock_public_sentiment(stock:str)-> int: #it's import 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:
        stock: a string which  represents the ticker name or name of a stock whose public sentiment is to be calculated
    """
    search = DuckDuckGoSearchResults(backend="news",output_format="list")
    search_result = search.invoke("Tesla")  
    print(search_result)

@tool
def classify_educational_article(text: str) -> str:
    """
    Classifier for judging the educational value of web pages.
    Args:
        text: The content of the educational article to be classified.
    Returns:
        str: This function will output a dictionary with the input text, the predicted score, and an integer score between 0 and 5
    """
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/fineweb-edu-classifier")
    model = AutoModelForSequenceClassification.from_pretrained("HuggingFaceTB/fineweb-edu-classifier")
    inputs = tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
    outputs = model(**inputs)
    logits = outputs.logits.squeeze(-1).float().detach().numpy()
    score = logits.item()
    result = {"text": text,
    "score": score,
    "int_score": int(round(max(0, min(score, 5)))),
     }
    return result
     
             

@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=[final_answer,image_generation_tool,get_square_root_tool,classify_educational_article], ## 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()