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 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_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)


@tool
def summarize_text(content: str) -> str:
    """
    Summarizes a given text.

    Args:
        content (str): The text content that needs summarization.

    Returns:
        str: A summarized version of the input text.
    """
    try:
        from transformers import pipeline
        summarizer = pipeline("summarization")
        summary = summarizer(content, max_length=100, min_length=30, do_sample=False)
        return summary[0]['summary_text']
    except Exception as e:
        return f"Error processing summary: {str(e)}"


@tool
def analyze_sentiment(text: str) -> str:
    """
    Analyzes the sentiment of a given text.

    Args:
        text (str): The input text for sentiment analysis.

    Returns:
        str: A sentiment classification ('Positive', 'Negative', or 'Neutral').
    """
    from textblob import TextBlob
    sentiment_score = TextBlob(text).sentiment.polarity
    if sentiment_score > 0:
        return "Positive sentiment detected!"
    elif sentiment_score < 0:
        return "Negative sentiment detected!"
    else:
        return "Neutral sentiment detected."


# Ensure image generation is included
tools_list = [final_answer, image_generation_tool, summarize_text, analyze_sentiment, get_current_time_in_timezone]

    
agent = CodeAgent(
    model=model,
    tools=[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()