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

# Custom Tool to fetch datasets related to body parts or imaging types

@tool
def my_custom_tool(arg1: str, arg2: int) -> str:
    """
    Search and retrieve publicly available medical datasets from Hugging Face based on any medical-related keyword.

    Args:
        arg1: A keyword related to medical data (e.g., 'cancer', 'diabetes', 'CT scan', 'radiology', 'dermoscopy').
        arg2: The maximum number of datasets to retrieve.

    Returns:
        A list of dataset names matching the search query, or a message stating that no datasets were found
        or that the keyword is not medically relevant.
    """
    try:
        keyword = arg1.strip().lower()
        limit = int(arg2)

        # ✅ Define a basic list of medically relevant terms
        medical_terms = [
            "skin", "brain", "cancer", "tumor", "mri", "ct", "xray", "ultrasound",
            "diabetes", "pneumonia", "covid", "lesion", "radiology", "pathology",
            "lung", "chest", "abdomen", "spine", "bone", "stroke", "eczema", "melanoma",
            "eye", "retina", "dermoscopy", "cardiology", "infection", "biopsy", "tooth",
            "toothache", "dental", "ear", "wrist", "hand", "leg", "arm", "heart"
        ]

        # ✅ Check if the keyword looks medically relevant
        if not any(term in keyword for term in medical_terms):
            return f"'{arg1}' does not appear to be a medical term."

        # ✅ Proceed to fetch datasets
        response = requests.get(
            f"https://huggingface.co/api/datasets?search={keyword}&limit={limit}"
        )
        response.raise_for_status()
        datasets = response.json()

        if not datasets:
            return f"No medical datasets found for '{arg1}'."

        results = [f"- {ds.get('id', 'Unknown')}" for ds in datasets[:limit]]
        return f"Medical datasets related to '{arg1}':\n" + "\n".join(results)

    except Exception as e:
        return f"Error searching medical datasets for '{arg1}': {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').

    Returns:
        A string showing the current local time in the specified timezone.
    """
    try:
        tz = pytz.timezone(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 setup
model = HfApiModel(
    max_tokens=2096,
    temperature=0.5,
    model_id='Qwen/Qwen2.5-Coder-32B-Instruct',  # this model may be overloaded
    custom_role_conversions=None,
)

# Load tool from hub
image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)

# Load prompt templates
with open("prompts.yaml", 'r') as stream:
    prompt_templates = yaml.safe_load(stream)

# Create the agent
agent = CodeAgent(
    model=model,
    tools=[final_answer, get_current_time_in_timezone, my_custom_tool],  # add your tools here
    max_steps=6,
    verbosity_level=1,
    grammar=None,
    planning_interval=None,
    name=None,
    description=None,
    prompt_templates=prompt_templates
)

# Launch the UI
GradioUI(agent).launch()