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

# Medical tool: Fetch research papers from PubMed
@tool
def get_pubmed_articles(query: str) -> str:
    """Fetches the latest research papers from PubMed based on a query.
    Args:
        query: The medical or biological topic to search for.
    """
    url = f"https://api.ncbi.nlm.nih.gov/lit/ctxp/v1/pubmed/?format=summary&term={query}"
    response = requests.get(url)
    if response.status_code == 200:
        return response.json()  # Return article summary
    else:
        return "Failed to fetch articles."

# Medical tool: Explain medical terminology
@tool
def explain_medical_term(term: str) -> str:
    """Provides a simple explanation of a medical term.
    Args:
        term: The medical term to explain.
    """
    explanations = {
        "hypertension": "Hypertension, or high blood pressure, is a condition where the force of blood against the artery walls is too high.",
        "diabetes": "Diabetes is a chronic disease where the body either doesn't produce enough insulin or can't use it effectively.",
        "anemia": "Anemia is a condition in which the blood lacks enough healthy red blood cells to carry oxygen.",
    }
    return explanations.get(term.lower(), "No definition found. Try a different term.")

# Tool for fetching current time in a timezone
@tool
def get_current_time_in_timezone(timezone: str) -> str:
    """Fetches the current local time in a specified timezone.
    Args:
        timezone: A string representing a valid timezone (e.g., 'America/New_York').
    """
    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)}"

# Load the medical AI model (BioBERT for medical text processing)
model = HfApiModel(
    max_tokens=2096,
    temperature=0.5,
    model_id='dmis-lab/biobert-v1.1',  # BioBERT for medical NLP tasks
    custom_role_conversions=None,
)

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

# Define the AI agent with medical expertise
agent = CodeAgent(
    model=model,
    tools=[FinalAnswerTool(), get_pubmed_articles, explain_medical_term, get_current_time_in_timezone],
    max_steps=6,
    verbosity_level=1,
    grammar=None,
    planning_interval=None,
    name="Medical Bio Expert",
    description="An AI agent specialized in medical biology, capable of answering biomedical questions, retrieving PubMed articles, and explaining medical terms.",
    prompt_templates=prompt_templates
)

# Launch the Gradio UI for interaction
GradioUI(agent, title="Medical Biology AI Expert", description="Ask me anything about medical biology!").launch()