from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool import datetime import requests import pytz import yaml from tools.final_answer import FinalAnswerTool from huggingface_hub import login import os from pytrends.request import TrendReq import pandas as pd from Gradio_UI import GradioUI from typing import Optional, Dict, Any def get_rxcui(drug_name: str) -> str: """Fetches the RxCUI (RxNorm Concept Unique Identifier) for a given drug name.""" url = f"https://rxnav.nlm.nih.gov/REST/rxcui.json?name={drug_name}&search=1" response = requests.get(url) if response.status_code == 200: data = response.json() if "idGroup" in data and "rxnormId" in data["idGroup"]: return data["idGroup"]["rxnormId"][0] # Return first RxCUI return None # Return None if RxCUI is not found def get_drug_interactions(rxcui: str) -> list: """Fetches all known drug interactions for a given RxCUI.""" url = f"https://rxnav.nlm.nih.gov/REST/interaction/interaction.json?rxcui={rxcui}" response = requests.get(url) if response.status_code == 200: data = response.json() interactions = [] if "interactionTypeGroup" in data: for group in data["interactionTypeGroup"]: for interaction in group.get("interactionType", []): for pair in interaction.get("interactionPair", []): interacting_rxcuis = [concept["minConceptItem"]["rxcui"] for concept in pair.get("interactionConcept", [])] interactions.append((interacting_rxcuis, pair.get("description", "No description available"))) return interactions return None # Return None if no interaction data is found @tool def drug_interaction_checker(drug_1: str, drug_2: str) -> str: """Checks for potential interactions between two medications using the NLM Interaction API. Args: drug_1: The name of the first drug. drug_2: The name of the second drug. """ rxcui_1 = get_rxcui(drug_1) rxcui_2 = get_rxcui(drug_2) if not rxcui_1 or not rxcui_2: return "❌ Could not find RxCUI for one or both drugs." # Get interactions for both drugs interactions_1 = get_drug_interactions(rxcui_1) interactions_2 = get_drug_interactions(rxcui_2) # Check if drug_2's RxCUI appears in drug_1's interaction list if interactions_1: for interacting_rxcuis, description in interactions_1: if rxcui_2 in interacting_rxcuis: return f"⚠️ Drug Interaction Warning: {description}" # Check if drug_1's RxCUI appears in drug_2's interaction list (redundancy check) if interactions_2: for interacting_rxcuis, description in interactions_2: if rxcui_1 in interacting_rxcuis: return f"⚠️ Drug Interaction Warning: {description}" return "✅ No significant interaction found for these drugs." @tool def market_trend_analyzer(product_keyword: str) -> str: """Analyzes market trends for a given product keyword using Google Trends and Statista. Args: product_keyword: The product or keyword to analyze. """ try: # Google Trends analysis pytrends = TrendReq(hl='en-US', tz=360) pytrends.build_payload([product_keyword], timeframe='today 12-m') interest_over_time = pytrends.interest_over_time() # Calculate trend metrics current_interest = interest_over_time[product_keyword].iloc[-1] avg_interest = interest_over_time[product_keyword].mean() trend_direction = "Increasing" if current_interest > avg_interest else "Decreasing" # Statista data (you'd need to replace this with actual API calls if available) statista_url = f"https://www.statista.com/search/?q={product_keyword}&Search=&qKat=search" statista_response = requests.get(statista_url) statista_data = f"Statista search results: {statista_url}" # Compile report report = f"Market Trend Analysis for '{product_keyword}':\n\n" report += f"Google Trends:\n" report += f"- Current Interest: {current_interest:.2f}\n" report += f"- Average Interest (12 months): {avg_interest:.2f}\n" report += f"- Trend Direction: {trend_direction}\n\n" report += f"Statista Data:\n{statista_data}\n" return report except Exception as e: return f"❌ Error analyzing market trends: {str(e)}" @tool def sig_expander(sig: str) -> str: """Expands a prescription SIG shorthand into human-readable patient instructions. Args: sig: A prescription instruction in shorthand (e.g., '1 tab po qd'). """ sig_map = { "1 tab po qd": "Take 1 tablet by mouth once daily.", "1 tab po bid": "Take 1 tablet by mouth twice daily.", "1 tab po tid": "Take 1 tablet by mouth three times daily.", "1 tab po qid": "Take 1 tablet by mouth four times daily.", "1 tab po prn": "Take 1 tablet by mouth as needed.", "1 tab po q12h": "Take 1 tablet by mouth every 12 hours.", "1 tab po q8h": "Take 1 tablet by mouth every 8 hours.", "2 tab po qd": "Take 2 tablets by mouth once daily." } return sig_map.get(sig.lower(), "❌ No direct match found. Please clarify.") @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 healthcare_software_trend_analyzer(product_keyword: str, timeframe: str = 'today 5-y', geo: str = '') -> str: """Analyzes market trends for healthcare software products using Google Trends. Args: product_keyword: The healthcare software product or category to analyze. timeframe: The time range for analysis (default: 'today 5-y' for 5 years). geo: Geographic region for analysis (default: '' for worldwide). """ HEALTHCARE_SOFTWARE_CATEGORIES = [ "Electronic Health Records", "Telemedicine", "Healthcare Analytics", "Medical Billing Software", "Practice Management Software", "Remote Patient Monitoring", "Healthcare CRM", "Medical Imaging Software","Sig", "Patient Instructions" ] try: # Suggest category if not in list if product_keyword not in HEALTHCARE_SOFTWARE_CATEGORIES: suggestion = f"\nNote: '{product_keyword}' is not in our list of common healthcare software categories. Consider using one of these: {', '.join(HEALTHCARE_SOFTWARE_CATEGORIES)}\n" else: suggestion = "" # Google Trends analysis pytrends = TrendReq(hl='en-US', tz=360) pytrends.build_payload([product_keyword], timeframe=timeframe, geo=geo) interest_over_time = pytrends.interest_over_time() # Calculate trend metrics current_interest = interest_over_time[product_keyword].iloc[-1] avg_interest = interest_over_time[product_keyword].mean() trend_direction = "Increasing" if current_interest > avg_interest else "Decreasing" # Compile report report = f"Healthcare Software Market Trend Analysis for '{product_keyword}':\n" report += f"Timeframe: {timeframe}\n" report += f"Region: {geo if geo else 'Worldwide'}\n\n" report += f"Google Trends Analysis:\n" report += f"- Current Interest: {current_interest:.2f}\n" report += f"- Average Interest: {avg_interest:.2f}\n" report += f"- Trend Direction: {trend_direction}\n" report += suggestion return report except Exception as e: return f"❌ Error analyzing healthcare software market trends: {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'). """ 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)}" login(os.environ["HF_TOKEN"]) final_answer = FinalAnswerTool() model = HfApiModel( max_tokens=2096, temperature=0.5, model_id='Qwen/Qwen2.5-Coder-32B-Instruct', 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, healthcare_software_trend_analyzer], ## 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()