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import streamlit as st
from langchain_groq import ChatGroq
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.tools import tool
from typing import TypedDict, Annotated, List, Optional
import json
# Configuration
class MedicalConfig:
SYSTEM_PROMPT = """You are an AI clinical assistant. Follow these rules:
1. Analyze patient data using latest medical guidelines
2. Always check for drug interactions
3. Use structured actions when needed:
- lab_order: Order lab tests
- prescribe: Prescribe medication
4. Flag high-risk conditions immediately"""
RED_FLAGS = {
"symptoms": ["chest pain", "shortness of breath", "severe headache"],
"vitals": {"temp": (38.5, "°C"), "hr": (120, "bpm"), "bp": ("180/120", "mmHg")}
}
# Define tools with proper schemas
@tool
def order_lab_test(test_name: str, reason: str) -> str:
"""Orders a lab test with specified parameters.
Args:
test_name: Name of the lab test to order
reason: Clinical justification for the test
Returns:
Confirmation message with test details
"""
return f"Lab ordered: {test_name} ({reason})"
@tool
def prescribe_medication(name: str, dosage: str, frequency: str) -> str:
"""Prescribes medication with specific dosage instructions.
Args:
name: Name of the medication
dosage: Dosage amount (e.g., '500mg')
frequency: Administration frequency (e.g., 'every 6 hours')
Returns:
Confirmation message with prescription details
"""
return f"Prescribed: {name} {dosage} {frequency}"
# Initialize tools
tools = [order_lab_test, prescribe_medication, TavilySearchResults(max_results=3)]
class MedicalAgent:
def __init__(self):
self.model = ChatGroq(temperature=0.1, model="Llama-3.3-70b-Specdec")
self.model_with_tools = self.model.bind_tools(tools)
def analyze_patient(self, patient_data: dict) -> Optional[dict]:
try:
response = self.model_with_tools.invoke([
SystemMessage(content=MedicalConfig.SYSTEM_PROMPT),
HumanMessage(content=f"Patient Data: {json.dumps(patient_data)}")
])
return response
except Exception as e:
st.error(f"Error analyzing patient data: {str(e)}")
return None
def process_action(self, action: dict) -> str:
try:
tool_name = action['name']
args = action['args']
if tool_name == "order_lab_test":
return order_lab_test.invoke(args)
elif tool_name == "prescribe_medication":
return prescribe_medication.invoke(args)
else:
return f"Unknown action: {tool_name}"
except Exception as e:
return f"Error processing action: {str(e)}"
# Rest of the Streamlit UI code remains the same... |