app.py

# app.py
import streamlit as st
from uuid import uuid4
import sqlite3
import plotly.express as px
from langchain_groq import ChatGroq
from langgraph.checkpoint.sqlite import SqliteSaver
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage

# Initialize components
def init_components():
    model = ChatGroq(temperature=0.1, model="Llama-3.3-70b-Specdec")
    tool = TavilySearchResults(max_results=3)
    conn = sqlite3.connect("medical_checkpoints.db", check_same_thread=False)
    memory = SqliteSaver(conn)
    return model, tool, memory

# Medical Agent Class
class AdvancedMedicalAgent:
    def __init__(self, model, tools, memory):
        self.model = model.bind_tools(tools)
        self.tools = {t.name: t for t in tools}
        self.memory = memory
        
    def process_query(self, patient_data, thread_id):
        # Initialize or retrieve conversation thread
        if 'history' not in st.session_state:
            st.session_state.history = []
            
        # Add patient data visualization
        self.display_patient_dashboard(patient_data)
        
        # Process medical inquiry
        response = self.model.invoke([
            SystemMessage(content=self.get_system_prompt()),
            HumanMessage(content=f"Patient Case:\n{patient_data}")
        ])
        
        # Handle tool calls
        if hasattr(response, 'tool_calls'):
            self.handle_medical_actions(response.tool_calls, thread_id)
            
        st.session_state.history.append(("AI", response.content))
        
    def display_patient_dashboard(self, data):
        tab1, tab2, tab3 = st.tabs(["Vitals", "History", "Timeline"])
        
        with tab1:
            fig = px.line(
                x=["Temperature", "BP", "Heart Rate"],
                y=[data['vitals']['temp'], 130, 85],  # Example data
                title="Vital Signs"
            )
            st.plotly_chart(fig)
            
        with tab2:
            st.json(data['history'])
            
        with tab3:
            st.vega_lite_chart({
                "mark": {"type": "circle", "tooltip": True},
                "encoding": {
                    "x": {"field": "timestamp", "type": "temporal"},
                    "y": {"field": "severity", "type": "quantitative"}
                }
            })

    def handle_medical_actions(self, tool_calls, thread_id):
        for call in tool_calls:
            st.warning(f"🩺 Pending Medical Action: {call['name']}")
            
            if st.button("Review Action Details"):
                st.write(f"**Action Type:** {call['name']}")
                st.write(f"**Parameters:** {call['args']}")
                
                if st.checkbox("I approve this action"):
                    result = self.tools[call['name']].invoke(call['args'])
                    st.session_state.history.append(
                        ("System", f"Action executed: {result}")
                    )
                else:
                    st.session_state.history.append(
                        ("System", "Action cancelled by clinician")
                    )

    def get_system_prompt(self):
        return """You are an advanced medical AI assistant. Follow these steps:
1. Analyze patient data using latest clinical guidelines
2. Consider drug interactions and contraindications
3. Propose differential diagnosis (3 possibilities)
4. Suggest evidence-based treatment options
5. Identify necessary lab tests with reasoning
6. Flag high-risk factors in RED
7. Maintain audit trail of all decisions"""

# Streamlit UI
def main():
    st.set_page_config(page_title="AI Clinical Assistant", layout="wide")
    
    with st.sidebar:
        st.header("Patient Intake")
        patient_data = {
            "symptoms": st.multiselect("Symptoms", ["Fever", "Cough", "Chest Pain"]),
            "history": {
                "conditions": st.text_input("Medical History"),
                "medications": st.text_input("Current Medications")
            },
            "vitals": {
                "temp": st.number_input("Temperature (°C)", 36.0, 42.0, 37.0),
                "bp": st.text_input("Blood Pressure (mmHg)", "120/80")
            }
        }
    
    st.title("🧠 AI-Powered Clinical Decision Support")
    
    model, tool, memory = init_components()
    agent = AdvancedMedicalAgent(model, [tool], memory)
    
    if st.button("Start Analysis"):
        with st.spinner("Analyzing patient case..."):
            agent.process_query(patient_data, str(uuid4()))
            
    if 'history' in st.session_state:
        st.subheader("Clinical Decision Log")
        for role, content in st.session_state.history:
            with st.chat_message(role):
                st.markdown(content)

if __name__ == "__main__":
    main()