Add application file

app.py

@@ -0,0 +1,131 @@
+# 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()