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import os
import streamlit as st
import requests
import pandas as pd
from datetime import datetime
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
import plotly.express as px
import plotly.graph_objects as go
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# Configuration & Data Classes
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
@dataclass
class UserConfig:
id: int
name: str
email: str
age: int
gender: str
user_type: str
created_at: str
updated_at: str
@dataclass
class ChatMessage:
role: str
content: str
timestamp: Optional[datetime] = None
class Config:
API_URL = os.getenv("API_URL", "https://clinical-agents-333016757590.us-central1.run.app/api/v1")
PAGE_TITLE = "AI Triage System"
PAGE_ICON = "π₯"
# UI Constants
SIDEBAR_WIDTH = 300
CHAT_HEIGHT = 500
# Colors
PRIMARY_COLOR = "#1f77b4"
SUCCESS_COLOR = "#2ca02c"
WARNING_COLOR = "#ff7f0e"
ERROR_COLOR = "#d62728"
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# Session State Manager
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
class SessionStateManager:
@staticmethod
def init_state():
"""Initialize session state with default values"""
defaults = {
"user_type": "patient",
"auth_done": False,
"user_id": None,
"user_data": None,
"messages": [],
"notes": [],
"chat_active": False,
"finished": False,
"current_assessment_id": None,
"show_help": False
}
for key, val in defaults.items():
if key not in st.session_state:
st.session_state[key] = val
@staticmethod
def get_user_config() -> Optional[UserConfig]:
"""Get current user configuration"""
if st.session_state.auth_done and st.session_state.user_data:
data = st.session_state.user_data
return UserConfig(
id=data["id"],
name=data["name"],
email=data["email"],
age=data["age"],
gender=data["gender"],
user_type=data["user_type"],
created_at=data["created_at"],
updated_at=data["updated_at"]
)
return None
@staticmethod
def reset_chat():
"""Reset chat state"""
st.session_state.messages = []
st.session_state.chat_active = False
st.session_state.finished = False
st.session_state.current_assessment_id = None
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# API Service Layer
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
class APIService:
@staticmethod
def login_user(name: str, email: str, age: int, gender: str, user_type: str) -> Tuple[bool, Dict]:
"""Login user with the new API structure"""
try:
payload = {
"name": name,
"email": email,
"age": age,
"gender": gender,
"user_type": user_type
}
resp = requests.post(
f"{Config.API_URL}/users/login",
json=payload,
timeout=10
)
resp.raise_for_status()
return True, resp.json()
except Exception as e:
return False, {"error": str(e)}
@staticmethod
def get_user_by_id(user_id: int) -> Tuple[bool, Dict]:
"""Get user information by ID"""
try:
resp = requests.get(f"{Config.API_URL}/users/{user_id}", timeout=10)
resp.raise_for_status()
return True, resp.json()
except Exception as e:
return False, {"error": str(e)}
@staticmethod
def fetch_assessments() -> List[Dict]:
"""Fetch all assessments from API"""
try:
resp = requests.get(f"{Config.API_URL}/assessments", timeout=10)
resp.raise_for_status()
return resp.json()
except Exception as e:
st.error(f"Failed to fetch assessments: {str(e)}")
return []
@staticmethod
def send_chat_message(message: str, history: List[Dict], patient_id: int) -> Tuple[bool, Dict]:
"""Send chat message to triage API"""
try:
payload = {
"message": message,
"history": history,
"patient_id": patient_id
}
resp = requests.post(
f"{Config.API_URL}/triage/chat",
json=payload,
timeout=30
)
resp.raise_for_status()
return True, resp.json()
except Exception as e:
return False, {"error": str(e)}
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# UI Components
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
class UIComponents:
@staticmethod
def render_header():
"""Render main header with branding"""
st.markdown("""
<div style="text-align: center; padding: 1rem 0; background: linear-gradient(90deg, #1f77b4, #2ca02c);
border-radius: 10px; margin-bottom: 2rem;">
<h1 style="color: white; margin: 0; font-size: 2.5rem;">π₯ AI Triage System</h1>
<p style="color: #f0f0f0; margin: 0.5rem 0 0 0; font-size: 1.1rem;">
Intelligent healthcare assessment at your fingertips
</p>
</div>
""", unsafe_allow_html=True)
@staticmethod
def render_sidebar_auth() -> bool:
"""Render sidebar authentication section"""
with st.sidebar:
st.markdown("### π€ Authentication")
# User type selection
user_type = st.selectbox(
"Select your role:",
["patient", "staff"],
key="user_type_select",
help="Choose whether you're a patient seeking assessment or medical staff"
)
st.session_state.user_type = user_type
# Sign in form
with st.form("signin_form"):
st.markdown("**Sign In / Register**")
name = st.text_input("Full Name", placeholder="Enter your full name")
email = st.text_input("Email", placeholder="Enter your email address")
# Additional fields for patients
if user_type == "patient":
col1, col2 = st.columns(2)
with col1:
age = st.number_input("Age", min_value=1, max_value=120, value=25)
with col2:
gender = st.selectbox("Gender", ["male", "female", "other"])
else:
age = 30 # Default for staff
gender = "not_specified"
submitted = st.form_submit_button("π Sign In", use_container_width=True)
if submitted:
if name.strip() and email.strip():
with st.spinner("π Signing in..."):
success, response = APIService.login_user(
name.strip(),
email.strip(),
age,
gender,
user_type
)
if success:
st.session_state.user_data = response
st.session_state.user_id = response["id"]
st.session_state.auth_done = True
st.success("β
Successfully signed in!")
st.rerun()
else:
st.error(f"β Login failed: {response.get('error', 'Unknown error')}")
else:
st.error("β Please enter both name and email")
# Show current user info
if st.session_state.auth_done and st.session_state.user_data:
st.markdown("---")
st.markdown("**Current User:**")
user_data = st.session_state.user_data
st.info(f"""
π€ **{user_data['name']}**
π§ {user_data['email']}
π·οΈ {user_data['user_type'].title()}
π ID: {user_data['id']}
""")
if st.button("πͺ Sign Out", use_container_width=True):
for key in list(st.session_state.keys()):
del st.session_state[key]
st.rerun()
return st.session_state.auth_done
@staticmethod
def render_chat_interface(user_config: UserConfig):
"""Render patient chat interface"""
st.markdown("### π¬ Chat Assessment")
# Chat container
chat_container = st.container(height=Config.CHAT_HEIGHT)
with chat_container:
# Display chat history
for i, msg in enumerate(st.session_state.messages):
with st.chat_message(msg["role"]):
st.markdown(msg["content"])
# Chat controls
col1, col2, col3 = st.columns([2, 1, 1])
with col1:
if not st.session_state.chat_active:
if st.button("π Start New Assessment", use_container_width=True, type="primary"):
UIComponents._start_new_assessment(user_config)
with col2:
if st.session_state.chat_active and st.button("π Reset Chat", use_container_width=True):
SessionStateManager.reset_chat()
st.rerun()
with col3:
if st.button("β Help", use_container_width=True):
st.session_state.show_help = not st.session_state.get("show_help", False)
# Help section
if st.session_state.get("show_help", False):
st.markdown("""
---
**βΉοΈ How to use the AI Triage System:**
1. **Start Assessment**: Click 'Start New Assessment' to begin
2. **Describe Symptoms**: Be detailed about your symptoms, when they started, and their severity
3. **Answer Questions**: The AI will ask follow-up questions to better understand your condition
4. **Get Results**: Receive your triage level and recommended next steps
**Tips for better results:**
- Be honest and specific about your symptoms
- Include timeline information (when symptoms started)
- Mention any relevant medical history
- Don't hesitate to ask for clarification
""")
# Chat input
if st.session_state.chat_active and not st.session_state.finished:
if user_input := st.chat_input("π Describe your symptoms or ask a question..."):
UIComponents._handle_user_message(user_input, user_config)
@staticmethod
def _start_new_assessment(user_config: UserConfig):
"""Start a new triage assessment"""
st.session_state.chat_active = True
st.session_state.messages = []
st.session_state.finished = False
with st.spinner("π Starting your assessment..."):
success, response = APIService.send_chat_message("", [], user_config.id)
if success:
st.session_state.messages.append({
"role": "assistant",
"content": response["response"]
})
st.rerun()
else:
st.error(f"β Failed to start assessment: {response.get('error', 'Unknown error')}")
st.session_state.chat_active = False
@staticmethod
def _handle_user_message(user_input: str, user_config: UserConfig):
"""Handle user message in chat"""
# Add user message
st.session_state.messages.append({"role": "user", "content": user_input})
with st.spinner("π€ AI is analyzing your response..."):
# Convert messages to API format
history = [{"role": msg["role"], "content": msg["content"]}
for msg in st.session_state.messages]
success, response = APIService.send_chat_message(user_input, history, user_config.id)
if success:
st.session_state.messages.append({
"role": "assistant",
"content": response["response"]
})
if response.get("finished", False):
st.session_state.chat_active = False
st.session_state.finished = True
st.success("β
Assessment completed successfully!")
st.balloons()
st.rerun()
else:
st.error(f"β Error: {response.get('error', 'Unknown error')}")
class StaffDashboard:
@staticmethod
def render_dashboard(user_config: UserConfig):
"""Render staff dashboard"""
st.markdown("### π Staff Dashboard")
# Fetch data
with st.spinner("π₯ Loading assessment data..."):
assessments = APIService.fetch_assessments()
if not assessments:
st.info("π No assessments available yet.")
return
df = pd.DataFrame(assessments)
df["created_at"] = pd.to_datetime(df["created_at"])
# Dashboard metrics
StaffDashboard._render_metrics(df)
# Charts
col1, col2 = st.columns(2)
with col1:
StaffDashboard._render_esi_distribution(df)
with col2:
StaffDashboard._render_timeline_chart(df)
# Data table
StaffDashboard._render_assessments_table(df)
@staticmethod
def _render_metrics(df: pd.DataFrame):
"""Render key metrics"""
col1, col2, col3, col4 = st.columns(4)
with col1:
st.metric(
"π Total Assessments",
len(df),
delta=None
)
with col2:
avg_esi = df['esi_level'].mean()
st.metric(
"β‘ Avg ESI Level",
f"{avg_esi:.1f}",
delta=None
)
with col3:
emergency_cases = len(df[df['esi_level'] <= 2])
st.metric(
"π¨ Emergency Cases",
emergency_cases,
delta=f"{(emergency_cases/len(df)*100):.1f}% of total"
)
with col4:
latest = df['created_at'].max()
hours_ago = (datetime.now() - latest.replace(tzinfo=None)).total_seconds() / 3600
st.metric(
"π Last Assessment",
f"{hours_ago:.1f}h ago",
delta=None
)
@staticmethod
def _render_esi_distribution(df: pd.DataFrame):
"""Render ESI level distribution chart"""
st.markdown("**π― ESI Level Distribution**")
esi_counts = df['esi_level'].value_counts().sort_index()
colors = ['#d62728', '#ff7f0e', '#ffbb78', '#2ca02c', '#98df8a']
fig = px.bar(
x=esi_counts.index,
y=esi_counts.values,
color=esi_counts.index,
color_continuous_scale='RdYlGn_r',
title="Distribution by ESI Level"
)
fig.update_layout(
xaxis_title="ESI Level",
yaxis_title="Count",
showlegend=False,
height=300
)
st.plotly_chart(fig, use_container_width=True)
@staticmethod
def _render_timeline_chart(df: pd.DataFrame):
"""Render assessments timeline"""
st.markdown("**π Assessment Timeline**")
# Group by date
df_daily = df.groupby(df['created_at'].dt.date).size().reset_index()
df_daily.columns = ['date', 'count']
fig = px.line(
df_daily,
x='date',
y='count',
title="Daily Assessment Volume",
markers=True
)
fig.update_layout(
xaxis_title="Date",
yaxis_title="Number of Assessments",
height=300
)
st.plotly_chart(fig, use_container_width=True)
@staticmethod
def _render_assessments_table(df: pd.DataFrame):
"""Render assessments data table"""
st.markdown("**π Recent Assessments**")
# Prepare display dataframe
display_df = df.copy()
display_df['created_at'] = pd.to_datetime(display_df['created_at']).dt.strftime("%Y-%m-%d %H:%M:%S")
# Add user information by fetching user details
if not display_df.empty:
# Create a dictionary to store user info to avoid multiple API calls
user_info_cache = {}
user_names = []
user_emails = []
for user_id in display_df['user_id']:
if user_id not in user_info_cache:
success, user_data = APIService.get_user_by_id(user_id)
if success:
user_info_cache[user_id] = user_data
else:
user_info_cache[user_id] = {"name": "Unknown", "email": "Unknown"}
user_info = user_info_cache[user_id]
user_names.append(user_info.get("name", "Unknown"))
user_emails.append(user_info.get("email", "Unknown"))
display_df['patient_name'] = user_names
display_df['patient_email'] = user_emails
# Select columns to display
columns = ["id", "patient_name", "patient_email", "esi_level", "diagnosis", "notes", "created_at"]
# Sort by creation time (newest first)
display_df = display_df.sort_values('created_at', ascending=False)
st.dataframe(
display_df[columns],
use_container_width=True,
hide_index=True,
column_config={
"id": "Assessment ID",
"patient_name": "Patient Name",
"patient_email": "Patient Email",
"esi_level": st.column_config.NumberColumn(
"ESI Level",
help="Emergency Severity Index (1=Most urgent, 5=Least urgent)",
min_value=1,
max_value=5,
format="%d"
),
"diagnosis": "Diagnosis",
"notes": "Notes",
"created_at": "Created At"
}
)
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# Main Application
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def main():
"""Main application entry point"""
# Page configuration
st.set_page_config(
page_title=Config.PAGE_TITLE,
layout="wide",
page_icon=Config.PAGE_ICON,
initial_sidebar_state="expanded"
)
# Initialize session state
SessionStateManager.init_state()
# Custom CSS
st.markdown("""
<style>
.main > div {
padding-top: 2rem;
}
.stChatMessage {
padding: 1rem;
border-radius: 10px;
margin-bottom: 1rem;
}
.stButton > button {
border-radius: 20px;
border: none;
font-weight: 600;
}
.metric-container {
background: #f8f9fa;
padding: 1rem;
border-radius: 10px;
margin-bottom: 1rem;
}
</style>
""", unsafe_allow_html=True)
# Render header
UIComponents.render_header()
# Handle authentication
if not UIComponents.render_sidebar_auth():
st.markdown("""
<div style="text-align: center; padding: 3rem; color: #666;">
<h3>π Welcome to AI Triage System</h3>
<p>Please sign in using the sidebar to get started with your health assessment.</p>
</div>
""", unsafe_allow_html=True)
st.stop()
# Get user configuration
user_config = SessionStateManager.get_user_config()
# Route to appropriate interface
if user_config.user_type == "patient":
UIComponents.render_chat_interface(user_config)
else:
StaffDashboard.render_dashboard(user_config)
if __name__ == "__main__":
main() |