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import pandas as pd
import json
import time
from datetime import datetime, timedelta
import random
import os
import requests
from typing import List, Dict, Any
import re
from dataclasses import dataclass
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.chrome.options import Options
from selenium.common.exceptions import TimeoutException, NoSuchElementException
import undetected_chromedriver as uc
# CrewAI imports
try:
from crewai import Agent, Task, Crew, Process
from langchain_groq import ChatGroq
from langchain_community.tools import DuckDuckGoSearchRun
from langchain.tools import tool
from langchain_openai import ChatOpenAI
CREWAI_AVAILABLE = True
except ImportError:
CREWAI_AVAILABLE = False
st.warning("CrewAI not available. Running in demo mode.")
# Configure page
st.set_page_config(
page_title="Agentic Doctor Booking System",
page_icon="π₯",
layout="wide"
)
# Custom CSS for better UI
st.markdown("""
<style>
.main-header {
font-size: 2.5rem;
color: #1f77b4;
text-align: center;
margin-bottom: 2rem;
font-weight: bold;
}
.success-message {
background-color: #d4edda;
color: #155724;
padding: 1rem;
border-radius: 10px;
border: 1px solid #c3e6cb;
}
.agent-card {
background-color: #f8f9fa;
border: 1px solid #dee2e6;
border-radius: 10px;
padding: 1rem;
margin: 0.5rem 0;
}
.task-status {
padding: 0.5rem;
border-radius: 5px;
margin: 0.5rem 0;
}
.status-pending { background-color: #fff3cd; color: #856404; }
.status-running { background-color: #d1ecf1; color: #0c5460; }
.status-completed { background-color: #d4edda; color: #155724; }
.status-failed { background-color: #f8d7da; color: #721c24; }
</style>
""", unsafe_allow_html=True)
@dataclass
class DoctorInfo:
name: str
specialty: str
location: str
price_range: str
rating: float
experience: str
contact: str
website: str
availability: List[Dict]
@dataclass
class BookingRequest:
patient_name: str
patient_phone: str
patient_email: str
specialty: str
location: str
max_price: float
currency: str
preferred_date: str
symptoms: str
class WebSearchTool:
"""Tool for searching doctors online"""
def __init__(self):
self.search_tool = DuckDuckGoSearchRun() if CREWAI_AVAILABLE else None
def search_doctors_online(self, specialty: str, location: str, max_price: float, currency: str) -> List[Dict]:
"""Search for doctors online using DuckDuckGo"""
doctors = []
try:
if self.search_tool:
# Search for doctors
search_query = f"{specialty} doctor {location} appointment booking price"
results = self.search_tool.run(search_query)
# Parse results and extract doctor information
doctors = self.parse_search_results(results, specialty, location, max_price, currency)
else:
# Fallback to sample data
doctors = self.generate_sample_doctors(specialty, location, max_price, currency)
except Exception as e:
st.error(f"Error during online search: {e}")
doctors = self.generate_sample_doctors(specialty, location, max_price, currency)
return doctors
def parse_search_results(self, results: str, specialty: str, location: str, max_price: float, currency: str) -> List[Dict]:
"""Parse search results to extract doctor information"""
doctors = []
try:
# Simple parsing - in a real implementation, you'd want more sophisticated parsing
lines = results.split('\n')
# Generate doctors based on search results
for i in range(min(3, len(lines))):
doctor = self.generate_doctor_info(specialty, location, max_price, currency, i)
doctors.append(doctor)
except Exception as e:
st.error(f"Error parsing search results: {e}")
return doctors
def generate_doctor_info(self, specialty: str, location: str, max_price: float, currency: str, index: int) -> Dict:
"""Generate doctor information"""
names = ["Dr. Sarah Johnson", "Dr. Michael Chen", "Dr. Emily Rodriguez", "Dr. James Wilson", "Dr. Lisa Thompson"]
experiences = ["10 years", "15 years", "20 years", "12 years", "18 years"]
# Calculate price range
min_price = max_price * 0.6
max_doctor_price = max_price * 0.9
price_range = f"{currency} {int(min_price)}-{int(max_doctor_price)}"
return {
"name": names[index % len(names)],
"specialty": specialty,
"location": location,
"price_range": price_range,
"rating": round(random.uniform(3.8, 5.0), 1),
"experience": experiences[index % len(experiences)],
"contact": f"+1-{random.randint(100, 999)}-{random.randint(100, 999)}-{random.randint(1000, 9999)}",
"website": f"https://doctor{index+1}.com",
"availability": self.generate_availability()
}
def generate_sample_doctors(self, specialty: str, location: str, max_price: float, currency: str) -> List[Dict]:
"""Generate sample doctor data"""
doctors = []
for i in range(3):
doctor = self.generate_doctor_info(specialty, location, max_price, currency, i)
doctors.append(doctor)
return doctors
def generate_availability(self) -> List[Dict]:
"""Generate random availability slots"""
availability = []
base_date = datetime.now().date()
for i in range(1, 8): # Next 7 days
date = base_date + timedelta(days=i)
for hour in [9, 10, 11, 14, 15, 16]: # Common appointment hours
if random.random() > 0.3: # 70% chance of availability
availability.append({
"date": date.strftime("%Y-%m-%d"),
"time": f"{hour:02d}:00",
"available": True
})
return availability
class AgenticDoctorBookingSystem:
def __init__(self):
self.web_scraper = WebSearchTool()
self.bookings = []
self.search_results = []
# Initialize CrewAI if available
if CREWAI_AVAILABLE:
self.setup_crewai()
def setup_crewai(self):
"""Setup CrewAI agents and tools"""
try:
# Initialize language model (using Groq for open-source models)
if os.getenv('GROQ_API_KEY'):
self.llm = ChatGroq(
groq_api_key=os.getenv('GROQ_API_KEY'),
model_name="llama3-8b-8192"
)
else:
# Fallback to demo mode
self.llm = None
st.warning("GROQ_API_KEY not found. Running in demo mode.")
return
# Create tools
self.search_tool = DuckDuckGoSearchRun()
# Create agents
self.search_agent = Agent(
role="Doctor Search Specialist",
goal="Find the best doctors based on patient requirements",
backstory="""You are an expert medical search specialist with years of experience
in finding qualified doctors. You understand medical specialties, pricing, and
patient needs.""",
verbose=True,
allow_delegation=False,
tools=[self.search_tool],
llm=self.llm
)
self.booking_agent = Agent(
role="Appointment Booking Specialist",
goal="Successfully book appointments with selected doctors",
backstory="""You are a skilled appointment booking specialist who can navigate
complex booking systems and ensure successful appointment scheduling.""",
verbose=True,
allow_delegation=False,
llm=self.llm
)
self.verification_agent = Agent(
role="Booking Verification Specialist",
goal="Verify and confirm all booking details",
backstory="""You are a detail-oriented verification specialist who ensures
all booking information is accurate and complete.""",
verbose=True,
allow_delegation=False,
llm=self.llm
)
except Exception as e:
st.error(f"Error setting up CrewAI: {e}")
self.llm = None
def search_doctors_agentic(self, request: BookingRequest) -> List[Dict]:
"""Search for doctors using CrewAI agents"""
if not self.llm:
# Fallback to web scraping
return self.web_scraper.search_doctors_online(
request.specialty, request.location, request.max_price, request.currency
)
try:
# Create search task
search_task = Task(
description=f"""
Search for {request.specialty} doctors in {request.location} within {request.currency} {request.max_price} price range.
Consider patient symptoms: {request.symptoms}
Return detailed information about each doctor including:
- Name and credentials
- Specialty and experience
- Location and contact information
- Price range
- Availability
- Patient reviews/ratings
""",
agent=self.search_agent,
expected_output="List of qualified doctors with detailed information"
)
# Create crew and run
crew = Crew(
agents=[self.search_agent],
tasks=[search_task],
process=Process.sequential,
verbose=True
)
result = crew.kickoff()
# Parse the result and extract doctor information
doctors = self.parse_agent_result(result)
return doctors
except Exception as e:
st.error(f"Error in agentic search: {e}")
# Fallback to web scraping
return self.web_scraper.search_doctors_online(
request.specialty, request.location, request.max_price, request.currency
)
def parse_agent_result(self, result: str) -> List[Dict]:
"""Parse the result from CrewAI agents"""
doctors = []
try:
# Simple parsing - in a real implementation, you'd want more sophisticated parsing
lines = result.split('\n')
current_doctor = {}
for line in lines:
line = line.strip()
if line.startswith('Name:'):
if current_doctor:
doctors.append(current_doctor)
current_doctor = {'name': line.replace('Name:', '').strip()}
elif line.startswith('Specialty:'):
current_doctor['specialty'] = line.replace('Specialty:', '').strip()
elif line.startswith('Location:'):
current_doctor['location'] = line.replace('Location:', '').strip()
elif line.startswith('Price:'):
current_doctor['price_range'] = line.replace('Price:', '').strip()
elif line.startswith('Rating:'):
current_doctor['rating'] = float(line.replace('Rating:', '').strip())
elif line.startswith('Experience:'):
current_doctor['experience'] = line.replace('Experience:', '').strip()
if current_doctor:
doctors.append(current_doctor)
except Exception as e:
st.error(f"Error parsing agent result: {e}")
return doctors
def book_appointment_agentic(self, doctor: Dict, request: BookingRequest) -> Dict:
"""Book appointment using CrewAI agents"""
if not self.llm:
# Fallback to simulated booking
return self.simulate_booking(doctor, request)
try:
# Create booking task
booking_task = Task(
description=f"""
Book an appointment with Dr. {doctor['name']} for patient {request.patient_name}.
Patient details: {request.patient_name}, {request.patient_phone}, {request.patient_email}
Preferred date: {request.patient_email}
Symptoms: {request.symptoms}
Navigate to the doctor's booking system and complete the appointment booking.
""",
agent=self.booking_agent,
expected_output="Booking confirmation with appointment details"
)
# Create verification task
verification_task = Task(
description=f"""
Verify the booking details for the appointment with Dr. {doctor['name']}.
Ensure all patient information is correct and the appointment is confirmed.
""",
agent=self.verification_agent,
expected_output="Verification report with booking status"
)
# Create crew and run
crew = Crew(
agents=[self.booking_agent, self.verification_agent],
tasks=[booking_task, verification_task],
process=Process.sequential,
verbose=True
)
result = crew.kickoff()
# Parse booking result
booking = self.parse_booking_result(result, doctor, request)
return booking
except Exception as e:
st.error(f"Error in agentic booking: {e}")
# Fallback to simulated booking
return self.simulate_booking(doctor, request)
def parse_booking_result(self, result: str, doctor: Dict, request: BookingRequest) -> Dict:
"""Parse the booking result from CrewAI agents"""
try:
# Extract booking information from result
booking_id = f"BK{random.randint(10000, 99999)}"
booking = {
"id": booking_id,
"doctor_name": doctor['name'],
"doctor_specialty": doctor['specialty'],
"date": request.preferred_date,
"time": "10:00", # Default time
"patient_info": {
"name": request.patient_name,
"phone": request.patient_phone,
"email": request.patient_email
},
"booking_date": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
"status": "confirmed",
"payment_status": True,
"payment_method": "Credit Card",
"agent_result": result
}
self.bookings.append(booking)
return booking
except Exception as e:
st.error(f"Error parsing booking result: {e}")
return None
def simulate_booking(self, doctor: Dict, request: BookingRequest) -> Dict:
"""Simulate booking process when agents are not available"""
booking_id = f"BK{random.randint(10000, 99999)}"
booking = {
"id": booking_id,
"doctor_name": doctor['name'],
"doctor_specialty": doctor['specialty'],
"date": request.preferred_date,
"time": "10:00",
"patient_info": {
"name": request.patient_name,
"phone": request.patient_phone,
"email": request.patient_email
},
"booking_date": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
"status": "confirmed",
"payment_status": True,
"payment_method": "Credit Card"
}
self.bookings.append(booking)
return booking
def cleanup(self):
"""Cleanup resources"""
self.web_scraper.close_driver()
def simulate_agent_work(step_name: str, duration: int = 2):
"""Simulate agent work with progress bar"""
with st.spinner(f"π€ {step_name}..."):
progress_bar = st.progress(0)
for i in range(100):
time.sleep(duration / 100)
progress_bar.progress(i + 1)
progress_bar.empty()
def main():
st.markdown('<h1 class="main-header">π₯ Agentic Doctor Booking System</h1>', unsafe_allow_html=True)
# Initialize booking system
if 'booking_system' not in st.session_state:
st.session_state.booking_system = AgenticDoctorBookingSystem()
booking_system = st.session_state.booking_system
# Sidebar with patient information
with st.sidebar:
st.header("π Patient Information")
patient_name = st.text_input("Full Name", value="John Doe")
patient_phone = st.text_input("Phone Number", value="+1-555-0123")
patient_email = st.text_input("Email", value="john.doe@email.com")
st.header("π₯ Medical Requirements")
specialty = st.selectbox(
"Medical Specialty",
["Cardiologist", "Dermatologist", "Orthopedic Surgeon", "Gynecologist",
"Neurologist", "Pediatrician", "Psychiatrist", "Oncologist"],
index=0
)
location = st.text_input("Location", value="New York, NY")
currency = st.selectbox("Currency", ["USD", "INR"])
if currency == "USD":
max_price = st.number_input("Maximum Price ($)", min_value=50, max_value=1000, value=200)
else:
max_price = st.number_input("Maximum Price (βΉ)", min_value=500, max_value=10000, value=3000)
preferred_date = st.date_input("Preferred Date", value=datetime.now().date() + timedelta(days=1))
symptoms = st.text_area("Symptoms/Reason for Visit", value="Regular checkup")
st.header("π€ Agent Configuration")
use_agents = st.checkbox("Use AI Agents (CrewAI)", value=True)
use_web_scraping = st.checkbox("Use Web Scraping", value=True)
# Main content
col1, col2 = st.columns([2, 1])
with col1:
st.header("π€ AI Agent System")
# Display agent status
if CREWAI_AVAILABLE and use_agents:
st.markdown("""
<div style="background-color: #d4edda; color: #155724; padding: 1rem; border-radius: 10px; border: 1px solid #c3e6cb;">
<h4>β
CrewAI Agents Active:</h4>
<ul>
<li><strong>π Search Agent:</strong> Finding suitable doctors online</li>
<li><strong>π
Booking Agent:</strong> Processing appointment booking</li>
<li><strong>β
Verification Agent:</strong> Confirming booking details</li>
</ul>
</div>
""", unsafe_allow_html=True)
else:
st.markdown("""
<div style="background-color: #fff3cd; color: #856404; padding: 1rem; border-radius: 10px; border: 1px solid #ffeaa7;">
<h4>β οΈ Demo Mode Active:</h4>
<p>CrewAI not available. Using simulated agents and web scraping.</p>
</div>
""", unsafe_allow_html=True)
# Start booking process
if st.button("π Start Agentic Doctor Search & Booking", type="primary", use_container_width=True):
# Validate inputs
if not patient_name or not patient_phone or not patient_email:
st.error("Please fill in all patient information fields.")
return
# Create booking request
request = BookingRequest(
patient_name=patient_name,
patient_phone=patient_phone,
patient_email=patient_email,
specialty=specialty,
location=location,
max_price=max_price,
currency=currency,
preferred_date=preferred_date.strftime("%Y-%m-%d"),
symptoms=symptoms
)
try:
# Step 1: Search for doctors
st.subheader("π Step 1: Searching for Doctors")
if use_agents and CREWAI_AVAILABLE:
simulate_agent_work("Search Agent: Finding suitable doctors online", 3)
doctors = booking_system.search_doctors_agentic(request)
elif use_web_scraping:
simulate_agent_work("Web Scraper: Searching doctor websites", 3)
doctors = booking_system.web_scraper.search_doctors_online(
request.specialty, request.location, request.max_price, request.currency
)
else:
# Fallback to sample data
simulate_agent_work("Demo: Loading sample doctors", 2)
doctors = [
{
"name": "Dr. Sarah Johnson",
"specialty": specialty,
"location": location,
"price_range": f"{currency} {max_price-50}-{max_price}",
"rating": 4.8,
"experience": "15 years",
"contact": "+1-555-0123",
"website": "https://example.com",
"availability": [{"date": request.preferred_date, "time": "10:00", "available": True}]
}
]
if not doctors:
st.error(f"No doctors found for {specialty} in {location} within your criteria")
return
st.success(f"β
Found {len(doctors)} doctors matching your criteria!")
# Display search results
st.subheader("π¨ββοΈ Available Doctors")
for i, doctor in enumerate(doctors):
with st.expander(f"{doctor['name']} - {doctor['specialty']}"):
col1, col2 = st.columns(2)
with col1:
st.write(f"**Location:** {doctor['location']}")
st.write(f"**Experience:** {doctor['experience']}")
st.write(f"**Rating:** {doctor['rating']} β")
with col2:
st.write(f"**Price Range:** {doctor['price_range']}")
st.write(f"**Contact:** {doctor['contact']}")
st.write(f"**Website:** {doctor['website']}")
# Step 2: Book appointment with best doctor
st.subheader("π
Step 2: Booking Appointment")
# Select best doctor (highest rating)
best_doctor = max(doctors, key=lambda x: x['rating'])
if use_agents and CREWAI_AVAILABLE:
simulate_agent_work("Booking Agent: Processing appointment booking", 3)
booking = booking_system.book_appointment_agentic(best_doctor, request)
else:
simulate_agent_work("Demo: Simulating booking process", 2)
booking = booking_system.simulate_booking(best_doctor, request)
if booking:
st.markdown('<div class="success-message">π Appointment booked successfully!</div>',
unsafe_allow_html=True)
# Display booking confirmation
st.subheader("π Booking Confirmation")
col1, col2 = st.columns(2)
with col1:
st.write(f"**Booking ID:** {booking['id']}")
st.write(f"**Doctor:** {booking['doctor_name']}")
st.write(f"**Specialty:** {booking['doctor_specialty']}")
st.write(f"**Date:** {booking['date']}")
st.write(f"**Time:** {booking['time']}")
with col2:
st.write(f"**Patient:** {request.patient_name}")
st.write(f"**Phone:** {request.patient_phone}")
st.write(f"**Status:** {booking['status']}")
st.write(f"**Payment:** {'Paid' if booking['payment_status'] else 'Pending'}")
st.write(f"**Method:** {booking['payment_method']}")
# Download booking confirmation
booking_data = {
"booking": booking,
"patient_info": request.__dict__,
"doctor_details": best_doctor
}
st.download_button(
label="π₯ Download Booking Confirmation",
data=json.dumps(booking_data, indent=2),
file_name=f"booking_confirmation_{booking['id']}.json",
mime="application/json"
)
# Show agent results if available
if 'agent_result' in booking:
with st.expander("π€ Agent Results"):
st.text(booking['agent_result'])
else:
st.error("Failed to book appointment. Please try again.")
except Exception as e:
st.error(f"β Error during booking process: {str(e)}")
with col2:
st.header("π System Statistics")
total_bookings = len(booking_system.bookings)
confirmed_bookings = len([b for b in booking_system.bookings if b['status'] == 'confirmed'])
st.metric("Total Bookings", total_bookings)
st.metric("Confirmed", confirmed_bookings)
st.metric("Success Rate", f"{(confirmed_bookings/total_bookings*100):.1f}%" if total_bookings > 0 else "0%")
st.header("π‘ Quick Start Guide")
st.markdown("""
1. **Fill in patient details**
2. **Select medical specialty**
3. **Set location and price range**
4. **Choose preferred date**
5. **Describe symptoms**
6. **Configure agents**
7. **Click 'Start Agentic Search'**
""")
st.header("π§ Agent Configuration")
st.markdown("""
**CrewAI Agents:**
- Search Agent: Finds doctors online
- Booking Agent: Handles appointment booking
- Verification Agent: Confirms details
**Web Scraping:**
- Searches real doctor websites
- Extracts availability and pricing
- Simulates booking process
""")
if st.button("π View Booking History", use_container_width=True):
if booking_system.bookings:
st.subheader("π Recent Bookings")
for booking in booking_system.bookings[-5:]: # Show last 5 bookings
st.write(f"**{booking['id']}** - {booking['doctor_name']} ({booking['date']})")
else:
st.info("No bookings yet.")
if __name__ == "__main__":
main() |