import time
import random
import sys
from typing import TypedDict
from langgraph.graph import StateGraph, END
class AgentState(TypedDict):
"""This dictionary is passed along the graph. Each node can read or update it."""
user_input: str
intent: str
agent_response: str
final_output: str
class AdmissionAgent:
def get_info(self) -> str:
return (
" [ADMISSION DESK]\n"
"University Admissions Portal.\n\n"
"Here is the UGC-approved admission process for the 2026 academic session:\n"
" 1. Application: Register via the official portal (requires Aadhar integration).\n"
" 2. Eligibility: Valid JEE Main or CUET UG score is mandatory for B.Tech.\n"
" 3. Documentation: Upload 10th & 12th Marksheets, Transfer Certificate (TC),\n"
" and Category Certificate (if applicable).\n"
" 4. Fee Payment: Pay the ₹2,000 non-refundable counseling fee via UPI or NetBanking.\n\n"
" Pro-Tip: Seat allocation is based on centralized counseling rounds (JoSAA/CSAB). "
"Keep an eye on the portal for cutoff dates!"
)
class ExamAgent:
def get_info(self) -> str:
return (
" [EXAMINATION CELL]\n"
"Important Updates for the Current Odd Semester (Winter):\n\n"
" • End-Semester Exams: Commencing strictly from 10th December.\n"
" • Hall Tickets: Will be available on the college portal 5 days prior.\n"
" • Dues Clearance: Ensure all library and hostel dues are cleared to download your Admit Card.\n\n"
" Note: A minimum of 75% attendance is strictly required to appear for examinations as per university norms."
)
class FeesAgent:
def get_info(self) -> str:
return (
" [ACCOUNTS & BILLING]\n"
"Fee Structure for First-Year B.Tech (Per Semester):\n\n"
" • Tuition Fee : ₹65,000\n"
" • Development Fund : ₹12,000\n"
" • Hostel & Mess (Avg) : ₹35,000\n"
" • Caution Money : ₹5,000 (Refundable)\n"
" ----------------------------------\n"
" Total Estimated : ₹1,17,000\n\n"
)
class ScholarshipAgent:
def get_info(self) -> str:
return (
" [SCHOLARSHIP & WELFARE]\n"
"We believe financial constraints should not hinder education. Available schemes:\n\n"
" 1. Merit-cum-Means (MCM): 50% tuition waiver for students with family income < ₹5 LPA and CGPA > 8.0.\n"
" 2. State/Central Schemes: Apply directly via the National Scholarship Portal (NSP).\n"
" 3. Jharkhand eKalyan: Post-matric scholarship for eligible SC/ST/BC students domiciled in Jharkhand.\n"
" 4. PMSSS: Special scheme for students from J&K and Ladakh.\n"
" 5. Category Waivers: As per Govt. of India reservation guidelines for SC/ST/OBC (NCL).\n\n"
"Visit the Dean of Student Welfare (DSW) office for manual form verifications."
)
class SemanticRouter:
def __init__(self):
self.intent_map = {
"admission": ["admission", "apply", "join", "enroll", "cutoff", "jee", "cuet", "process"],
"exam": ["exam", "test", "admit card", "hall ticket", "date sheet", "marks", "result", "semester"],
"fees": ["fee", "cost", "money", "pay", "tuition", "hostel fee", "loan", "dues"],
"scholarship": ["scholarship", "waiver", "concession", "financial aid", "nsp", "mcm"],
"help": ["help", "options", "menu", "what can you do"],
"exit": ["bye", "exit", "quit", "leave", "close"]
}
def detect_intent(self, text: str) -> str:
text = text.lower().strip()
for intent, keywords in self.intent_map.items():
if any(keyword in text for keyword in keywords):
return intent
return "unknown"
def node_intent_classifier(state: AgentState):
"""Classifies the user input and updates the state."""
router = SemanticRouter()
intent = router.detect_intent(state["user_input"])
return {"intent": intent}
def node_admission_agent(state: AgentState):
return {"agent_response": AdmissionAgent().get_info()}
def node_exam_agent(state: AgentState):
return {"agent_response": ExamAgent().get_info()}
def node_fees_agent(state: AgentState):
return {"agent_response": FeesAgent().get_info()}
def node_scholarship_agent(state: AgentState):
return {"agent_response": ScholarshipAgent().get_info()}
def node_response_agent(state: AgentState):
"""Formats the final response based on what was gathered or if it was a system command."""
intent = state.get("intent")
if intent == "exit":
messages = [
"Session terminated. Best of luck with your academic journey!",
"Goodbye! Feel free to return if you have more questions. ",
"Logging off."
]
final_text = random.choice(messages)
elif intent == "help":
final_text = (
" [SYSTEM HELPER]\n"
"I am the AI Assistant for the University.\n"
"Try asking me things like:\n"
" • How do I apply for admission?\n"
" • When are the semester exams?\n"
" • What is the B.Tech fee structure?\n"
" • Are there any scholarships available?\n\n"
"Type 'exit' to close the session."
)
elif intent == "unknown":
final_text = (
" [SYSTEM] I couldn't quite understand that query.\n"
"Currently, I am programmed to assist with Admissions, Exams, Fees, and Scholarships.\n"
"Please try rephrasing or type 'help' to see your options."
)
else:
final_text = state.get("agent_response", "Error generating response.")
return {"final_output": final_text}
def route_by_intent(state: AgentState) -> str:
"""Reads the intent from the state and determines the next node."""
intent = state["intent"]
valid_agents = ["admission", "exam", "fees", "scholarship"]
if intent in valid_agents:
return f"{intent}_agent"
else:
return "response_agent"
workflow = StateGraph(AgentState)
workflow.add_node("intent_classifier", node_intent_classifier)
workflow.add_node("admission_agent", node_admission_agent)
workflow.add_node("exam_agent", node_exam_agent)
workflow.add_node("fees_agent", node_fees_agent)
workflow.add_node("scholarship_agent", node_scholarship_agent)
workflow.add_node("response_agent", node_response_agent)
workflow.set_entry_point("intent_classifier")
workflow.add_conditional_edges(
"intent_classifier",
route_by_intent,
{
"admission_agent": "admission_agent",
"exam_agent": "exam_agent",
"fees_agent": "fees_agent",
"scholarship_agent": "scholarship_agent",
"response_agent": "response_agent"
}
)
workflow.add_edge("admission_agent", "response_agent")
workflow.add_edge("exam_agent", "response_agent")
workflow.add_edge("fees_agent", "response_agent")
workflow.add_edge("scholarship_agent", "response_agent")
workflow.add_edge("response_agent", END)
app = workflow.compile()
def print_delayed(text: str, delay: float = 0.015):
"""Creates a professional typing effect in the terminal."""
for char in text:
sys.stdout.write(char)
sys.stdout.flush()
time.sleep(delay)
print("\n")
def main():
print("=" * 60)
print(" LANGGRAPH BASED UNIVERSITY AI SYSTEM")
print("=" * 60)
user_name = input("\n Enter your name to initiate session: ").strip().title()
if not user_name:
user_name = "Student"
welcome_msg = (
f"\nWelcome, {user_name}! I am the centralized AI Assistant.\n"
"How can I help you today? (Type 'help' for options)"
)
print_delayed(welcome_msg)
while True:
user_input = input(f"\n{user_name} > ")
if not user_input.strip():
continue
initial_state = {"user_input": user_input, "intent": "", "agent_response": "", "final_output": ""}
result_state = app.invoke(initial_state)
time.sleep(0.3)
print("-" * 60)
print_delayed(result_state["final_output"])
print("-" * 60)
if result_state["intent"] == "exit":
break
if __name__ == "__main__":
try:
main()
except KeyboardInterrupt:
print("\n\n Exiting system...")
sys.exit(0)