Update app.py

app.py

@@ -1,212 +1,68 @@
-from fastapi import APIRouter, HTTPException, Request
-from pydantic import BaseModel
-from typing import List, Optional, Dict
-import time
+"""
+LibBee - Main Application Entry Point
+"""
+import sys
 import os
-import re
-import json
-from datetime import datetime
 
-# Import OpenAI for classification
-try:
-    from langchain_openai import ChatOpenAI
-    from langchain_anthropic import ChatAnthropic
-except ImportError:
-    pass
-
-router = APIRouter()
-
-class ChatMessage(BaseModel):
-    role: str
-    content: str
-
-class AgentRequest(BaseModel):
-    question: str
-    model: str = "gpt"
-    history: List[ChatMessage] = []
-
-class AgentResponse(BaseModel):
-    answer: str
-    intent: str = "general"
-    tools_used: List[str] = []
-    search_results: List[dict] = []
-    sources: List[dict] = []
-    model_used: str = "gpt"
-    response_time: float = 0
-
-# ========== STAFF DIRECTORY DATA (from app81.py) ==========
-STAFF_DIRECTORY = [
-    {
-        "full_name": "Dr. Abdulla Al Hefeiti",
-        "role": "Library Director / Assistant Provost, Libraries",
-        "details": "He can help with strategic and institutional matters, library leadership, and partnerships. Email: abdulla.alhefeiti@ku.ac.ae | Phone: +971 2 312 3331",
-        "tokens": ["abdulla", "hefeiti", "abdulla al hefeiti", "al hefeiti"],
-    },
-    {
-        "full_name": "Nikesh Narayanan",
-        "role": "Research & Access Services Librarian",
-        "details": "He can help with research support, Khazna institutional repository, ORCID, SciVal, Scopus, research impact, AI tools for research, bibliometrics, scholarly communication, and LibGuides. Email: nikesh.narayanan@ku.ac.ae | Phone: +971 2 312 3980",
-        "tokens": ["nikesh", "narayanan", "nikesh narayanan"],
-    },
-    {
-        "full_name": "Jason Fetty",
-        "role": "Medical Librarian",
-        "details": "He can help with medical and health sciences research, systematic reviews, PubMed, Embase, CINAHL, UpToDate, and clinical databases. Email: jason.fetty@ku.ac.ae | Phone: +971 2 312 4722",
-        "tokens": ["jason", "fetty", "jason fetty"],
-    },
-    {
-        "full_name": "Walter Brian Hall",
-        "role": "Digital & Technology Services Librarian / Systems Librarian",
-        "details": "He can help with library website, systems, technology, digital infrastructure, ORCID, STEAM, coding, Open Access publishing, APC funding, general library services, and public services. Email: walter.hall@ku.ac.ae | Phone: +971 2 312 3163",
-        "tokens": ["walter", "brian", "hall", "walter brian hall"],
-    },
-]
-
-# ========== REGEX PATTERNS (from app81.py) ==========
-SOCIAL_RE = re.compile(r"^(hi|hello|hey|good morning|good afternoon|good evening|how are you|who are you|what can you do|thanks|thank you|bye|goodbye)\b", re.IGNORECASE)
-PURE_GREETING_RE = re.compile(r"^(hi|hello|hey|good morning|good afternoon|good evening)[!.\s]*$", re.IGNORECASE)
-HOURS_RE = re.compile(r"\b(library hours?|opening hours?|when.*open|when.*close|open today|closed today)\b", re.IGNORECASE)
-LIBRARY_CUE_RE = re.compile(r"\b(library|librarian|borrow|primo|database|article|book|ill|interlibrary|full text)\b", re.IGNORECASE)
-MEDICAL_KEYWORDS = ['medicine','medical','health','clinical','nursing','pharmacy','pubmed','embase']
-
-_CLASSIFIER_SYSTEM = """You are the routing brain of LibBee...""" # [Truncated for brevity - copy from app81.py lines ~875-915]
-
-# ========== HELPER FUNCTIONS ==========
-def _normalize_name_query(text: str):
-    return [t for t in re.sub(r"[^a-z0-9 ]+", " ", (text or "").lower()).split() if t]
-
-def _match_staff_name(question: str) -> Optional[Dict]:
-    tokens = _normalize_name_query(question)
-    if not tokens:
-        return None
-    for staff in STAFF_DIRECTORY:
-        staff_tokens = set()
-        for tok in staff.get("tokens", []):
-            staff_tokens.update(_normalize_name_query(tok))
-        if all(tok in staff_tokens for tok in tokens):
-            return staff
-    return None
-
-def _staff_name_answer(staff: dict) -> str:
-    return f"Did you mean <strong>{staff['full_name']}</strong>?<br><br><strong>{staff['full_name']}</strong> is the <strong>{staff['role']}</strong>. {staff['details']}"
-
-def _looks_library_hours_question(question: str) -> bool:
-    return bool(HOURS_RE.search(question.lower()))
-
-def _hours_redirect_answer() -> str:
-    return """
-    Here are the current <strong>Khalifa University Library</strong> hours:<br><br>
-    <strong>Habshan Library (SAN Campus):</strong><br>
-    Sunday – Wednesday: 8:00 AM – 7:00 PM<br>
-    Thursday: 8:00 AM – 4:00 PM<br>
-    Friday: Closed<br>
-    Saturday: 12:00 PM – 8:00 PM<br><br>
-    ⚠️ Check official page for latest times: <a href="https://library.ku.ac.ae/hours">library.ku.ac.ae/hours</a>
-    """
-
-async def _llm_classify(question: str, history: list, model: str = "gpt"):
-    """Copy this function from app81.py lines ~918-960"""
-    # Simplified version for now:
-    q = question.lower()
-    if SOCIAL_RE.match(q):
-        return {"intent": "social", "casual_answer": "Hello! I'm LibBee, the KU Library AI Assistant."}
-    elif any(k in q for k in MEDICAL_KEYWORDS):
-        return {"intent": "search_medical"}
-    elif LIBRARY_CUE_RE.search(q):
-        return {"intent": "library_info"}
-    else:
-        return {"intent": "general"}
-
-# ========== MAIN ENDPOINT ==========
-@router.post("", response_model=AgentResponse)
-async def agent_query(request: Request, req: AgentRequest):
-    start = time.time()
-    
-    question = req.question.strip()
-    
-    # 1. Instant handlers (from app81.py Layer 0)
-    
-    # Staff name match
-    staff_match = _match_staff_name(question)
-    if staff_match:
-        return AgentResponse(
-            answer=_staff_name_answer(staff_match),
-            intent="library_info",
-            tools_used=["staff_name_match"],
-            model_used=req.model,
-            response_time=time.time() - start
-        )
-    
-    # Hours question
-    if _looks_library_hours_question(question):
-        return AgentResponse(
-            answer=_hours_redirect_answer(),
-            intent="library_info",
-            tools_used=["hours_hardcoded"],
-            model_used=req.model,
-            response_time=time.time() - start
-        )
-    
-    # Greeting
-    if PURE_GREETING_RE.match(question):
-        return AgentResponse(
-            answer="Hi! I'm <strong>LibBee</strong>, the Khalifa University Library AI Assistant. I can help you find articles and books, search databases, and answer library questions. What would you like help with?",
-            intent="social_greeting",
-            tools_used=[],
-            model_used=req.model,
-            response_time=time.time() - start
-        )
-    
-    # 2. LLM Classification (Layer 1)
-    try:
-        classification = await _llm_classify(question, req.history, req.model)
-        intent = classification.get("intent", "general")
-        
-        # 3. Route by intent
-        if intent == "social":
-            return AgentResponse(
-                answer=classification.get("casual_answer", "Hello! How can I help?"),
-                intent="social",
-                model_used=req.model,
-                response_time=time.time() - start
-            )
-        
-        elif intent == "library_info":
-            # For now, return generic library info response
-            # Later: Add RAG here
-            return AgentResponse(
-                answer="I can help with library information. This is where your RAG logic will go.",
-                intent="library_info",
-                tools_used=["get_library_info"],
-                model_used=req.model,
-                response_time=time.time() - start
-            )
-            
-        elif intent in ["search_academic", "search_medical"]:
-            return AgentResponse(
-                answer=f"I'll help you search for {'medical' if intent == 'search_medical' else 'academic'} literature on: {question}",
-                intent=intent,
-                tools_used=["search_primo"],
-                model_used=req.model,
-                response_time=time.time() - start
-            )
-        
-        else:
-            return AgentResponse(
-                answer="I'm the KU Library AI Assistant. I can help you find articles, books, and answer library questions.",
-                intent="general",
-                model_used=req.model,
-                response_time=time.time() - start
-            )
-            
-    except Exception as e:
-        return AgentResponse(
-            answer=f"Error processing request: {str(e)}",
-            intent="error",
-            model_used=req.model,
-            response_time=time.time() - start
-        )
-
-@router.get("/test")
-async def test():
-    return {"status": "ok", "message": "Agent is working!"}
+# Add current directory to path for src imports
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+# Import your routers
+from src.api import agent
+from src.api import search
+from src.api import admin
+
+# Create the FastAPI app instance
+app = FastAPI(
+    title="LibBee - KU Library AI",
+    description="Khalifa University Library AI Assistant",
+    version="2.0.0"
+)
+
+# CORS Configuration
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=[
+        "https://nikeshn.github.io",  # Your GitHub Pages
+        "http://localhost:8080",
+        "http://localhost:3000",
+        "http://127.0.0.1:5500"
+    ],
+    allow_credentials=True,
+    allow_methods=["POST", "GET", "OPTIONS"],
+    allow_headers=["*"],
+    max_age=86400,
+)
+
+# Include routers
+app.include_router(agent.router, prefix="/agent", tags=["Agent"])
+app.include_router(search.router, prefix="/search", tags=["Search"])
+app.include_router(admin.router, prefix="/admin", tags=["Admin"])
+
+@app.get("/")
+def health_check():
+    return {
+        "status": "ok",
+        "version": "2.0.0",
+        "service": "LibBee KU Library AI",
+        "endpoints": ["/agent", "/search", "/admin"]
+    }
+
+@app.get("/config")
+def public_config():
+    return {
+        "welcome_message": "Hi! I'm LibBee, the Khalifa University Library AI Assistant.",
+        "default_model": "gpt",
+        "max_results": "5"
+    }
+
+@app.get("/year")
+def get_year():
+    from datetime import datetime
+    now = datetime.utcnow()
+    return {"year": now.year, "month": now.month, "date": now.strftime("%Y-%m-%d")}
+
+print("✅ LibBee API loaded successfully")