app.py

import json
import os
import re
import sqlite3
from typing import TypedDict, Annotated

from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from dotenv import load_dotenv

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import (
    HumanMessage,
    BaseMessage,
    SystemMessage,
)
from langgraph.graph.message import add_messages
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.sqlite import SqliteSaver

# RAG imports
from rag import load_student_documents, create_vectorstore, get_retriever

load_dotenv(override=True)

# ------------------ RAG SETUP ------------------
documents = load_student_documents("studentDataset.csv")
vectorstore = create_vectorstore(documents)
retriever = get_retriever(vectorstore)

# ------------------ LLM ------------------
llm = ChatGoogleGenerativeAI(
    model="models/gemini-2.5-flash",
    temperature=0.3,
)

# ------------------ STATE ------------------
class ChatState(TypedDict):
    messages: Annotated[list[BaseMessage], add_messages]

# ------------------ GRAPH NODE ------------------
def chat_node(state: ChatState):
    user_msg = state["messages"][-1].content

    # Exact register number search
    match = re.search(r"\b\d{4,}\b", user_msg)
    docs = []
    if match:
        reg_no = match.group()
        docs = [d for d in documents if reg_no in d.page_content]

    # Vector fallback
    if not docs:
        docs = retriever.invoke(user_msg)

    context = "\n\n".join(d.page_content for d in docs)

    system_prompt = f"""
    You are a student database assistant.
    Answer ONLY using the information below.
    If the answer is not present, say "I don't have that information".
    DATA:
    {context}
    """

    response = llm.invoke([
        SystemMessage(content=system_prompt),
        HumanMessage(content=user_msg),
    ])

    return {"messages": [response]}

# ------------------ GRAPH ------------------
graph = StateGraph(ChatState)
graph.add_node("chat_node", chat_node)
graph.add_edge(START, "chat_node")
graph.add_edge("chat_node", END)

conn = sqlite3.connect("a.db", check_same_thread=False)
checkpointer = SqliteSaver(conn=conn)

workflow = graph.compile(checkpointer=checkpointer)

# ------------------ FASTAPI ------------------
app = FastAPI()

@app.websocket("/chat")
async def chat_ws(websocket: WebSocket):
    await websocket.accept()

    try:
        session_id = websocket.query_params.get("session_id", "default")

        while True:
            user_text = await websocket.receive_text()

            config = {"configurable": {"thread_id": session_id}}

            # STREAM RESPONSE
            for msg, _ in workflow.stream(
                {"messages": [HumanMessage(user_text)]},
                config=config,
                stream_mode="messages",
            ):
                if msg.content:
                    await websocket.send_json({
                        "type": "response",
                        "content": msg.content
                    })

            # SIGNAL COMPLETION
            await websocket.send_json({
                "type": "complete"
            })

    except WebSocketDisconnect:
        print("Middleware disconnected")
    except Exception as e:
        print(f"Backend error: {e}")
        await websocket.send_json({
            "type": "error",
            "message": "Backend error occurred"
        })