Update app.py

app.py

@@ -1,117 +1,3 @@
-# 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
-
-#     # 1️⃣ 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]
-
-#     # 2️⃣ 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}}
-#             response_text = ""
-
-#             for msg, _ in workflow.stream(
-#                 {"messages": [HumanMessage(user_text)]},
-#                 config=config,
-#                 stream_mode="messages",
-#             ):
-#                 if msg.content:
-#                     await websocket.send_text(msg.content)
-
-#             # signal end of response
-#             await websocket.send_text("__END__")
-
-#     except WebSocketDisconnect:
-#         print("Middleware disconnected")
-
-
-
 import json
 import os
 import re
@@ -155,14 +41,14 @@ class ChatState(TypedDict):
 def chat_node(state: ChatState):
     user_msg = state["messages"][-1].content
 
-    # 1️⃣ Exact register number search
+    # 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]
 
-    # 2️⃣ Vector fallback
+    # Vector fallback
     if not docs:
         docs = retriever.invoke(user_msg)
 
@@ -209,7 +95,7 @@ async def chat_ws(websocket: WebSocket):
 
             config = {"configurable": {"thread_id": session_id}}
 
-            # 🔥 STREAM RESPONSE
+            # STREAM RESPONSE
             for msg, _ in workflow.stream(
                 {"messages": [HumanMessage(user_text)]},
                 config=config,
@@ -221,7 +107,7 @@ async def chat_ws(websocket: WebSocket):
                         "content": msg.content
                     })
 
-            # ✅ SIGNAL COMPLETION
+            # SIGNAL COMPLETION
             await websocket.send_json({
                 "type": "complete"
             })