chatbot.py

import os
from dotenv import load_dotenv
from langchain.chat_models import init_chat_model
from typing import Sequence
from langchain_core.messages import HumanMessage, BaseMessage
from langgraph.graph.message import add_messages
from typing_extensions import Annotated, TypedDict
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import START, StateGraph
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import warnings

warnings.filterwarnings("ignore")
load_dotenv()
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = os.getenv("GOOGLE_APPLICATION_CREDENTIALS")

model = init_chat_model("gemini-2.5-flash", model_provider="google_genai")

prompt_template = ChatPromptTemplate.from_messages(
    [
("system",
 """You are an appointment scheduling assistant for a local clinic system. 
You can directly list hospitals and doctors and create appointments 
by calling backend functions I provide. But don't tell which functions you are calling.
Be polite and user-friendly.
You do not refer users to outside systems like MHRS or any external websites.

When you list items (like hospitals or doctors):
- Always show them **vertically**, one per line.
- Use numbered or bulleted format with line breaks (e.g. each starts with '- ' or '1. ').
- Never write them inline separated by commas or asterisks.

When the user gives a hospital, doctor, date, or time,
respond with confirmation in a human way.

All data is stored locally in PostgreSQL."""),
        MessagesPlaceholder(variable_name="messages"),
    ]
)

class State(TypedDict):
    messages: Annotated[Sequence[BaseMessage], add_messages]
    language: str

workflow = StateGraph(state_schema=State)

def call_model(state: State):
    prompt = prompt_template.invoke(state)
    response = model.invoke(prompt)
    return {"messages": [response]}

workflow.add_edge(START, "model")
workflow.add_node("model", call_model)
memory = MemorySaver()
app = workflow.compile(checkpointer=memory)