File size: 2,564 Bytes
82fedd3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | from langchain_core.messages import HumanMessage, SystemMessage,AIMessageChunk
from langchain_core.runnables.config import RunnableConfig
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_core.prompts import ChatPromptTemplate
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import START, MessagesState, StateGraph
from langsmith import traceable
import chainlit as cl
from dotenv import load_dotenv
load_dotenv()
workflow = StateGraph(state_schema=MessagesState)
#print(os.environ.get("GOOGLE_API_KEY"))
model = ChatGoogleGenerativeAI(model="gemini-2.5-pro", temperature=0.5)
with open("sys_prompt.txt", "r",encoding="utf-8") as f:
sys_prompt=f.read()
ChatPromptTemplate.from_messages([SystemMessage(content=sys_prompt) ])
#model = ChatOpenAI(model="gpt-4o-mini", temperature=0)
def call_model(state: MessagesState):
response = model.invoke(state["messages"])
return {"messages": response}
workflow.add_edge(START, "model")
workflow.add_node("model", call_model)
memory = MemorySaver()
app = workflow.compile(checkpointer=memory)
@cl.password_auth_callback
def auth_callback(username: str, password: str):
# Fetch the user matching username from your database
# and compare the hashed password with the value stored in the database
if (username, password) == ("admin", "admin"):
return cl.User(
identifier="admin", metadata={"role": "admin", "provider": "credentials"}
)
else:
return None
# @cl.on_chat_resume
# async def on_chat_resume(thread):
# pass
@cl.on_message
async def main(message: cl.Message):
if message.elements:
for file in message.elements:
if file.mime not in ["image/png", "image/jpeg" , "document/pgf"]:
await cl.ErrorMessage(content="Unsupported file type").send()
answer = cl.Message(content="")
await answer.send()
config: RunnableConfig = {
"configurable": {"thread_id": cl.context.session.thread_id}
}
for msg, _ in app.stream(
{"messages": [HumanMessage(content=message.content)]},
config,
stream_mode="messages",
):
if isinstance(msg, AIMessageChunk):
answer.content += msg.content # type: ignore
await answer.update()
@cl.on_audio_chunk
async def on_audio_chunk(chunk: cl.InputAudioChunk):
return {"audio": chunk} |