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from typing import TypedDict, Annotated
from langgraph.graph.message import add_messages
from langchain_core.messages import AnyMessage, HumanMessage, AIMessage, SystemMessage
from langgraph.prebuilt import ToolNode
from langgraph.graph import START, StateGraph
from langgraph.prebuilt import tools_condition
from tools import search_tool, weather_info_tool, hub_stats_tool
import gradio as gr
from smolagents import GradioUI, CodeAgent, HfApiModel
from retriever import load_guest_dataset
from dotenv import load_dotenv
import yaml
load_dotenv()
# Import our custom tools from their modules
from retriever import load_guest_dataset
# Import our custom tools from their modules
from retriever import load_guest_dataset
model = HfApiModel()
guest_info_tool = load_guest_dataset()
# Load prompts from YAML file
with open("prompts.yaml", 'r') as stream:
prompt_templates = yaml.safe_load(stream)
# Get the system prompt from the YAML file
system_prompt = prompt_templates["system_prompt"]
# Initialize the chat model
chat = ChatOllama(model="qwen2:7b",
verbose=True)
# Define available tools
tools = [
load_guest_dataset,
search_tool,
weather_info_tool,
hub_stats_tool
]
# Bind tools to the chat model
chat_with_tools = chat.bind_tools(tools)
# Generate the AgentState and Agent graph
class AgentState(TypedDict):
messages: Annotated[list[AnyMessage], add_messages]
def assistant(state: AgentState):
# If this is the first message, add the system prompt
if len(state["messages"]) == 1 and isinstance(state["messages"][0], HumanMessage):
# Add system message with the ReAct framework prompt
messages = [SystemMessage(content=system_prompt)] + state["messages"]
else:
messages = state["messages"]
return {
"messages": [chat_with_tools.invoke(messages)],
}
## The graph
builder = StateGraph(AgentState)
# Define nodes: these do the work
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))
# Define edges: these determine how the control flow moves
builder.add_edge(START, "assistant")
builder.add_conditional_edges(
"assistant",
# If the latest message requires a tool, route to tools
# Otherwise, provide a direct response
tools_condition,
)
builder.add_edge("tools", "assistant")
graph_app = builder.compile()
graph_state = {}
# Gradio expects a function with (chat_history, user_message) -> (updated_chat_history)
def chat_fn(message, history):
session_id = "session-123"
result = graph_app.invoke(
{"messages": [HumanMessage(content=message)]},
config={"configurable": {"thread_id": session_id}}
)
response = result["messages"][-1].content
history.append((message, response))
return history, ""
# Create Gradio interface
with gr.Blocks() as demo:
gr.Markdown("### LangGraph Chat with Gradio")
chatbot = gr.Chatbot()
msg = gr.Textbox(label="Type your message")
send_btn = gr.Button("Send")
# Hook the send button
send_btn.click(fn=chat_fn, inputs=[msg, chatbot], outputs=[chatbot, msg])
if __name__ == "__main__":
demo.launch()
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