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https://github.com/coleam00/ai-agents-masterclass.git
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135 lines
4.6 KiB
Python
135 lines
4.6 KiB
Python
import asana
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from asana.rest import ApiException
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from openai import OpenAI
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from dotenv import load_dotenv
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from datetime import datetime
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import streamlit as st
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import json
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import os
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from langchain_core.tools import tool
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from langchain_openai import ChatOpenAI
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from langchain_anthropic import ChatAnthropic
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from langchain_core.messages import SystemMessage, AIMessage, HumanMessage, ToolMessage
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load_dotenv()
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model = os.getenv('LLM_MODEL', 'gpt-4o')
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configuration = asana.Configuration()
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configuration.access_token = os.getenv('ASANA_ACCESS_TOKEN', '')
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api_client = asana.ApiClient(configuration)
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tasks_api_instance = asana.TasksApi(api_client)
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@tool
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def create_asana_task(task_name, due_on="today"):
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"""
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Creates a task in Asana given the name of the task and when it is due
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Example call:
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create_asana_task("Test Task", "2024-06-24")
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Args:
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task_name (str): The name of the task in Asana
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due_on (str): The date the task is due in the format YYYY-MM-DD. If not given, the current day is used
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Returns:
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str: The API response of adding the task to Asana or an error message if the API call threw an error
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"""
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if due_on == "today":
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due_on = str(datetime.now().date())
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task_body = {
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"data": {
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"name": task_name,
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"due_on": due_on,
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"projects": [os.getenv("ASANA_PROJECT_ID", "")]
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}
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}
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try:
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api_response = tasks_api_instance.create_task(task_body, {})
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return json.dumps(api_response, indent=2)
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except ApiException as e:
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return f"Exception when calling TasksApi->create_task: {e}"
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def prompt_ai(messages, nested_calls=0):
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if nested_calls > 5:
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raise "AI is tool calling too much!"
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# First, prompt the AI with the latest user message
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tools = [create_asana_task]
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asana_chatbot = ChatOpenAI(model=model) if "gpt" in model.lower() else ChatAnthropic(model=model)
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asana_chatbot_with_tools = asana_chatbot.bind_tools(tools)
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stream = asana_chatbot_with_tools.stream(messages)
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first = True
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for chunk in stream:
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if first:
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gathered = chunk
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first = False
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else:
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gathered = gathered + chunk
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yield chunk
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has_tool_calls = len(gathered.tool_calls) > 0
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# Second, see if the AI decided it needs to invoke a tool
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if has_tool_calls:
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# If the AI decided to invoke a tool, invoke it
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available_functions = {
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"create_asana_task": create_asana_task
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}
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# Add the tool request to the list of messages so the AI knows later it invoked the tool
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messages.append(gathered)
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# Next, for each tool the AI wanted to call, call it and add the tool result to the list of messages
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for tool_call in gathered.tool_calls:
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tool_name = tool_call["name"].lower()
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selected_tool = available_functions[tool_name]
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tool_output = selected_tool.invoke(tool_call["args"])
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messages.append(ToolMessage(tool_output, tool_call_id=tool_call["id"]))
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# Call the AI again so it can produce a response with the result of calling the tool(s)
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additional_stream = prompt_ai(messages, nested_calls + 1)
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for additional_chunk in additional_stream:
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yield additional_chunk
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def main():
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st.title("Asana Chatbot")
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# Initialize chat history
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if "messages" not in st.session_state:
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st.session_state.messages = [
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SystemMessage(content=f"You are a personal assistant who helps manage tasks in Asana. The current date is: {datetime.now().date()}")
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]
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# Display chat messages from history on app rerun
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for message in st.session_state.messages:
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message_json = json.loads(message.json())
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message_type = message_json["type"]
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if message_type in ["human", "ai", "system"]:
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with st.chat_message(message_type):
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st.markdown(message_json["content"])
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# React to user input
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if prompt := st.chat_input("What would you like to do today?"):
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# Display user message in chat message container
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st.chat_message("user").markdown(prompt)
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# Add user message to chat history
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st.session_state.messages.append(HumanMessage(content=prompt))
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# Display assistant response in chat message container
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with st.chat_message("assistant"):
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stream = prompt_ai(st.session_state.messages)
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response = st.write_stream(stream)
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st.session_state.messages.append(AIMessage(content=response))
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if __name__ == "__main__":
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main() |