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from langchain_google_genai import GoogleGenerativeAI
from crewai_tools import SerperDevTool
import datetime
import json
import os
import streamlit as st
from typing import Dict, List, Tuple, Union
from langchain_core.agents import AgentFinish
from dotenv import load_dotenv
load_dotenv()
# search tool
search_tool = SerperDevTool(n_results=5)
# model for generate content
llm = GoogleGenerativeAI(
model="gemini-pro", google_api_key=os.getenv("GOOGLE_API_KEY"))
# Step_callback function
def step_callback(agent_output: Union[str, List[Tuple[Dict, str]], AgentFinish], agent_name, *args):
with st.chat_message("AI"):
# Try to parse the output if it is a JSON string
if isinstance(agent_output, str):
try:
agent_output = json.loads(agent_output)
except json.JSONDecodeError:
pass
if isinstance(agent_output, list) and all(
isinstance(item, tuple) for item in agent_output
):
for action, description in agent_output:
# Print attributes based on assumed structure
st.write(f"Agent Name: {agent_name}")
st.write(f"Tool used: {getattr(action, 'tool', 'Unknown')}")
st.write(
f"Tool input: {getattr(action, 'tool_input', 'Unknown')}")
st.write(f"{getattr(action, 'log', 'Unknown')}")
with st.expander("Show observation"):
st.markdown(f"Observation\n\n{description}")
# Check if the output is a dictionary as in the second case
elif isinstance(agent_output, AgentFinish):
st.write(f"Agent Name: {agent_name}")
output = agent_output.return_values
st.write(f"I finished my task:\n{output['output']}")
# Handle unexpected formats
else:
st.write(type(agent_output))
st.write(agent_output)
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