trip_agents.py

from crewai import Agent
import re
import streamlit as st
from langchain_community.llms import OpenAI

from tools.browser_tools import BrowserTools
from tools.calculator_tools import CalculatorTools
from tools.search_tools import SearchTools
from crewai.llm import LLM

model_kwargs = {
    "model": f"openai/{st.secrets['MODEL_ID']}", 
    "base_url": st.secrets["MODEL_BASE_URL"],
    "api_key": st.secrets["OPENAI_API_KEY"]
}
llm = LLM(**model_kwargs) # Loading Llama


class TripAgents():

    def city_selection_agent(self):
        return Agent(
            role='City Selection Expert',
            goal='Select the best city based on weather, season, and prices',
            backstory='An expert in analyzing travel data to pick ideal destinations',
            tools=[
                SearchTools.search_internet,
                BrowserTools.scrape_and_summarize_website,
            ],
            verbose=True,
            llm=llm
            # step_callback=streamlit_callback,
        )

    def local_expert(self):
        return Agent(
            role='Local Expert at this city',
            goal='Provide the BEST insights about the selected city',
            backstory="""A knowledgeable local guide with extensive information
        about the city, it's attractions and customs""",
            tools=[
                SearchTools.search_internet,
                BrowserTools.scrape_and_summarize_website,
            ],
            verbose=True,
            llm=llm
            # step_callback=streamlit_callback,
        )

    def travel_concierge(self):
        return Agent(
            role='Amazing Travel Concierge',
            goal="""Create the most amazing travel itineraries with budget and 
        packing suggestions for the city""",
            backstory="""Specialist in travel planning and logistics with 
        decades of experience""",
            tools=[
                SearchTools.search_internet,
                BrowserTools.scrape_and_summarize_website,
                CalculatorTools.calculate,
            ],
            verbose=True,
            llm=llm
            # step_callback=streamlit_callback,
        )

class StreamToExpander:
    def __init__(self, expander):
        self.expander = expander
        self.buffer = []
        self.colors = ['red', 'green', 'blue', 'orange']  # Define a list of colors
        self.color_index = 0  # Initialize color index

    def write(self, data):
        # Filter out ANSI escape codes using a regular expression
        cleaned_data = re.sub(r'\x1B\[[0-9;]*[mK]', '', data)

        # Check if the data contains 'task' information
        task_match_object = re.search(r'\"task\"\s*:\s*\"(.*?)\"', cleaned_data, re.IGNORECASE)
        task_match_input = re.search(r'task\s*:\s*([^\n]*)', cleaned_data, re.IGNORECASE)
        task_value = None
        if task_match_object:
            task_value = task_match_object.group(1)
        elif task_match_input:
            task_value = task_match_input.group(1).strip()

        if task_value:
            st.toast(":robot_face: " + task_value)

        # Check if the text contains the specified phrase and apply color
        if "Entering new CrewAgentExecutor chain" in cleaned_data:
            # Apply different color and switch color index
            self.color_index = (self.color_index + 1) % len(self.colors)  # Increment color index and wrap around if necessary

            cleaned_data = cleaned_data.replace("Entering new CrewAgentExecutor chain", f":{self.colors[self.color_index]}[Entering new CrewAgentExecutor chain]")

        if "City Selection Expert" in cleaned_data:
            # Apply different color 
            cleaned_data = cleaned_data.replace("City Selection Expert", f":{self.colors[self.color_index]}[City Selection Expert]")
        if "Local Expert at this city" in cleaned_data:
            cleaned_data = cleaned_data.replace("Local Expert at this city", f":{self.colors[self.color_index]}[Local Expert at this city]")
        if "Amazing Travel Concierge" in cleaned_data:
            cleaned_data = cleaned_data.replace("Amazing Travel Concierge", f":{self.colors[self.color_index]}[Amazing Travel Concierge]")
        if "Finished chain." in cleaned_data:
            cleaned_data = cleaned_data.replace("Finished chain.", f":{self.colors[self.color_index]}[Finished chain.]")

        self.buffer.append(cleaned_data)
        if "\n" in data:
            self.expander.markdown(''.join(self.buffer), unsafe_allow_html=True)
            self.buffer = []