src/langgraphagenticai/main.py

import streamlit as st 
import json

from src.langgraphagenticai.ui.streamlit.load_ui import LoadStreamlitUI
from src.langgraphagenticai.llms.groq_llm import GroqChatLLM

# MAIN function start
def load_langgraph_agenticai_app():
    """
    Loads and runs the LangGraph AgenticAI application with Streamlit UI.
    This function initializes the UI, handles user input, configures the 
    LLM model, sets up the graph based on the selected use case, and displays
    the output while implementing exception handling for robustness.
    """

    # Load UI
    ui = LoadStreamlitUI()
    user_input = ui.load_streamlit_ui()

    if not user_input:
        st.error("Error: Failed to load user input from the UI")
        return 

    # Text input for user message
    if st.session_state.IsFetchButtonClick:
        user_message = st.session_state.timeframe
    else:
        user_message = st.chat_input("Enter your message")

    if user_message:
        try:
            # Configure LLM
            obj_llm_config = GroqChatLLM(user_controls_input=user_input)
            model = obj_llm_config.get_llm_model()

            if not model:
                st.error("Error: LLM model could not be initialized")
                return

            # Initialize and set up the graph based on use case
            usecase = user_input.get('selected_usecase')
            if not usecase:
                st.error("Error: No use case selected.")
                return

            ## Graph builder
            graph_builder = GraphBuilder(model)

        except Exception as e:
            raise ValueError(f"Error Occurred with Exception: {e}")

    print(user_input)