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| import streamlit as st | |
| import json | |
| from src.langgraphagenticai.ui.streamlit.display_result import DisplayResultStreamlit | |
| from src.langgraphagenticai.ui.streamlit.load_ui import LoadStreamlitUI | |
| from src.langgraphagenticai.llms.groq_llm import GroqChatLLM | |
| from src.langgraphagenticai.graph.graph_builder import GraphBuilder | |
| # 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) | |
| try: | |
| graph = graph_builder.setup_graph(usecase) | |
| DisplayResultStreamlit(usecase, graph, user_message).display_result_in_ui() | |
| except Exception as e: | |
| st.error(f"Error: Graph setup failed {e}") | |
| return | |
| except Exception as e: | |
| raise ValueError(f"Error Occurred with Exception: {e}") |