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# #!/usr/bin/env python
# import os
# import gradio as gr
# from services.data_service import DataService
# from services.ui_service import general_chat_ui, study_support_ui
# from configs.config import GPT4O_MODEL, CLAUDE_MODEL
# from vector_services.data_curator import DataCurator
# def main() -> None:
# base_dir = "./usiu-knowledge-base"
# vector_store_dir = "./usiu_vector_db"
# # Load the existing vector store and obtain its retriever.
# curator = DataCurator(knowledge_base_dir=base_dir, persist_directory=vector_store_dir)
# vector_store = curator.load_vectorstore()
# retriever = curator.get_retriever()
# # Initialize DataService with the retriever so that general chat uses RAG.
# data_service = DataService(retriever=retriever)
# prompts_service = data_service.prompts_service
# # Get system prompts for general and study support.
# general_chat_prompt = prompts_service.get_prompt("general")
# study_prompt = prompts_service.get_prompt("study")
# # Use the ui_service functions that already manage state properly.
# general_ui = general_chat_ui(general_chat_prompt, GPT4O_MODEL)
# study_ui = study_support_ui(study_prompt, CLAUDE_MODEL)
# # Assemble the interfaces in tabs.
# interfaces = [general_ui, study_ui]
# tab_names = ["General Academic Chat", "Study Support Chat"]
# demo = gr.TabbedInterface(interfaces, tab_names)
# demo.launch(share=True, inbrowser=True, server_name="localhost", server_port=8001)
# if __name__ == "__main__":
# main()
#!/usr/bin/env python
import os
import gradio as gr
from services.data_service import DataService
from services.ui_service import dashboard_ui
from configs.config import GPT4O_MODEL, CLAUDE_MODEL
from vector_services.data_curator import DataCurator
def main() -> None:
base_dir = "./usiu-knowledge-base"
vector_store_dir = "./vector_services/usiu_vector_db"
# Load the existing vector store and obtain its retriever.
curator = DataCurator(knowledge_base_dir=base_dir, persist_directory=vector_store_dir)
vector_store = curator.load_vectorstore()
retriever = curator.get_retriever()
# Initialize DataService with the retriever so that general chat uses RAG.
data_service = DataService(retriever=retriever)
prompts_service = data_service.prompts_service
# Get system prompts for general and study support.
general_chat_prompt, study_prompt = prompts_service.get_prompt()
# Build the dashboard, passing the relevant prompts and model identifiers.
dashboard = dashboard_ui(general_chat_prompt, GPT4O_MODEL, study_prompt, CLAUDE_MODEL)
dashboard.launch(share=True, inbrowser=True, server_name="0.0.0.0")
if __name__ == "__main__":
main()
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