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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() | |