import gradio as gr
from dotenv import load_dotenv
from langchain_core.messages import HumanMessage, AIMessage

from database.db_handler import init_db
from langchain_logic.agent_setup import create_agent_executor

# Load environment variables from .env for local development
# On Hugging Face, this line will do nothing, which is what we want.
load_dotenv() 

# --- App Setup ---
# Initialize the database and table if they don't exist
print("Initializing database...")
init_db()
print("Database initialized.")

# Create the agent executor
agent_executor = create_agent_executor()
print("Agent Executor created.")

# --- Gradio Interface ---

# We need to manage chat history
def respond(message, chat_history):
    # Convert Gradio's chat history to LangChain's format
    history_langchain_format = []
    for human, ai in chat_history:
        history_langchain_format.append(HumanMessage(content=human))
        history_langchain_format.append(AIMessage(content=ai))

    # Invoke the agent
    response = agent_executor.invoke({
        "input": message,
        "chat_history": history_langchain_format
    })
    
    # Append the new interaction to the chat history
    chat_history.append((message, response['output']))
    return "", chat_history


# Build the Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("# Appointment Scheduling Assistant")
    chatbot = gr.Chatbot()
    msg = gr.Textbox(label="Your Message", placeholder="Type your request here (e.g., 'show all appointments', 'book a haircut for Jane Doe')")
    clear = gr.Button("Clear")

    msg.submit(respond, [msg, chatbot], [msg, chatbot])
    clear.click(lambda: None, None, chatbot, queue=False)

if __name__ == "__main__":
    demo.launch(debug=True) # debug=True is for local testing