import gradio as gr
import asyncio
import os
from dotenv import load_dotenv
os.system("pip install git+https://huggingface.co/spaces/Agents-MCP-Hackathon/iLearn.git")
from ilearn_core.agent import iLearnAgent
# Load environment variables for local development
load_dotenv()

def initialize_agent():
    """
    Called once when the Gradio app loads to create a single agent instance.
    """
    return iLearnAgent(
        provider_name=os.getenv("TOOL_DECISION_PROVIDER", "groq"),
        model_display_name="Llama 3 8B (Groq)"
    )

async def handle_chat_submit(user_message: str, history: list, agent_state: iLearnAgent):
    """
    Handles the chat interaction, streaming the agent's response to the UI.
    """
    history.append([user_message, ""])
    yield history

    full_response = ""
    async for chunk in agent_state.chat(user_message):
        full_response += chunk
        history[-1][1] = full_response
        yield history

with gr.Blocks(theme=gr.themes.Soft(), css="footer {display: none !important}") as demo:
    gr.Markdown("# 🤖 iLearn: The Autonomous Learning Agent")
    
    agent_state = gr.State()

    chatbot = gr.Chatbot(
        label="Conversation",
        bubble_full_width=False,
        height=600,
        render_markdown=True,
    )

    with gr.Row():
        msg_textbox = gr.Textbox(
            show_label=False,
            placeholder="Ask a question...",
            scale=7,
            autofocus=True,
            container=False,
        )
        submit_btn = gr.Button("Send", variant="primary", scale=1, min_width=150)

    demo.load(
        fn=initialize_agent,
        inputs=None,
        outputs=[agent_state],
        show_progress="hidden"
    )

    submit_action = msg_textbox.submit(
        fn=handle_chat_submit,
        inputs=[msg_textbox, chatbot, agent_state],
        outputs=[chatbot],
    )
    
    submit_btn.click(
        fn=handle_chat_submit,
        inputs=[msg_textbox, chatbot, agent_state],
        outputs=[chatbot],
    )
    
    submit_action.then(
        fn=lambda: gr.update(value=""),
        inputs=None,
        outputs=[msg_textbox],
        queue=False,
    )

if __name__ == "__main__":
    demo.queue().launch()