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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() |