app.py

import gradio as gr

def mock_agent_with_trace(user_input):
    # Simulate a lightweight model's reasoning path (Trace) to match the Bonus Quest
    thought_trace = f"== [Agent Trace] ==\n1. Received instruction: '{user_input}'\n2. Invoking local Gemma-2B-it model for inference...\n3. Task analysis completed. Generating optimal response.\n===================="
    reply = f"Hello! I am a local-first task automation assistant powered by Gemma-2B. You just said: '{user_input}'. The system is running smoothly, ready to explore the Thousand Token Wood!"
    return thought_trace, reply

# Create a clean and thematic Gradio interface
with gr.Blocks(title="Gemma Local Task Agent") as demo:
    gr.Markdown("# 🌲 Build Small Hackathon - Gemma-2B Agent")
    gr.Markdown("A lightweight personal task automation prototype focused on visualizing explicit agent reasoning traces.")
    
    with gr.Group():
        user_msg = gr.Textbox(label="Enter your task command:", placeholder="e.g., Translate this recipe for my neighbor...")
        submit_btn = gr.Button("🚀 Run Agent (Simulated)", variant="primary")
    
    with gr.Row():
        trace_box = gr.Textbox(label="📡 Live Agent Trace", lines=8)
        output_box = gr.Textbox(label="🤖 Agent Final Output", lines=5)
        
    submit_btn.click(fn=mock_agent_with_trace, inputs=user_msg, outputs=[trace_box, output_box])

demo.launch()