import gradio as gr
from mec_api import run_mec_pipeline, get_demo_scenarios

# ─── LOAD DEMO SCENARIOS ─────────────────────────────────────────────────────────
scenario_map = get_demo_scenarios()

# ─── PROCESS SCENARIO FUNCTION ────────────────────────────────────────────────────
def process_scenario(selected_label):
    fusion_prompt, final_uesp, empathic_response, emid = run_mec_pipeline(selected_label)

    # Extract values from final_uesp
    primary_emotion = final_uesp.get('primary_emotion', 'TBD')
    emotion_arc = final_uesp.get('emotion_arc_trajectory', 'TBD')
    resonance = final_uesp.get('resonance_pattern', 'TBD')
    tone = final_uesp.get('tone', 'TBD')
    blend_states = final_uesp.get('blend_weights', 'None detected')
    intervention_strategy = final_uesp.get('intervention_strategy', 'RSM-DEFAULT')

    # Construct the Contextual Emotional State section
    emotion_summary = f"""
    Contextual Emotional State:
    - Primary Emotion: {primary_emotion}
    - Emotional Arc: {emotion_arc}
    - Resonance: {resonance}
    - Emotion ID (EmID): {emid}
  """

    # Construct the Empathic Response section
    empathic_response_section = f"""
    Empathic Response:
    {empathic_response}
    """

    return emotion_summary, empathic_response_section

# ─── BUILD GRADIO UI ───────────────────────────────────────────────────────────────
def create_ui():
    with gr.Blocks(title="MEC MVP DEMO") as demo:  
        gr.Markdown("## Master Emotional Core (MEC™)")

        scenario_selector = gr.Radio(
            choices=list(scenario_map.keys()),
            label="Choose a Demo Scenario"
        )
        scenario_display = gr.Textbox(
            label="Scenario Text", lines=4, interactive=False
        )

        # On scenario selection, display the text
        scenario_selector.change(
            fn=lambda label: scenario_map.get(label, ""),
            inputs=scenario_selector,
            outputs=scenario_display
        )

        run_btn = gr.Button("Run MEC")
        emotion_summary_box = gr.Textbox(
            label="MEC Emotion Summary", lines=6, interactive=False
        )
        empathic_response_box = gr.Textbox(
            label="Empathic Response", lines=6, interactive=False
        )

        # Process the selected scenario
        run_btn.click(
            fn=process_scenario,
            inputs=scenario_selector,
            outputs=[emotion_summary_box, empathic_response_box]
        )

    return demo

if __name__ == "__main__":
    ui = create_ui()
    ui.launch(share=True)