app.py

import gradio as gr
import pandas as pd
import json
import os
from evo_transformer import EvoTransformer
from plot import plot_radar_chart
from diagram import plot_architecture_diagram

# === Initialize ===
evo = EvoTransformer()

# === Gradio Output Placeholders ===
accuracy_output = gr.Text(label="Simulated Accuracy")
params_output = gr.Text(label="Estimated Parameters")
current_config_output = gr.Text(label="Current Config Summary")
radar_image = gr.Image(label="Final Generation Trait Radar")
diagram_image = gr.Image(label="Illustrative Transformer Structure")
history_outputs = []

# === Core Evolution Function ===
def evolve_model(generations):
    evo.__init__()  # Reset EvoTransformer
    evo.evolve(generations)
    score = evo.evaluate()
    radar_path = "radar_chart.png"
    diagram_path = "architecture_diagram.png"

    # Plot radar chart
    try:
        plot_radar_chart(evo.config, radar_path)
    except Exception as e:
        print("Radar chart error:", e)
        radar_path = None

    # Plot architecture diagram
    diagram_bytes = None
    try:
        plot_architecture_diagram(evo.config)
        if os.path.exists(diagram_path):
            with open(diagram_path, "rb") as f:
                diagram_bytes = f.read()
    except Exception as e:
        print("Diagram plot error:", e)

    # History cards
    history = evo.get_history()
    history_cards = [f"Gen {i+1} Config: {h}" for i, h in enumerate(history)]

    # History downloadables
    df = pd.DataFrame(history)
    df_csv_path = "evo_history.csv"
    df.to_csv(df_csv_path, index=False)

    json_path = "evo_history.json"
    with open(json_path, "w") as f:
        json.dump(history, f, indent=2)

    return (
        f"{score['accuracy']*100:.2f}%",
        f"{score['params']:.2f}M params",
        str(evo.config),
        radar_path,
        diagram_bytes,
        *history_cards,
        df_csv_path,
        json_path
    )

# === Gradio Interface ===
generations_input = gr.Slider(1, 10, value=3, step=1, label="Number of Generations")
evolve_btn = gr.Button("\U0001F9EC Evolve Architecture")

with gr.Blocks(title="EvoTransformer") as demo:
    gr.Markdown("""
    # 🧬 EvoTransformer – Evolving Transformer Architectures  
    Simulate trait mutation and adaptive architecture generation.
    """)

    with gr.Row():
        generations_input.render()
        evolve_btn.render()

    accuracy_output.render()
    params_output.render()
    current_config_output.render()

    gr.Markdown("## 🧬 Evolution History")
    radar_image.render()
    diagram_image.render()

    with gr.Accordion("Downloadable Files", open=True):
        csv_file = gr.File(label="Download CSV History")
        json_file = gr.File(label="Download JSON History")

    for _ in range(10):
        card = gr.Textbox(label="", interactive=False)
        history_outputs.append(card)

    evolve_btn.click(
        fn=evolve_model,
        inputs=[generations_input],
        outputs=[
            accuracy_output,
            params_output,
            current_config_output,
            radar_image,
            diagram_image,
            *history_outputs,
            csv_file,
            json_file
        ]
    )

demo.launch()