app.py

import gradio as gr
import plotly.graph_objs as go
import json

# Dummy data - replace with real model benchmarks later
MODEL_EVALS = {
    "Proteins": {
        "Nexa Bio1 (Secondary)": 0.71,
        "Porter6 (Secondary)": 0.8456,
        "DeepCNF (Secondary)": 0.85,
        "AlphaFold2 (Tertiary GDT-TS)": 0.924,
        "Nexa Bio2 (Tertiary)": 0.90,
    },
    "Astro": {
        "Nexa Astro": 0.97,
        "Baseline CNN": 0.89,
    },
    "Materials": {
        "Nexa Materials": 0.9999,
        "Random Forest Baseline": 0.92,
    },
    "QST": {
        "Nexa PIN Model": 0.80,
        "Quantum TomoNet": 0.85,
    },
    "HEP": {
        "Nexa HEP Model": 0.91,
        "CMSNet": 0.94,
    },
    "CFD": {
        "Nexa CFD Model": 0.92,
        "FlowNet": 0.89,
    },
}

def plot_domain(domain):
    models = list(MODEL_EVALS[domain].keys())
    scores = list(MODEL_EVALS[domain].values())

    fig = go.Figure()
    fig.add_trace(go.Bar(x=models, y=scores, marker_color='indigo'))
    fig.update_layout(
        title=f"Model Benchmark Scores — {domain}",
        xaxis_title="Model",
        yaxis_title="Score",
        yaxis_range=[0, 1.0],
        template="plotly_white",
        height=500
    )
    return fig

def get_model_details(domain):
    return json.dumps(MODEL_EVALS[domain], indent=2)

def display_eval(domain):
    plot = plot_domain(domain)
    details = get_model_details(domain)
    return plot, details

domain_list = list(MODEL_EVALS.keys())

with gr.Blocks(title="Nexa Evals — Scientific ML Benchmark Suite") as demo:
    gr.Markdown("""
    # 🔬 Nexa Evals
    A benchmarking suite comparing Nexa models against SOTA across scientific domains.
    """)

    with gr.Row():
        domain = gr.Dropdown(domain_list, label="Select Domain")
        show_btn = gr.Button("Run Evaluation")

    with gr.Row():
        plot_output = gr.Plot(label="Benchmark Plot")
        metrics_output = gr.Code(label="Raw Scores (JSON)", language="json")

    show_btn.click(display_eval, inputs=domain, outputs=[plot_output, metrics_output])

demo.launch()