app.py

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

# Data for tabular models
TABULAR_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,
    },
}

# Data for LLMs
LLM_MODEL_EVALS = {
    "LLM (General OSIR)": {
        "Nexa Mistral Sci-7B": 0.61,
        "Llama-3-8B-Instruct": 0.39,
        "Mixtral-8x7B-Instruct-v0.1": 0.41,
        "Claude-3-Sonnet": 0.64,
        "GPT-4-Turbo": 0.68,
        "GPT-4o": 0.71,
    },
    "LLM (Field-Specific OSIR)": {
        "Nexa Bio Adapter": 0.66,
        "Nexa Astro Adapter": 0.70,
        "GPT-4o (Biomed)": 0.69,
        "Claude-3-Opus (Bio)": 0.67,
        "Llama-3-8B-Bio": 0.42,
        "Mixtral-8x7B-BioTune": 0.43,
    },
}

# Universal plotting function for horizontal bar charts
def plot_horizontal_bar(domain, data, color):
    sorted_items = sorted(data.items(), key=lambda x: x[1], reverse=True)
    models, scores = zip(*sorted_items)

    fig = go.Figure()
    fig.add_trace(go.Bar(
        x=scores,
        y=models,
        orientation='h',
        marker_color=color,
    ))

    fig.update_layout(
        title=f"Model Benchmark Scores — {domain}",
        xaxis_title="Score",
        yaxis_title="Model",
        xaxis_range=[0, 1.0],
        template="plotly_white",
        height=500,
        margin=dict(l=120, r=20, t=40, b=40),
    )
    return fig

# Display functions for each section
def display_tabular_eval(domain):
    if domain not in TABULAR_MODEL_EVALS:
        return None, "Invalid domain selected"
    plot = plot_horizontal_bar(domain, TABULAR_MODEL_EVALS[domain], 'indigo')
    details = json.dumps(TABULAR_MODEL_EVALS[domain], indent=2)
    return plot, details

def display_llm_eval(domain):
    if domain not in LLM_MODEL_EVALS:
        return None, "Invalid domain selected"
    plot = plot_horizontal_bar(domain, LLM_MODEL_EVALS[domain], 'lightblue')
    details = json.dumps(LLM_MODEL_EVALS[domain], indent=2)
    return plot, details

# Gradio interface
with gr.Blocks(css="body {font-family: 'Inter', sans-serif; background-color: #fafafa;}") as demo:
    gr.Markdown("""
    # 🔬 Nexa Evals — Scientific ML Benchmark Suite
    A comprehensive benchmarking suite comparing Nexa models against state-of-the-art models across scientific domains and language models.
    """)

    with gr.Tabs():
        with gr.TabItem("Tabular Models"):
            with gr.Row():
                tabular_domain = gr.Dropdown(
                    choices=list(TABULAR_MODEL_EVALS.keys()),
                    label="Select Domain",
                    value="Proteins"
                )
                show_tabular_btn = gr.Button("Show Evaluation")
            tabular_plot = gr.Plot(label="Benchmark Plot")
            tabular_details = gr.Code(label="Raw Scores (JSON)", language="json")
            show_tabular_btn.click(
                fn=display_tabular_eval,
                inputs=tabular_domain,
                outputs=[tabular_plot, tabular_details]
            )

        with gr.TabItem("LLMs"):
            with gr.Row():
                llm_domain = gr.Dropdown(
                    choices=list(LLM_MODEL_EVALS.keys()),
                    label="Select Domain",
                    value="LLM (General OSIR)"
                )
                show_llm_btn = gr.Button("Show Evaluation")
            llm_plot = gr.Plot(label="Benchmark Plot")
            llm_details = gr.Code(label="Raw Scores (JSON)", language="json")
            show_llm_btn.click(
                fn=display_llm_eval,
                inputs=llm_domain,
                outputs=[llm_plot, llm_details]
            )

    gr.Markdown("""
    ---
    ### ℹ️ About
    Nexa Evals provides benchmarks for both tabular models and language models in scientific domains:
    - **Tabular Models**: Evaluated on domain-specific metrics (e.g., accuracy, GDT-TS) across fields like Proteins, Astro, Materials, QST, HEP, and CFD.
    - **Language Models**: Assessed using the SciEval benchmark under the OSIR initiative, focusing on scientific utility, information entropy, internal consistency, hypothesis framing, domain grounding, and math logic.
    Scores range from 0 to 1, with higher values indicating better performance. Models are sorted by score in descending order for easy comparison.
    """)

demo.launch()