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,
    },
}

# Data for Nexa Mistral Sci-7B Evaluation (based on the provided image)
NEXA_MISTRAL_EVALS = {
    "Nexa Mistral Sci-7B": {
        "Scientific Utility": {"OSIR (General)": 7.0, "OSIR-Field (Physics)": 8.5},
        "Symbolism & Math Logic": {"OSIR (General)": 6.0, "OSIR-Field (Physics)": 7.5},
        "Citation & Structure": {"OSIR (General)": 5.5, "OSIR-Field (Physics)": 6.0},
        "Thematic Grounding": {"OSIR (General)": 7.0, "OSIR-Field (Physics)": 8.0},
        "Hypothesis Framing": {"OSIR (General)": 6.0, "OSIR-Field (Physics)": 7.0},
        "Internal Consistency": {"OSIR (General)": 9.0, "OSIR-Field (Physics)": 9.5},
        "Entropy / Novelty": {"OSIR (General)": 6.5, "OSIR-Field (Physics)": 6.0},
    }
}

# Universal plotting function with highlighted Nexa models
def plot_horizontal_bar(domain, data, highlight_keyword="Nexa", highlight_color='indigo', default_color='lightgray'):
    sorted_items = sorted(data.items(), key=lambda x: x[1], reverse=True)
    models, scores = zip(*sorted_items)
    colors = [highlight_color if highlight_keyword in model else default_color for model in models]

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

    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),
        yaxis=dict(automargin=True),
    )
    return fig

# Plotting function for Nexa Mistral Sci-7B Evaluation
def plot_mistral_eval(metric):
    if metric not in NEXA_MISTRAL_EVALS["Nexa Mistral Sci-7B"]:
        return None, "Invalid metric selected"
    data = NEXA_MISTRAL_EVALS["Nexa Mistral Sci-7B"][metric]
    models = list(data.keys())
    scores = list(data.values())

    fig = go.Figure()
    fig.add_trace(go.Bar(
        x=scores,
        y=models,
        orientation='h',
        marker_color=['yellow', 'orange']  # Matching the provided image colors
    ))

    fig.update_layout(
        title=f"Nexa Mistral Sci-7B Evaluation: {metric}",
        xaxis_title="Score (1-10)",
        yaxis_title="Model",
        xaxis_range=[0, 10],
        template="plotly_white",
        height=400,
        margin=dict(l=120, r=20, t=40, b=40),
        yaxis=dict(automargin=True),
        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
    )
    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], highlight_color='indigo', default_color='lightgray')
    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], highlight_color='lightblue', default_color='gray')
    details = json.dumps(LLM_MODEL_EVALS[domain], indent=2)
    return plot, details

def display_mistral_eval(metric):
    plot = plot_mistral_eval(metric)
    details = json.dumps(NEXA_MISTRAL_EVALS["Nexa Mistral Sci-7B"][metric], indent=2)
    return plot, details

# Gradio interface with improved styling
with gr.Blocks(css="body {font-family: 'Inter', sans-serif; background-color: #f0f0f0; color: #333;}") as demo:
    gr.Markdown("""
    # 🔬 Nexa Evals — Scientific ML Benchmark Suite
    A comprehensive benchmarking suite comparing Nexa models against state-of-the-art 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]
            )

        with gr.TabItem("Nexa Mistral Sci-7B"):
            with gr.Row():
                mistral_metric = gr.Dropdown(
                    choices=list(NEXA_MISTRAL_EVALS["Nexa Mistral Sci-7B"].keys()),
                    label="Select Metric",
                    value="Scientific Utility"
                )
                show_mistral_btn = gr.Button("Show Evaluation")
            mistral_plot = gr.Plot(label="Benchmark Plot")
            mistral_details = gr.Code(label="Raw Scores (JSON)", language="json")
            show_mistral_btn.click(
                fn=display_mistral_eval,
                inputs=mistral_metric,
                outputs=[mistral_plot, mistral_details]
            )

    gr.Markdown("""
    ---
    ### ℹ️ About
    Nexa Evals provides benchmarks for tabular models, language models, and specific evaluations like Nexa Mistral Sci-7B:
    - **Tabular Models**: Evaluated on domain-specific metrics across fields like Proteins and Astro.
    - **LLMs**: Assessed using the SciEval benchmark under the OSIR initiative.
    - **Nexa Mistral Sci-7B**: Compares general (OSIR) and physics-specific (OSIR-Field) performance across multiple metrics.
    Scores are normalized where applicable (0-1 for tabular/LLMs, 1-10 for Mistral).
    """)

demo.launch()