app.py

import gradio as gr
import matplotlib.pyplot as plt
import numpy as np

# Data for Tabular Models (normalized to 0-10 from original 0-1 data)
TABULAR_MODEL_EVALS = {
    "Proteins": {
        "Nexa Bio1 (Secondary)": 7.1,
        "Porter6 (Secondary)": 8.5,
        "DeepCNF (Secondary)": 8.5,
        "AlphaFold2 (Tertiary GDT-TS)": 9.2,
        "Nexa Bio2 (Tertiary)": 9.0,
    },
    "Astro": {
        "Nexa Astro": 9.7,
        "Baseline CNN": 8.9,
    },
    "Materials": {
        "Nexa Materials": 10.0,
        "Random Forest Baseline": 9.2,
    },
    "QST": {
        "Nexa PIN Model": 8.0,
        "Quantum TomoNet": 8.5,
    },
    "HEP": {
        "Nexa HEP Model": 9.1,
        "CMSNet": 9.4,
    },
    "CFD": {
        "Nexa CFD Model": 9.2,
        "FlowNet": 8.9,
    },
}

# Data for Nexa Mistral Sci-7B Evaluation (from your 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},
    }
}

# Plotting function using Matplotlib
def plot_comparison(domain, data_type):
    if data_type == "mistral":
        metric = domain
        data = NEXA_MISTRAL_EVALS["Nexa Mistral Sci-7B"][metric]
        models = list(data.keys())
        scores = list(data.values())
        fig, ax = plt.subplots(figsize=(8, 6), facecolor='#e0e0e0')
        y_pos = np.arange(len(models))
        width = 0.35
        ax.barh(y_pos - width/2, scores[:1], width, label=models[0], color='yellow')
        ax.barh(y_pos + width/2, scores[1:], width, label=models[1], color='orange')
    else:
        data = TABULAR_MODEL_EVALS[domain] if data_type == "tabular" else LLM_MODEL_EVALS[domain]
        models = list(data.keys())
        scores = list(data.values())
        fig, ax = plt.subplots(figsize=(8, 6), facecolor='#e0e0e0')
        y_pos = np.arange(len(models))
        width = 0.8
        colors = ['indigo' if 'Nexa' in model else 'lightgray' if data_type == "tabular" else 'gray' for model in models]
        ax.barh(y_pos, scores, width, color=colors)

    ax.set_yticks(y_pos)
    ax.set_yticklabels(models)
    ax.set_xlabel('Score (1-10)')
    ax.set_title(f"{('Nexa Mistral Sci-7B Evaluation: ' if data_type == 'mistral' else '')}{domain}")
    ax.set_xlim(0, 10)
    if data_type == "mistral":
        ax.legend()
    ax.grid(True, axis='x', linestyle='--', alpha=0.7)
    plt.tight_layout()

    return fig

# Display functions
def display_tabular_eval(domain):
    return plot_comparison(domain, "tabular")

def display_llm_eval(domain):
    return plot_comparison(domain, "llm")

def display_mistral_eval(metric):
    return plot_comparison(metric, "mistral")

# Gradio interface
with gr.Blocks(css="body {font-family: 'Inter', sans-serif; background-color: #e0e0e0; color: #333;}") as demo:
    gr.Markdown("""
    # 🔬 Nexa Evals — Scientific ML Benchmark Suite
    A benchmarking suite for Nexa models across various domains.
    """)

    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")
            show_tabular_btn.click(
                fn=display_tabular_eval,
                inputs=tabular_domain,
                outputs=tabular_plot
            )

        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")
            show_llm_btn.click(
                fn=display_llm_eval,
                inputs=llm_domain,
                outputs=llm_plot
            )

        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")
            show_mistral_btn.click(
                fn=display_mistral_eval,
                inputs=mistral_metric,
                outputs=mistral_plot
            )

    with gr.TabItem("About"):
        gr.Markdown("""
        # ℹ️ About Nexa Evals
        Nexa Evals benchmarks Nexa models across scientific domains:
        - **Tabular Models**: Compares Nexa models against baselines.
        - **LLMs**: Evaluates Nexa language models against competitors.
        - **Nexa Mistral Sci-7B**: Compares general and physics-specific performance.
        Scores are on a 1-10 scale.
        """)

demo.launch()