app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, T5EncoderModel
import matplotlib.pyplot as plt
import numpy as np
from llmprop_model import T5Predictor
from llmprop_utils import replace_bond_lengths_with_num, replace_bond_angles_with_ang

device = torch.device("cpu")  # HuggingFace free tier is CPU only

tokenizer = AutoTokenizer.from_pretrained("tokenizers/t5_tokenizer_trained_on_modified_part_of_C4_and_textedge")
tokenizer.add_tokens(["[CLS]", "[NUM]", "[ANG]"])

base_model = T5EncoderModel.from_pretrained("google/t5-v1_1-small")
base_model.resize_token_embeddings(len(tokenizer))

model = T5Predictor(base_model, 512, drop_rate=0.2, pooling="cls")
model.load_state_dict(
    torch.load(
        "best_checkpoint_for_band_gap.pt",
        map_location=device
    ),
    strict=False
)
model.to(device)
model.eval()

TRAIN_MEAN = 1.0258
TRAIN_STD  = 1.5106

def predict(description):
    if not description.strip():
        return "Please enter a crystal description.", ""
    text = replace_bond_lengths_with_num(description)
    text = replace_bond_angles_with_ang(text)
    encoded = tokenizer(
        "[CLS] " + text,
        add_special_tokens=True,
        padding="max_length",
        truncation=True,
        max_length=256,
        return_tensors="pt"
    )
    input_ids      = encoded["input_ids"].to(device)
    attention_mask = encoded["attention_mask"].to(device)
    with torch.no_grad():
        _, prediction = model(input_ids, attention_mask)
    band_gap = (prediction.squeeze().cpu().item() * TRAIN_STD) + TRAIN_MEAN
    band_gap = max(0.0, band_gap)
    if band_gap < 0.1:
        material_type = "Metal (zero or near-zero band gap)"
        confidence    = "High"
    elif band_gap < 1.0:
        material_type = "Narrow gap semiconductor"
        confidence    = "Medium"
    elif band_gap < 3.0:
        material_type = "Semiconductor"
        confidence    = "Medium"
    else:
        material_type = "Wide gap semiconductor / Insulator"
        confidence    = "Low"
    result  = f"Predicted Band Gap: {band_gap:.4f} eV"
    details = f"Material Type: {material_type}\nConfidence: {confidence}"
    return result, details

def results_chart():
    true_vals = [0.00, 0.00, 2.68, 2.62, 0.43]
    pred_vals = [0.00, 0.02, 1.31, 1.55, 0.01]
    labels    = ["Sc2Co3Al", "PdCu11N4", "AgClO4", "Mg3Si4(BiO7)2", "LiCuSO4F"]
    fig, axes = plt.subplots(1, 2, figsize=(12, 5))
    ax1 = axes[0]
    ax1.scatter(true_vals, pred_vals, color="#38bdf8", s=100, zorder=5)
    max_val = max(max(true_vals), max(pred_vals)) + 0.5
    ax1.plot([0, max_val], [0, max_val], "r--", label="Perfect prediction")
    for i, label in enumerate(labels):
        ax1.annotate(label, (true_vals[i], pred_vals[i]),
                     textcoords="offset points", xytext=(5, 5), fontsize=8)
    ax1.set_xlabel("True Band Gap (eV)")
    ax1.set_ylabel("Predicted Band Gap (eV)")
    ax1.set_title("Predicted vs True Band Gap")
    ax1.legend()
    ax2 = axes[1]
    errors = [abs(t - p) for t, p in zip(true_vals, pred_vals)]
    ax2.bar(labels, errors, color="#38bdf8", edgecolor="#0284c7")
    ax2.axhline(y=0.6678, color="red", linestyle="--", label="Test MAE (0.6678 eV)")
    ax2.set_xlabel("Material")
    ax2.set_ylabel("Absolute Error (eV)")
    ax2.set_title("Prediction Error by Material")
    ax2.legend()
    plt.xticks(rotation=15, ha="right")
    plt.tight_layout()
    return fig

examples = [
    ["Silicon dioxide crystallizes in the orthorhombic structure. Si-O bond lengths range from 1.6 to 1.7 Angstroms. Bond angles are approximately 109 degrees."],
    ["Copper crystallizes in the face-centered cubic structure. Cu-Cu bond lengths are 2.55 Angstroms. Metallic bonding."],
    ["Zinc sulfide crystallizes in the cubic zinc blende structure. Zn-S bond lengths are 2.34 Angstroms. Coordination number is 4."],
]

with gr.Blocks(theme=gr.themes.Soft(), title="LLM-Prop") as demo:
    gr.Markdown("""
    # LLM-Prop: Crystal Band Gap Predictor
    **Predicting material band gaps from natural language crystal structure descriptions using T5 Transformer**
    """)
    with gr.Tabs():
        with gr.Tab("Demo"):
            gr.Markdown("### Paste a crystal structure description to predict its band gap")
            with gr.Row():
                with gr.Column():
                    inp = gr.Textbox(
                        label="Crystal Structure Description",
                        placeholder="e.g. Silicon dioxide crystallizes in the orthorhombic structure...",
                        lines=6
                    )
                    btn = gr.Button("Predict Band Gap", variant="primary")
                    gr.Examples(examples=examples, inputs=inp)
                with gr.Column():
                    out_result  = gr.Textbox(label="Prediction", lines=2)
                    out_details = gr.Textbox(label="Material Type & Confidence", lines=3)
            btn.click(fn=predict, inputs=inp, outputs=[out_result, out_details])
        with gr.Tab("Results"):
            gr.Markdown("### Model Performance on Test Samples")
            gr.Markdown("""
            | Metric | Value |
            |---|---|
            | Test MAE | 0.6678 eV |
            | Best Val MAE | 0.6393 eV |
            | Training epochs | 15 |
            | Training samples | 125,098 |
            | Model parameters | 35.3M |
            """)
            gr.Plot(value=results_chart())
        with gr.Tab("About"):
            gr.Markdown("""
            ## About LLM-Prop
            **LLM-Prop** is a deep learning system that predicts the band gap of crystalline
            materials from natural language descriptions of their crystal structures.
            ### Model Architecture
            - **Base model:** T5-small encoder (Google)
            - **Parameters:** 35.3 million
            - **Input:** Crystal structure text (max 256 tokens)
            - **Output:** Band gap in eV (regression)
            ### Dataset
            - **Training:** 125,098 samples
            - **Validation:** 9,945 samples
            - **Test:** 11,531 samples
            ### Results
            - **Test MAE:** 0.6678 eV
            - **Baseline MAE:** ~1.5 eV
            - **Improvement over baseline:** ~55%
            ### Reference
            Based on the paper: [LLM-Prop](https://www.nature.com/articles/s41524-025-01536-2)
            """)

demo.launch()