adding link for verified model

app.py

@@ -5,7 +5,6 @@ import numpy as np
 
 import gradio as gr
 from datasets import load_dataset
-from gradio_leaderboard import Leaderboard
 from datetime import datetime
 import os
 
@@ -65,6 +64,11 @@ def format_dataframe(df, show_percentage=False, selected_groups=None, compact_vi
 
     # Add symbols to model names based on various properties
     if 'model_name' in display_df.columns:
+        # Model links mapping
+        model_links = {
+            'CrystaLLM-pi': 'https://huggingface.co/c-bone/CrystaLLM-pi_base',
+        }
+
         def add_model_symbols(row):
             name = row['model_name']
             symbols = []
@@ -83,7 +87,12 @@ def format_dataframe(df, show_percentage=False, selected_groups=None, compact_vi
             elif name == 'CrystaLLM-pi':
                 symbols.append('✅')
 
-            return f"{name} {' '.join(symbols)}" if symbols else name
+            symbol_str = f" {' '.join(symbols)}" if symbols else ""
+
+            # Add link if model has one
+            if name in model_links:
+                return f'<a href="{model_links[name]}" target="_blank">{name}</a>{symbol_str}'
+            return f"{name}{symbol_str}"
 
         display_df['model_name'] = df.apply(add_model_symbols, axis=1)
 
@@ -267,7 +276,7 @@ def gradio_interface() -> gr.Blocks:
 
 Generative machine learning models hold great promise for accelerating materials discovery, particularly through the inverse design of inorganic crystals, enabling an unprecedented exploration of chemical space. Yet, the lack of standardized evaluation frameworks makes it difficult to evaluate, compare and further develop these ML models meaningfully.
 
-**LeMat-GenBench** introduces a unified benchmark for generative models of crystalline materials, with standardized evaluation metrics** for meaningful model comparison, diverse tasks, and this leaderboard to encourage and track community progress.
+**LeMat-GenBench** introduces a unified benchmark for generative models of crystalline materials, with standardized evaluation metrics for meaningful model comparison, diverse tasks, and this leaderboard to encourage and track community progress.
 
 📄 **Paper**: [arXiv](https://arxiv.org/abs/2512.04562) | 💻 **Code**: [GitHub](https://github.com/LeMaterial/lemat-genbench) | 📧 **Contact**: siddharth.betala-ext [at] entalpic.ai, alexandre.duval [at] entalpic.ai
 """)
@@ -327,6 +336,7 @@ Generative machine learning models hold great promise for accelerating materials
                         value=formatted_df,
                         interactive=False,
                         wrap=True,
+                        datatype=["html"] + [None] * (len(formatted_df.columns) - 1) if len(formatted_df.columns) > 0 else None,
                         column_widths=["180px"] + ["160px"] * (len(formatted_df.columns) - 1) if len(formatted_df.columns) > 0 else None,
                         show_fullscreen_button=True
                     )