update leaderbord, readme, longer traj

.github/README.md

@@ -1,13 +1,14 @@
 <div align="center">
     <h1>MLIP Arena</h1>
     <a href="https://zenodo.org/doi/10.5281/zenodo.13704399"><img src="https://zenodo.org/badge/776930320.svg" alt="DOI"></a>
+    <a href="https://huggingface.co/spaces/atomind/mlip-arena"><img src="https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo-with-title.svg" style="height: 20px; background-color: white;" alt="Hugging Face"></a>
 </div>
 
 > [!CAUTION]
 > MLIP Arena is currently in pre-alpha. The results are not stable. Please intepret them with care. 
 
 > [!NOTE]
-> If you're interested in joining the effort, please reach out to Yuan at [cyrusyc@berkeley.edu](mailto:cyrusyc@berkeley.edu). See [project page](https://github.com/orgs/atomind-ai/projects/1) for some outstanding tasks. 
+> If you're interested in joining the effort, please reach out to Yuan at [cyrusyc@berkeley.edu](mailto:cyrusyc@berkeley.edu).
 
 MLIP Arena is an open-source platform for benchmarking machine learning interatomic potentials (MLIPs). The platform provides a unified interface for users to evaluate the performance of their models on a variety of tasks, including single-point density functional theory calculations and molecular dynamics simulations. The platform is designed to be extensible, allowing users to contribute new models, benchmarks, and training data to the platform.
 
@@ -15,6 +16,12 @@ MLIP Arena is an open-source platform for benchmarking machine learning interato
 
 MLIP Arena is now in pre-alpha. If you're interested in joining the effort, please reach out to Yuan at [cyrusyc@berkeley.edu](mailto:cyrusyc@berkeley.edu). See [project page](https://github.com/orgs/atomind-ai/projects/1) for some outstanding tasks. 
 
+### Development
+
+```
+streamlit run serva/app.py
+```
+
 ### Add new MLIP models
 
 If you have pretrained MLIP models that you would like to contribute to the MLIP Arena and show benchmark in real-time, there are two ways:

pyproject.toml

@@ -10,7 +10,7 @@ authors=[
 ]
 description=""
 readme="README.md"
-requires-python=">=3.8"
+requires-python=">=3.10"
 keywords=[
     "pytorch",
     "machine-learning-interatomic-potentials",

serve/leaderboard.py

@@ -104,12 +104,13 @@ for task in TASKS:
     #  Call the function from the imported module
     if hasattr(task_module, "render"):
         task_module.render()
-        st.page_link(
-            f"tasks/{TASKS[task]['task-page']}.py",
-            label="Task page",
-            icon=":material/link:",
-        )
         # if st.button(f"Go to task page"):
         #     st.switch_page(f"tasks/{TASKS[task]['task-page']}.py")
     else:
-        st.write("Results for the task are not available yet.")
+        st.write("Rank metrics are not available yet but the task has been implemented. Please see the following task page for more information.")
+    
+    st.page_link(
+        f"tasks/{TASKS[task]['task-page']}.py",
+        label="Task page",
+        icon=":material/link:",
+    )