streamlit community installation fix; update readme

.devcontainer/devcontainer.json

@@ -1,33 +1,3 @@
-{
-  "name": "Python 3",
-  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
-  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
-  "customizations": {
-    "codespaces": {
-      "openFiles": [
-        "README.md",
-        "serve/app.py"
-      ]
-    },
-    "vscode": {
-      "settings": {},
-      "extensions": [
-        "ms-python.python",
-        "ms-python.vscode-pylance"
-      ]
-    }
-  },
-  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
-  "postAttachCommand": {
-    "server": "streamlit run serve/app.py --server.enableCORS false --server.enableXsrfProtection false"
-  },
-  "portsAttributes": {
-    "8501": {
-      "label": "Application",
-      "onAutoForward": "openPreview"
-    }
-  },
-  "forwardPorts": [
-    8501
-  ]
-}
+version https://git-lfs.github.com/spec/v1
+oid sha256:a525cdb835f1b6c36c5d09b1663e2dc0b2e5a40b97214fc9ee2fc0366b9df622
+size 986

.github/README.md

@@ -19,13 +19,26 @@ MLIP Arena is now in pre-alpha. If you're interested in joining the effort, plea
 ### Development
 
 ```
-streamlit run serva/app.py
+streamlit run serve/app.py
 ```
 
+### Add new benchmark tasks
+
+1. Follow the task template to implement the task class and upload the script along with metadata to the MLIP Arena [here](../mlip_arena/tasks/README.md).
+2. Code a benchmark script to evaluate the performance of your model on the task. The script should be able to load the model and the dataset, and output the evaluation metrics.
+
 ### 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:
 
+#### External ASE Calculator (easy)
+
+1. Implement new ASE Calculator class in [mlip_arena/models/external.py](../mlip_arena/models/externals.py). 
+2. Name your class with awesome model name and add the same name to [registry](../mlip_arena/models/registry.yaml) with metadata.
+
+> [!CAUTION] 
+> Remove unneccessary outputs under `results` class attributes to avoid error for MD simulations. Please refer to other class definition for example.
+
 #### Hugging Face Model (recommended, difficult)
 
 0. Inherit Hugging Face [ModelHubMixin](https://huggingface.co/docs/huggingface_hub/en/package_reference/mixins) class to your awesome model class definition. We recommend [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/en/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin).
@@ -36,36 +49,22 @@ If you have pretrained MLIP models that you would like to contribute to the MLIP
 > [!NOTE] 
 > CPU benchmarking will be performed automatically. Due to the limited amount GPU compute, if you would like to be considered for GPU benchmarking, please create a pull request to demonstrate the offline performance of your model (published paper or preprint). We will review and select the models to be benchmarked on GPU.
 
-#### External ASE Calculator (easy)
 
-1. Implement new ASE Calculator class in [mlip_arena/models/external.py](../mlip_arena/models/externals.py). 
-2. Name your class with awesome model name and add the same name to [registry](../mlip_arena/models/registry.yaml) with metadata.
-
-> [!CAUTION] 
-> Remove unneccessary outputs under `results` class attributes to avoid error for MD simulations. Please refer to other class definition for example.
-
-### Add new benchmark tasks
-
-1. Follow the task template to implement the task class and upload the script along with metadata to the MLIP Arena [here](../mlip_arena/tasks/README.md).
-2. Code a benchmark script to evaluate the performance of your model on the task. The script should be able to load the model and the dataset, and output the evaluation metrics.
 
 ### Add new datasets
 
-1. Create a new [Hugging Face Dataset](https://huggingface.co/new-dataset) repository and upload the reference data (e.g. DFT, AIMD, experimental measurements such as RDF).
+The goal is to compile and keep the copy of all the open source data in a unified format for lifelong learning with [Hugging Face Auto-Train](https://huggingface.co/docs/hub/webhooks-guide-auto-retrain). 
 
+1. Create a new [Hugging Face Dataset](https://huggingface.co/new-dataset) repository and upload the reference data (e.g. DFT, AIMD, experimental measurements such as RDF).
 
 #### Single-point density functional theory calculations
 
 - [ ] MPTrj
 - [ ] [Alexandria](https://huggingface.co/datasets/atomind/alexandria)
 - [ ] QM9
+- [ ] SPICE
 
 #### Molecular dynamics calculations
 
 - [ ] [MD17](http://www.sgdml.org/#datasets)
 - [ ] [MD22](http://www.sgdml.org/#datasets)
-
-
-### [Hugging Face Auto-Train](https://huggingface.co/docs/hub/webhooks-guide-auto-retrain)
-
-Planned but not yet impelemented.