roll back

app.py

@@ -56,7 +56,7 @@ with header_col2:
     st.markdown("### Select a Model")
     selected_model = st.selectbox(
         "",
-        options=["Pangu-Weather", "FengWu", "Aurora", "Climax", "Prithvi", "LSTM"],
+        options=["Pangu-Weather", "FengWu", "Aurora", "Climax", "Prithvi", "GEOS-Specific-LSTM", "GEOS-Finetuned-Climax"],
         index=0,
         key="model_selector",
         help="Select the model you want to use."
@@ -74,6 +74,15 @@ with left_col:
     if selected_model == "Prithvi":
         (config, uploaded_surface_files, uploaded_vertical_files, 
          clim_surf_path, clim_vert_path, config_path, weights_path) = prithvi_config_ui()
+    elif selected_model == "Climax":
+        st.info("Climax model is not yet available.")
+        st.stop()
+    elif selected_model == "GEOS-Specific-LSTM":
+        st.info("GEOS-Specific-LSTM model is not yet available.")
+        st.stop()
+    elif selected_model == "GEOS-Finetuned-Climax":
+        st.info("GEOS-Finetuned-Climax model is not yet available.")
+        st.stop()
     elif selected_model == "Aurora":
         uploaded_files = aurora_config_ui()
     elif selected_model == "Pangu-Weather":

aurora_utils.py

@@ -5,14 +5,79 @@ import numpy as np
 from datetime import datetime
 
 def aurora_config_ui():
-    st.subheader("Aurora Model Data Upload")
-    st.markdown("### Drag and Drop Your Data Files Here")
+    st.subheader("Aurora Model Data Input")
+
+    # Detailed data description section
+    st.markdown("""
+    **Available Models & Usage:**
+
+    Aurora provides several pretrained and fine-tuned models at 0.25° and 0.1° resolutions.  
+    Models and weights are available through the HuggingFace repository: [microsoft/aurora](https://huggingface.co/microsoft/aurora).
+
+    **Aurora 0.25° Pretrained**  
+    - Trained on a variety of data.  
+    - Suitable if no fine-tuned version exists for your dataset or to fine-tune Aurora yourself.
+    - Use if your dataset is ERA5 at 0.25° resolution (721x1440).  
+
+    **Aurora 0.25° Pretrained Small**  
+    - A smaller version of the pretrained model for debugging purposes.
+
+    **Aurora 0.25° Fine-Tuned**  
+    - Fine-tuned on IFS HRES T0.  
+    - Best performance at 0.25° but should only be used for IFS HRES T0 data.  
+    - May not give optimal results for other datasets.
+
+    **Aurora 0.1° Fine-Tuned**  
+    - For IFS HRES T0 at 0.1° resolution (1801x3600).  
+    - Best performing at 0.1° resolution.  
+    - Data must match IFS HRES T0 conditions.
+
+    **Required Variables & Pressure Levels:**
+
+    For all Aurora models at these resolutions, the following inputs are required:
+
+    - **Surface-level variables:** 2t, 10u, 10v, msl  
+    - **Static variables:** lsm, slt, z  
+    - **Atmospheric variables:** t, u, v, q, z  
+    - **Pressure levels (hPa):** 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000  
+
+    Latitude range should decrease from 90°N to -90°S, and longitude range from 0° to 360° (excluding 360°). Data should be in single precision float32.
+
+
+    **Data Format (Batch):**
+    Data should be provided as a `aurora.Batch` object:
+    - `surf_vars` dict with shape (b, t, h, w)
+    - `static_vars` dict with shape (h, w)
+    - `atmos_vars` dict with shape (b, t, c, h, w)
+    - `metadata` containing lat, lon, time, and atmos_levels.
+
+    For detailed instructions and examples, refer to the official Aurora documentation and code repository.
+    """)
+
+    # File uploader for Aurora data
+    st.markdown("### Upload Your Input Data Files for Aurora")
+    st.markdown("Upload the NetCDF files (e.g., `.nc`, `.netcdf`, `.nc4`) containing the required variables.")
     uploaded_files = st.file_uploader(
-        "Upload Data Files for Aurora",
+        "Drag and drop or select multiple .nc files",
         accept_multiple_files=True,
         key="aurora_uploader",
         type=["nc", "netcdf", "nc4"]
     )
+
+    st.markdown("---")
+    st.markdown("### References & Resources")
+    st.markdown("""
+    - **HuggingFace Repository:** [microsoft/aurora](https://huggingface.co/microsoft/aurora)
+    - **Model Usage Examples:**  
+      ```python
+      from aurora import Aurora
+
+      model = Aurora()
+      model.load_checkpoint("microsoft/aurora", "aurora-0.25-pretrained.ckpt")
+      ```
+    - **API & Documentation:** Refer to the Aurora official GitHub and HuggingFace pages for detailed instructions.
+    """)
+
     return uploaded_files
 
 def prepare_aurora_batch(ds):