Update README.md

README.md

@@ -15,14 +15,35 @@ tags:
 - therapeutics
 library_name: tdc
 license: bsd-2-clause
+datasets:
+- scvi-tools/DATASET-FOR-UNIT-TESTING-1
 ---
-COMING SOON
-weights extracted from https://cellxgene.cziscience.com/census-models
 
-## Model description
+# scVI
 Single-cell variational inference (scVI) is a powerful tool for the probabilistic analysis of single-cell transcriptomics data. It uses deep generative models to address technical noise and batch effects, providing a robust framework for various downstream analysis tasks. 
 To load the pre-trained model, use the Files and Versions tab files.
 
+# Code
+
+```python
+from tdc.multi_pred.anndata_dataset import DataLoader
+from tdc import tdc_hf_interface
+
+adata = DataLoader("scvi_test_dataset",
+                   "./data",
+                   dataset_names=["scvi_test_dataset"],
+                   no_convert=True).adata
+
+scvi = tdc_hf_interface("scVI")
+model = scvi.load()
+output = model(adata)
+```
+
+# TDC.scVI Source Code
+* https://github.com/apliko-xyz/PyTDC/blob/main/tdc/model_server/models/scvi.py
+* weights extracted from https://cellxgene.cziscience.com/census-models
+
+
 ## References
 * Lopez, R., Regier, J., Cole, M., Jordan, M. I., & Yosef, N. (2018). Deep Generative Modeling for Single-cell Transcriptomics. Nature Methods, 15, 1053-1058.
 * Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., Liu, Y., Samaran, J., Misrachi, G., Nazaret, A., Clivio, O., Xu, C. A., Ashuach, T., Lotfollahi, M., Svensson, V., Beltrame, E., Talavera-López, C., ... Yosef, N. (2021). scvi-tools: a library for deep probabilistic analysis of single-cell omics data. bioRxiv.