Upload app.py

app.py

@@ -75,14 +75,16 @@ dataset_names = [
 title = "Predict Drug-Target Interactions with <span style='font-variant:small-caps;'>BarlowDTI</span>"
 
 description = """
-Input Amino Acid Sequence and SMILES to get interaction predictions visualized as a spider graph and in a table. 
-The values ca be interpreted as the probability of interaction between the drug and target (0 = no interaction, 1 = interaction).
+Enter the amino acid sequence and SMILES to get interaction predictions visualized as a spider graph and in a table. 
+The values can be interpreted as the probability of interaction between the drug and the target (0 = no interaction, 1 = interaction).
 
-__Note: Inference may take a loger time, you can upgrade to a paid GPU-enabled plan for faster inference.__
+Thank you for using <span style='font-variant:small-caps;'>BarlowDTI</span>!
+
+Note: Inference may take longer, you can upgrade to a paid GPU-enabled plan for faster inference.
 """
 
 article = """
-This interface enables the use of <span style='font-variant:small-caps;'>BarlowDTI</span><sub>XXL</sub> to predict drug-target interactions. 
+This interface lets the scientific community use <span style='font-variant:small-caps;'>BarlowDTI</span><sub>XXL</sub> to predict drug-target interactions. 
 The model ensemble consists of four models trained on different datasets: our own curated and refined dataset based on 
 [Golts et. al](https://doi.org/10.48550/arXiv.2401.17174) 
 in combination with 
@@ -90,7 +92,7 @@ in combination with
 [BioSNAP](https://snap.stanford.edu/index.html), and 
 [DAVIS](https://doi.org/10.1038/nbt.1990).
 
-If you use this interface in your research, please cite our paper:
+If you use our approach in your research, please cite our paper:
 ```
 @misc{schuh2024barlowtwinsdeepneural,
       title={Barlow Twins Deep Neural Network for Advanced 1D Drug-Target Interaction Prediction},