Update app.py

app.py

@@ -50,26 +50,22 @@ st.markdown("### Hello, paper classifier!")
 ## Demo buttons and their callbacks
 
 
-def demo_cl_callback():
+def demo_immunology_callback():
     """
-    Use https://ai.facebook.com/blog/large-language-model-llama-meta-ai/ for demo
+    Use https://www.biorxiv.org/content/10.1101/2022.12.01.518788v1 for demo
     """
-    paper_title = (
-        "Introducing LLaMA: A foundational, 65-billion-parameter large language model"
-    )
-    paper_abstract = "Over the last year, large language models — natural language processing (NLP) systems with billions of parameters — have shown new capabilities to generate creative text, solve mathematical theorems, predict protein structures, answer reading comprehension questions, and more. They are one of the clearest cases of the substantial potential benefits AI can offer at scale to billions of people. Smaller models trained on more tokens — which are pieces of words — are easier to retrain and fine-tune for specific potential product use cases. We trained LLaMA 65B and LLaMA 33B on 1.4 trillion tokens. Our smallest model, LLaMA 7B, is trained on one trillion tokens. Like other large language models, LLaMA works by taking a sequence of words as an input and predicts a next word to recursively generate text. To train our model, we chose text from the 20 languages with the most speakers, focusing on those with Latin and Cyrillic alphabets."
+    paper_title = "Using TCR and BCR sequencing to unravel the role of T and B cells in abdominal aortic aneurysm"
+    paper_abstract = "Recent evidence suggests that AAA displays characteristics of an autoimmune disease and it gained increasing prominence that specific antigen-driven T cells in the aortic tissue may contribute to the initial immune response. We found no clonal expansion of TCRs or BCRs in elastase-induced AAA in mice."
     st.session_state["title"] = paper_title
     st.session_state["abstract"] = paper_abstract
 
 
-def demo_cv_callback():
+def demo_virology_callback():
     """
-    Use https://arxiv.org/abs/2010.11929 for demo
+    Use https://doi.org/10.1016/j.cell.2020.08.001 for demo
     """
-    paper_title = (
-        "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale"
-    )
-    paper_abstract = "While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train."
+    paper_title = "Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment"
+    paper_abstract = "Coronavirus disease 2019 (COVID-19) is a mild to moderate respiratory tract infection, however, a subset of patients progress to severe disease and respiratory failure. The mechanism of protective immunity in mild forms and the pathogenesis of severe COVID-19 associated with increased neutrophil counts and dysregulated immune responses remain unclear. In a dual-center, two-cohort study, we combined single-cell RNA-sequencing and single-cell proteomics of whole-blood and peripheral-blood mononuclear cells to determine changes in immune cell composition and activation in mild versus severe COVID-19 (242 samples from 109 individuals) over time. HLA-DRhiCD11chi inflammatory monocytes with an interferon-stimulated gene signature were elevated in mild COVID-19. Severe COVID-19 was marked by occurrence of neutrophil precursors, as evidence of emergency myelopoiesis, dysfunctional mature neutrophils, and HLA-DRlo monocytes. Our study provides detailed insights into the systemic immune response to SARS-CoV-2 infection and reveals profound alterations in the myeloid cell compartment associated with severe COVID-19."
     st.session_state["title"] = paper_title
     st.session_state["abstract"] = paper_abstract
 
@@ -85,9 +81,9 @@ def clear_callback():
 
 col1, col2, col3 = st.columns([1, 1, 1])
 with col1:
-    st.button("Demo: LLaMA paper", on_click=demo_cl_callback)
+    st.button("Demo: immunology", on_click=demo_immunology_callback)
 with col2:
-    st.button("Demo: ViT paper", on_click=demo_cv_callback)
+    st.button("Demo: virology", on_click=demo_virology_callback)
 with col3:
     st.button("Clear fields", on_click=clear_callback)