docs: add presentation deck resource links

README.md

@@ -15,6 +15,7 @@ python_version: "3.10"
 **Large-Scale Chest X-Ray Pathology Detection** — Deep Learning & Big Data Project
 
 [![CI](https://github.com/arudaev/chexvision/actions/workflows/ci.yml/badge.svg)](https://github.com/arudaev/chexvision/actions/workflows/ci.yml)
+[![Deck](https://img.shields.io/badge/Slides-GitHub%20Pages-0f766e?logo=githubpages)](https://arudaev.github.io/chexvision/)
 [![Dataset](https://img.shields.io/badge/HF-Dataset-blue?logo=huggingface)](https://huggingface.co/datasets/arudaev/chest-xray-14-320)
 [![Demo](https://img.shields.io/badge/HF-Demo-orange?logo=huggingface)](https://huggingface.co/spaces/arudaev/chexvision-demo)
 [![Python 3.10+](https://img.shields.io/badge/python-3.10%2B-blue.svg)](https://www.python.org/)
@@ -23,6 +24,16 @@ python_version: "3.10"
 
 ---
 
+## Project Resources
+
+- [Presentation deck](https://arudaev.github.io/chexvision/)
+- [Live demo](https://huggingface.co/spaces/arudaev/chexvision-demo)
+- [Dataset](https://huggingface.co/datasets/arudaev/chest-xray-14-320)
+- [Scratch model](https://huggingface.co/arudaev/chexvision-scratch)
+- [DenseNet model](https://huggingface.co/arudaev/chexvision-densenet)
+
+---
+
 ## Overview
 
 CheXVision tackles **automated chest X-ray pathology detection** at scale using the [NIH Chest X-ray14](https://www.nih.gov/news-events/news-releases/nih-clinical-center-provides-one-largest-publicly-available-chest-x-ray-datasets-scientific-community) dataset — 112,120 frontal-view X-ray images labeled with 14 pathological conditions.
@@ -288,6 +299,7 @@ All heavy compute runs in the cloud. Local machines are for editing and lightwei
 | Component | Platform | Notes |
 |-----------|----------|-------|
 | Source code | [GitHub](https://github.com/arudaev/chexvision) | CI on every push (lint, test, type check) |
+| Presentation deck | [GitHub Pages](https://arudaev.github.io/chexvision/) | Browser-based project presentation |
 | Dataset | [HF Dataset](https://huggingface.co/datasets/arudaev/chest-xray-14-320) | 36 Parquet shards · 320×320 · pinned revision |
 | Training | [Kaggle kernels](kaggle/) | Free T4 GPU; `dispatch.py` fully automates push |
 | Model (scratch) | [arudaev/chexvision-scratch](https://huggingface.co/arudaev/chexvision-scratch) | Auto-uploaded by kernel after training |

app/app.py

@@ -36,6 +36,7 @@
 # HF Hub model repos
 HF_SCRATCH_REPO = "arudaev/chexvision-scratch"
 HF_DENSENET_REPO = "arudaev/chexvision-densenet"
+PRESENTATION_DECK_URL = "https://arudaev.github.io/chexvision/"
 
 
 def _try_load_checkpoint(
@@ -218,7 +219,7 @@ def main() -> None:
     st.markdown("**Large-Scale Chest X-Ray Pathology Detection** — Upload a chest X-ray for AI-powered analysis.")
 
     st.sidebar.header("About")
-    st.sidebar.markdown("""
+    st.sidebar.markdown(f"""
     **Dataset**: NIH Chest X-ray14 (112,120 images)
 
     **Two Models**:
@@ -229,8 +230,9 @@ def main() -> None:
     - Multi-label: 14 pathologies
     - Binary: Normal vs Abnormal
 
+    [Presentation Deck]({PRESENTATION_DECK_URL}) |
     [GitHub](https://github.com/arudaev/chexvision) |
-    [Dataset](https://huggingface.co/datasets/arudaev/chest-xray-14)
+    [Dataset](https://huggingface.co/datasets/arudaev/chest-xray-14-320)
     """)
     sidebar_selected_sample = render_sidebar_samples()
 

src/utils/hub.py

@@ -15,6 +15,9 @@
     "CHEXVISION_DATASET_REVISION",
     "44443e6ee968b3c6094b63f14a27698c40b50680",
 )
+PROJECT_REPO_URL = "https://github.com/arudaev/chexvision"
+PRESENTATION_DECK_URL = "https://arudaev.github.io/chexvision/"
+DEMO_SPACE_URL = "https://huggingface.co/spaces/arudaev/chexvision-demo"
 
 # NIH Chest X-ray14 pathology labels in canonical order
 PATHOLOGY_LABELS = [
@@ -401,6 +404,13 @@ def render_model_card(
 > 14-class chest X-ray pathology detection + binary normal/abnormal classification
 > on the NIH Chest X-ray14 dataset (112,120 images).
 
+## Project Resources
+
+- [GitHub repository]({PROJECT_REPO_URL})
+- [Presentation deck]({PRESENTATION_DECK_URL})
+- [Live demo]({DEMO_SPACE_URL})
+- [Dataset](https://huggingface.co/datasets/{dataset_repo})
+
 ## Architecture
 
 {arch_diagram}
@@ -442,7 +452,8 @@ def render_model_card(
 CheXNet (Rajpurkar et al., 2017) — the seminal paper establishing DenseNet-121 for chest X-ray
 classification — reported **0.841 macro AUC-ROC** on a comparable split of this dataset.
 CheXVision-DenseNet matches this benchmark. See the
-[CheXVision demo](https://huggingface.co/spaces/arudaev/chexvision-demo) for live inference.
+[CheXVision demo]({DEMO_SPACE_URL}) for live inference, or the
+[presentation deck]({PRESENTATION_DECK_URL}) for the project walkthrough.
 
 ## Citation