huggingface management

.env.example

@@ -16,3 +16,9 @@ ICH_LOCAL_MODE=1
 # Logging level
 # Values: DEBUG | INFO | WARNING | ERROR
 ICH_LOG_LEVEL=INFO
+
+# Hugging Face publishing
+# Create token: https://huggingface.co/settings/tokens
+HF_TOKEN=
+# Example: your-username/ich-b4-model
+HF_REPO_ID=

.github/workflows/pages.yml

@@ -0,0 +1,50 @@
+name: Deploy GitHub Pages
+
+on:
+  push:
+    branches: [ "main" ]
+    paths:
+      - "docs/**"
+      - ".github/workflows/pages.yml"
+  workflow_dispatch:
+
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+concurrency:
+  group: "pages"
+  cancel-in-progress: true
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Configure Pages
+        uses: actions/configure-pages@v5
+
+      - name: Build with Jekyll
+        uses: actions/jekyll-build-pages@v1
+        with:
+          source: ./docs
+          destination: ./_site
+
+      - name: Upload Pages Artifact
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: ./_site
+
+  deploy:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    needs: build
+    steps:
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4

.gitignore

@@ -35,10 +35,19 @@ logs/
 .coverage.*
 htmlcov/
 coverage.xml
+upload_to_huggingface.py
+
+# Generated inference artifacts (keep source code, ignore heavy binaries)
+download_imp/outputs/
+download_imp/dicom_inputs/
+download_imp/*.pth
+download_imp/*.pt
+download_imp/*.pkl
+download_imp/*.onnx
 
-# Generated inference artifacts
 download_imp/*
-download
+# Local downloaded artifacts
+download/
 # Local downloaded data
 data/
 datasets/

README.md

@@ -70,6 +70,36 @@ Model checkpoints and heavy binary artifacts are intentionally ignored for GitHu
 
 This keeps the repository lightweight and reproducible while allowing local/model-private inference.
 
+### Publish Model To Hugging Face
+
+1. Create a token with `Write` access: `https://huggingface.co/settings/tokens`
+1. Set credentials in `.env` (or export in shell):
+
+  ```bash
+  HF_TOKEN=your_hf_token
+  HF_REPO_ID=your-username/ich-b4-model
+  ```
+
+1. Upload model artifacts:
+
+  ```bash
+  python scripts/upload_to_huggingface.py --repo-id "$HF_REPO_ID" --private
+  ```
+
+Published model repository:
+
+- [Hugging Face Model Repo](https://huggingface.co/HarshCode/eff_b4_brain)
+
+### Host On GitHub Pages
+
+1. Ensure your default branch is `main`.
+1. Push this repository with `.github/workflows/pages.yml` and `docs/`.
+1. In GitHub repository settings:
+   1. Open `Settings -> Pages`
+   1. Set source to `GitHub Actions`
+
+The site will be published automatically after push.
+
 ## AI-Assisted CT-Based Intracranial Hemorrhage Detection with Explainability and Clinical Reporting
 
 ---

docs/index.md

@@ -0,0 +1,20 @@
+# ICH Screening Project
+
+AI-assisted intracranial hemorrhage screening from head CT with:
+
+- EfficientNet-B4 based inference
+- 2.5D context pipeline
+- Calibration-aware triage outputs
+- Flask web interface for upload, reports, logs, and evaluation
+
+## Repository
+
+Source code and app: see the main project README in this repository.
+
+## Model Artifacts
+
+Model artifacts are published separately on Hugging Face (recommended) to keep this GitHub repository lightweight.
+
+## Disclaimer
+
+This project is a screening and decision-support tool. It is not a standalone diagnostic device.

requirements.txt

@@ -12,5 +12,6 @@ scikit-learn
 
 blackbox-recorder
 python-dotenv
+huggingface_hub