README.md

metadata

title: XDL Colitis Demo
emoji: 👁
colorFrom: pink
colorTo: red
sdk: gradio
sdk_version: 6.5.1
app_file: app.py
pinned: false

XDL Colitis Demo

Simple Gradio demo for DenseNet-based colitis image classification with optional XDL overlays.

Features

• Batch inference only
• Case selector:
  • Multiclass (4 Classes)
  • Crohn vs TB
  • Kolitis Ulseratif vs Infeksi
• Folder upload support (gr.File directory upload)
• Optional local folder-path input
• Automatic device detection (cuda if available, else cpu)
• Presentable summary card for key outputs (device, processed count, final class, mean confidence, etc.)
• Final folder classification by majority vote of per-image top class
• Mean confidence for the selected final class
• Compact GradCAM + SmoothGrad overlays for classified images
• Optional saving of compact XDL results to a local folder

Run

.venv/bin/python app.py

Then open the Gradio URL in your browser.

Project Structure

• app.py: minimal launcher
• src/ui.py: batch-only Gradio UI
• src/inference.py: model loading + inference + batch/XDL pipeline
• src/xdl.py: DenseNet-specific XDL helper functions
• src/densenet.py: model definition
• src/preprocessing.py: inference transform

Notes

• Default model paths:
  • models/multiclass.pth (Multiclass)
  • models/crohn_tb.pth (Crohn vs TB)
  • models/ku_infeksi.pth (Kolitis Ulseratif vs Infeksi)
• You can override model paths with env vars:
  • MODEL_PATH_MULTICLASS
  • MODEL_PATH_CROHN_TB
  • MODEL_PATH_KU_INFEKSI
• If model loading fails, the UI returns a clear error instead of silently using random weights.