restructure

0219_gradio/README.md

@@ -1,41 +0,0 @@
-# PhySH Taxonomy Classifier — Gradio App
-
-Interactive web app that predicts APS PhySH **disciplines** and **research-area concepts**
-for a given paper title + abstract.
-
-## How it works
-
-1. Text is embedded with `google/embeddinggemma-300m` (768-dim, L2-normalised).
-2. **Stage 1** — A multi-label MLP predicts discipline probabilities (18 classes).
-3. **Stage 2** — A discipline-conditioned MLP concatenates the embedding with discipline
-   probabilities and predicts research-area concepts (186 classes).
-
-Both models are `.pt` checkpoints trained in `../0120_taxonomy_training_inference/`.
-
-## Setup
-
-The app uses the project-level virtualenv (`.venv` at the repo root).
-
-```bash
-# From the repo root
-source .venv/bin/activate
-
-# Install the one extra dependency
-pip install gradio
-```
-
-## Run
-
-```bash
-cd 0219_gradio
-python app.py
-```
-
-Then open `http://127.0.0.1:7860` in your browser.
-
-## Model files
-
-The app expects these checkpoints in the same directory as `app.py`:
-
-- `discipline_classifier_gemma_20260130_140842.pt`
-- `concept_conditioned_gemma_20260130_140842.pt`

README.md

@@ -1,12 +1,41 @@
----
-title: Physics Topic Labeling
-emoji: 🏢
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 6.6.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# PhySH Taxonomy Classifier — Gradio App
+
+Interactive web app that predicts APS PhySH **disciplines** and **research-area concepts**
+for a given paper title + abstract.
+
+## How it works
+
+1. Text is embedded with `google/embeddinggemma-300m` (768-dim, L2-normalised).
+2. **Stage 1** — A multi-label MLP predicts discipline probabilities (18 classes).
+3. **Stage 2** — A discipline-conditioned MLP concatenates the embedding with discipline
+   probabilities and predicts research-area concepts (186 classes).
+
+Both models are `.pt` checkpoints trained in `../0120_taxonomy_training_inference/`.
+
+## Setup
+
+The app uses the project-level virtualenv (`.venv` at the repo root).
+
+```bash
+# From the repo root
+source .venv/bin/activate
+
+# Install the one extra dependency
+pip install gradio
+```
+
+## Run
+
+```bash
+cd 0219_gradio
+python app.py
+```
+
+Then open `http://127.0.0.1:7860` in your browser.
+
+## Model files
+
+The app expects these checkpoints in the same directory as `app.py`:
+
+- `discipline_classifier_gemma_20260130_140842.pt`
+- `concept_conditioned_gemma_20260130_140842.pt`