Fix Space README metadata

README.md

@@ -1,74 +1,18 @@
-# Microbiome Transformer (Set-Based OTU Stability Model)
-
-This repository provides Transformer checkpoints for microbiome set modeling using SSU rRNA OTU embeddings (ProkBERT-derived vectors) and optional text metadata embeddings.
-
-Please see https://github.com/the-puzzler/microbiome-model for more information and relevant code.
-
-## Model summary
-
-- **Architecture:** `MicrobiomeTransformer` (see `model.py`)
-- **Input type 1 (DNA/OTU):** 384-d embeddings
-- **Input type 2 (text metadata):** 1536-d embeddings
-- **Core behavior:** permutation-invariant set encoding via Transformer encoder (no positional encodings)
-- **Output:** per-token scalar logits (used as stability scores)
-
-## Available checkpoints
-
-| Filename | Size variant | Metadata variant | `d_model` | `num_layers` | `dim_feedforward` | `nhead` |
-|---|---|---|---:|---:|---:|---:|
-| `small-notext.pt` | small | DNA-only | 20 | 3 | 80 | 5 |
-| `small-text.pt` | small | DNA + text | 20 | 3 | 80 | 5 |
-| `large-notext.pt` | large | DNA-only | 100 | 5 | 400 | 5 |
-| `large-text.pt` | large | DNA + text | 100 | 5 | 400 | 5 |
-
-Shared dimensions:
-- `OTU_EMB = 384`
-- `TXT_EMB = 1536`
-- `DROPOUT = 0.1`
-
-## Input expectations
-
-1. Build a set of OTU embeddings (ProkBERT vectors) per sample.
-2. Optionally build a set of text embeddings (metadata) per sample for text-enabled variants.
-3. Feed both sets as:
-   - `embeddings_type1`: shape `(B, N_otu, 384)`
-   - `embeddings_type2`: shape `(B, N_txt, 1536)`
-   - `mask`: shape `(B, N_otu + N_txt)` with valid positions as `True`
-   - `type_indicators`: shape `(B, N_otu + N_txt)` (0 for OTU tokens, 1 for text tokens)
-
-## Minimal loading example
-
-```python
-import torch
-from model import MicrobiomeTransformer
-
-ckpt_path = "large-notext.pt"  # or small-notext.pt / small-text.pt / large-text.pt
-checkpoint = torch.load(ckpt_path, map_location="cpu")
-state_dict = checkpoint.get("model_state_dict", checkpoint)
-
-is_small = "small" in ckpt_path
-model = MicrobiomeTransformer(
-    input_dim_type1=384,
-    input_dim_type2=1536,
-    d_model=20 if is_small else 100,
-    nhead=5,
-    num_layers=3 if is_small else 5,
-    dim_feedforward=80 if is_small else 400,
-    dropout=0.1,
-)
-model.load_state_dict(state_dict, strict=False)
-model.eval()
-```
-
-## Intended use
-
-- Microbiome representation learning from OTU sets
-- Stability-style scoring of community members
-- Downstream analyses such as dropout/colonization prediction and rollout trajectory experiments
-
-## Limitations
-
-- This is a research model and not a clinical diagnostic tool.
-- Outputs depend strongly on upstream OTU mapping, embedding resolution, and cohort preprocessing.
-- Text-enabled checkpoints expect compatible metadata embedding pipelines.
-
+---
+title: Microbiome Space
+emoji: 🧬
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: "5.0.0"
+python_version: "3.10"
+app_file: app.py
+pinned: false
+---
+
+# Microbiome Gene Scoring Explorer
+
+Upload a FASTA of genes, embed with `prokbert-mini-long` (mean pooling), score with `large-notext`, and inspect:
+- UMAP of input embeddings
+- UMAP of final embeddings
+- Logit distribution