Restore GloVe to dropdown; files uploading to HF

app.py

@@ -167,7 +167,7 @@ embedding space.
 
 **Available models:**
 - **MOTES 100k** (default): Children's writing embeddings (ages 10–12) from the MOTES study
-- **GloVe 840B** (available for local use): General-purpose embeddings from Common Crawl (Pennington et al. 2014). Due to the 5.4 GB model size, GloVe is not hosted on this Space. For self-hosted deployments, download vectors from [Stanford NLP](https://nlp.stanford.edu/projects/glove/) and see [massivetexts/glove-840b-gensim](https://huggingface.co/massivetexts/glove-840b-gensim) for Gensim conversion instructions.
+- **GloVe 840B**: General-purpose embeddings trained on 840B Common Crawl tokens (Pennington et al. 2014). Large vocabulary, good for adult responses. English only.
 
 **Options:**
 - **Stopword filtering**: Skip common functional words (the, and, etc.)
@@ -214,8 +214,8 @@ with gr.Blocks(title="OCS Semantic Scoring", theme=OCS_THEME) as demo:
         "For higher accuracy, try [Ocsai LLM scoring](https://openscoring.du.edu/ocsai)."
     )
 
-    # Only show hosted models in the dropdown
-    model_choices = [(MODELS[k]["description"], k) for k in MODELS if MODELS[k].get("hosted", True)]
+    # Model choices for dropdowns
+    model_choices = [(MODELS[k]["description"], k) for k in MODELS]
 
     # Load model controls
     with gr.Row():

scoring.py

@@ -32,9 +32,9 @@ MODELS = {
         "repo": "massivetexts/glove-840b-gensim",
         "files": ["glove.840B-300d.wv", "glove.840B-300d.wv.vectors.npy"],
         "main_file": "glove.840B-300d.wv",
-        "description": "GloVe 840B 300d — self-hosted only (5.4 GB, see repo for setup)",
+        "description": "GloVe 840B 300d (Pennington et al. 2014) — general English vocabulary",
         "scaling": {"min": 0.6456, "max": 0.9610},
-        "hosted": False,  # model files not uploaded; too large for HF Space
+        "hosted": True,
     },
 }