name and path fixes

app.py

@@ -7,19 +7,21 @@ import os
 from model import FFNModule, FeatureNormPredictor, FFNParams, TrainingParams
 
 
-def predict (Word, Sentence, llm, norm, layer):
+def predict (Word, Sentence, LLM, Norm, Layer):
     models = {'BERT': 'bert-base-uncased',
               'ALBERT': 'albert-xxlarge-v2',
               'roBERTa': 'roberta-base'}
     if Word not in Sentence: return "invalid input: word not in sentence"
-    model_name_hf = llm.lower()
-    norm_name_hf = norm.lower()
-    lm = cwe.CWE(models[llm])
+    model_name_hf = LLM.lower()
+    norm_name_hf = Norm.lower()
+    lm = cwe.CWE(models[LLM])
 
-    full_name_hf = f"jwalanthi/semantic-feature-classifiers/{model_name_hf}_models_all/{model_name_hf}_to_{norm_name_hf}_layer{layer}"
+    repo_id = "jwalanthi/semantic-feature-classifiers"
+    subfolder = "{model_name_hf}_models_all"
+    name_hf = f"{model_name_hf}_to_{norm_name_hf}_layer{Layer}"
 
-    model_path = hf_hub_download(f"{full_name_hf}.ckpt", use_auth_token=os.environ['TOKEN'])
-    label_path = hf_hub_download(f"{full_name_hf}.txt", use_auth_token=os.environ['TOKEN'])
+    model_path = hf_hub_download(repo_id = repo_id, subfolder=subfolder, filename=f"{name_hf}.ckpt", use_auth_token=os.environ['TOKEN'])
+    label_path = hf_hub_download(repo_id = repo_id, subfolder=subfolder, filename=f"{name_hf}.txt", use_auth_token=os.environ['TOKEN'])
 
     model = FeatureNormPredictor.load_from_checkpoint(
         checkpoint_path=model_path,