Read Donut Files Properly

app.py

@@ -41,15 +41,21 @@ def config_style():
     st.markdown('<h1 class="main-title">Merit Embeddings 🎒📃🏆</h1>', unsafe_allow_html=True)
 
 def load_embeddings(model, version, embedding_prefix):
+
+    if embedding_prefix == "averaged":
+        weight = ""
+    elif embedding_prefix == "weighted":
+        weight = "0.5_"
+
     if model == "Donut":
-        df_real = pd.read_csv(f"data/donut_{version}_de_Rodrigo_merit_secret_all_{embedding_prefix}embeddings.csv")
+        df_real = pd.read_csv(f"data/{model.lower()}/{version}/{embedding_prefix}/de_Rodrigo_merit_secret_all_{weight}embeddings.csv")
         df_par = pd.read_csv(f"data/donut_{version}_de_Rodrigo_merit_es-digital-paragraph-degradation-seq_{embedding_prefix}embeddings.csv")
-        df_line = pd.read_csv(f"data/donut_{version}_de_Rodrigo_merit_es-digital-line-degradation-seq_{embedding_prefix}embeddings.csv")
-        df_seq  = pd.read_csv(f"data/donut_{version}_de_Rodrigo_merit_es-digital-seq_{embedding_prefix}embeddings.csv")
-        df_rot  = pd.read_csv(f"data/donut_{version}_de_Rodrigo_merit_es-digital-rotation-degradation-seq_{embedding_prefix}embeddings.csv")
-        df_zoom = pd.read_csv(f"data/donut_{version}_de_Rodrigo_merit_es-digital-zoom-degradation-seq_{embedding_prefix}embeddings.csv")
-        df_render = pd.read_csv(f"data/donut_{version}_de_Rodrigo_merit_es-render-seq_{embedding_prefix}embeddings.csv")
-        df_pretratrained = pd.read_csv(f"data/donut_{version}_de_Rodrigo_merit_aux_IIT-CDIP_{embedding_prefix}embeddings.csv")
+        df_line = pd.read_csv(f"data/{model.lower()}/{version}/{embedding_prefix}/de_Rodrigo_merit_es-digital-line-degradation-seq_{weight}embeddings.csv")
+        df_seq  = pd.read_csv(f"data/{model.lower()}/{version}/{embedding_prefix}/de_Rodrigo_merit_es-digital-seq_{weight}embeddings.csv")
+        df_rot  = pd.read_csv(f"data/{model.lower()}/{version}/{embedding_prefix}/de_Rodrigo_merit_es-digital-rotation-degradation-seq_{weight}embeddings.csv")
+        df_zoom = pd.read_csv(f"data/{model.lower()}/{version}/{embedding_prefix}/de_Rodrigo_merit_es-digital-zoom-degradation-seq_{weight}embeddings.csv")
+        df_render = pd.read_csv(f"data/{model.lower()}/{version}/{embedding_prefix}/de_Rodrigo_merit_es-render-seq_{weight}embeddings.csv")
+        df_pretratrained = pd.read_csv(f"data/{model.lower()}/{version}/{embedding_prefix}/de_Rodrigo_merit_aux_IIT-CDIP_{weight}embeddings.csv")
         
         # Asignar etiquetas de versión
         df_real["version"] = "real"
@@ -540,9 +546,9 @@ def run_model(model_name):
     # Selector para el método de cómputo del embedding
     embedding_computation = st.selectbox("¿Cómo se computa el embedding?", options=["weighted", "averaged"], key=f"embedding_method_{model_name}")
     # Se asigna el prefijo correspondiente
-    prefijo_embedding = "weighted_" if embedding_computation == "weighted" else "averaged_"
+    # prefijo_embedding = "weighted_" if embedding_computation == "weighted" else "averaged_"
     
-    embeddings = load_embeddings(model_name, version, prefijo_embedding)
+    embeddings = load_embeddings(model_name, version, embedding_computation)
     if embeddings is None:
         return