Include Weight Selector for Weighted Computation

app.py

@@ -14,7 +14,7 @@ from sklearn.linear_model import LinearRegression
 
 N_COMPONENTS = 2
 TSNE_NEIGHBOURS = 150
-WEIGHT_FACTOR = 0.05
+# WEIGHT_FACTOR = 0.05
 
 TOOLTIPS = """
 <div>
@@ -28,6 +28,7 @@ TOOLTIPS = """
 """
 
 def config_style():
+    # st.set_page_config(layout="wide")
     st.markdown("""
         <style>
         .main-title { font-size: 50px; color: #4CAF50; text-align: center; }
@@ -533,13 +534,19 @@ def optimize_tsne_params(df_combined, embedding_cols, df_f1, distance_metric):
 def run_model(model_name):
     version = st.selectbox("Select Model Version:", options=["vanilla", "finetuned_real"], key=f"version_{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}")
+    embedding_computation = st.selectbox("¿Cómo se computa el embedding?", options=["averaged", "weighted"], key=f"embedding_method_{model_name}")
     # Se asigna el prefijo correspondiente
 
     if embedding_computation == "weighted":
-        weight_factor = f"{WEIGHT_FACTOR}_"
+         selected_weight_factor = st.selectbox(
+             "Seleccione el Weight Factor", 
+             options=[0.05, 0.1, 0.25, 0.5], 
+             index=0,  # índice 1 para que por defecto sea 0.05
+             key=f"weight_factor_{model_name}"
+         )
+         weight_factor = f"{selected_weight_factor}_"
     else:
-        weight_factor = ""
+         weight_factor = ""
     
     embeddings = load_embeddings(model_name, version, embedding_computation, weight_factor)
     if embeddings is None: