app.py

import streamlit as st
import pandas as pd
import numpy as np
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource, DataTable, TableColumn, CustomJS, Select, Button, HoverTool
from bokeh.layouts import column
from bokeh.palettes import Reds9, Blues9, Oranges9, Purples9, Greys9, BuGn9, Greens9
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE, trustworthiness
from sklearn.metrics import pairwise_distances
import io
import ot
from sklearn.linear_model import LinearRegression

N_COMPONENTS = 2
TSNE_NEIGHBOURS = 150
WEIGHT_FACTOR = 0.25

TOOLTIPS = """
<div>
    <div>
        <img src="@img{safe}" style="width:128px; height:auto; float: left; margin: 0px 15px 15px 0px;" alt="@img" border="2"></img>
    </div>
    <div>
        <span style="font-size: 17px; font-weight: bold;">@label</span>
    </div>
</div>
"""

def config_style():
    st.markdown("""
        <style>
        .main-title { font-size: 50px; color: #4CAF50; text-align: center; }
        .sub-title { font-size: 30px; color: #555; }
        .custom-text { font-size: 18px; line-height: 1.5; }
        .bk-legend {
            max-height: 200px;
            overflow-y: auto;
        }
        </style>
    """, unsafe_allow_html=True)
    st.markdown('<h1 class="main-title">Merit Embeddings 🎒📃🏆</h1>', unsafe_allow_html=True)

def load_embeddings(model, version, embedding_prefix, weight_factor):
    if model == "Donut":
        df_real = pd.read_csv(f"data/donut/{version}/{embedding_prefix}/de_Rodrigo_merit_secret_all_{weight_factor}embeddings.csv")
        df_par = pd.read_csv(f"data/donut/{version}/{embedding_prefix}/de_Rodrigo_merit_es-digital-paragraph-degradation-seq_{weight_factor}embeddings.csv")
        df_line = pd.read_csv(f"data/donut/{version}/{embedding_prefix}/de_Rodrigo_merit_es-digital-line-degradation-seq_{weight_factor}embeddings.csv")
        df_seq  = pd.read_csv(f"data/donut/{version}/{embedding_prefix}/de_Rodrigo_merit_es-digital-seq_{weight_factor}embeddings.csv")
        df_rot  = pd.read_csv(f"data/donut/{version}/{embedding_prefix}/de_Rodrigo_merit_es-digital-rotation-degradation-seq_{weight_factor}embeddings.csv")
        df_zoom = pd.read_csv(f"data/donut/{version}/{embedding_prefix}/de_Rodrigo_merit_es-digital-zoom-degradation-seq_{weight_factor}embeddings.csv")
        df_render = pd.read_csv(f"data/donut/{version}/{embedding_prefix}/de_Rodrigo_merit_es-render-seq_{weight_factor}embeddings.csv")
        df_pretratrained = pd.read_csv(f"data/donut/{version}/{embedding_prefix}/de_Rodrigo_merit_aux_IIT-CDIP_{weight_factor}embeddings.csv")
        
        # Asignar etiquetas de versión
        df_real["version"] = "real"
        df_par["version"] = "synthetic"
        df_line["version"] = "synthetic"
        df_seq["version"] = "synthetic"
        df_rot["version"] = "synthetic"
        df_zoom["version"] = "synthetic"
        df_render["version"] = "synthetic"
        df_pretratrained["version"] = "pretrained"

        # Asignar fuente (source)
        df_par["source"] = "es-digital-paragraph-degradation-seq"
        df_line["source"] = "es-digital-line-degradation-seq"
        df_seq["source"] = "es-digital-seq"
        df_rot["source"] = "es-digital-rotation-degradation-seq"
        df_zoom["source"] = "es-digital-zoom-degradation-seq"
        df_render["source"] = "es-render-seq"
        df_pretratrained["source"] = "pretrained"

        return {"real": df_real, 
                "synthetic": pd.concat([df_seq, df_line, df_par, df_rot, df_zoom, df_render], ignore_index=True),
                "pretrained": df_pretratrained}
    
    elif model == "Idefics2":
        df_real = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_secret_britanico_{embedding_prefix}embeddings.csv")
        df_par = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_es-digital-paragraph-degradation-seq_{embedding_prefix}embeddings.csv")
        df_line = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_es-digital-line-degradation-seq_{embedding_prefix}embeddings.csv")
        df_seq  = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_es-digital-seq_{embedding_prefix}embeddings.csv")
        df_rot  = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_es-digital-rotation-degradation-seq_{embedding_prefix}embeddings.csv")
        df_zoom = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_es-digital-zoom-degradation-seq_{embedding_prefix}embeddings.csv")
        df_render = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_es-render-seq_{embedding_prefix}embeddings.csv")
        
        # Cargar ambos subconjuntos pretrained y combinarlos
        df_pretratrained_PDFA = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_aux_PDFA_{embedding_prefix}embeddings.csv")
        df_pretratrained_IDL = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_aux_IDL_{embedding_prefix}embeddings.csv")
        df_pretratrained = pd.concat([df_pretratrained_PDFA, df_pretratrained_IDL], ignore_index=True)
        
        # Asignar etiquetas de versión
        df_real["version"] = "real"
        df_par["version"] = "synthetic"
        df_line["version"] = "synthetic"
        df_seq["version"] = "synthetic"
        df_rot["version"] = "synthetic"
        df_zoom["version"] = "synthetic"
        df_render["version"] = "synthetic"
        df_pretratrained["version"] = "pretrained"

        # Asignar fuente (source)
        df_par["source"] = "es-digital-paragraph-degradation-seq"
        df_line["source"] = "es-digital-line-degradation-seq"
        df_seq["source"] = "es-digital-seq"
        df_rot["source"] = "es-digital-rotation-degradation-seq"
        df_zoom["source"] = "es-digital-zoom-degradation-seq"
        df_render["source"] = "es-render-seq"
        df_pretratrained["source"] = "pretrained"

        return {"real": df_real, 
                "synthetic": pd.concat([df_seq, df_line, df_par, df_rot, df_zoom, df_render], ignore_index=True),
                "pretrained": df_pretratrained}
    
    else:
        st.error("Modelo no reconocido")
        return None




def split_versions(df_combined, reduced):
    # Asignar las coordenadas si la reducción es 2D
    if reduced.shape[1] == 2:
        df_combined['x'] = reduced[:, 0]
        df_combined['y'] = reduced[:, 1]
    df_real = df_combined[df_combined["version"] == "real"].copy()
    df_synth = df_combined[df_combined["version"] == "synthetic"].copy()
    df_pretrained = df_combined[df_combined["version"] == "pretrained"].copy()
    
    unique_real = sorted(df_real['label'].unique().tolist())
    unique_synth = {}
    for source in df_synth["source"].unique():
        unique_synth[source] = sorted(df_synth[df_synth["source"] == source]['label'].unique().tolist())
    unique_pretrained = sorted(df_pretrained['label'].unique().tolist())
    
    df_dict = {"real": df_real, "synthetic": df_synth, "pretrained": df_pretrained}
    unique_subsets = {"real": unique_real, "synthetic": unique_synth, "pretrained": unique_pretrained}
    return df_dict, unique_subsets


def get_embedding_from_df(df):
    # Retorna el embedding completo (4 dimensiones en este caso) guardado en la columna 'embedding'
    if 'embedding' in df.columns:
        return np.stack(df['embedding'].to_numpy())
    elif 'x' in df.columns and 'y' in df.columns:
        return df[['x', 'y']].values
    else:
        raise ValueError("No se encontró embedding o coordenadas x,y en el DataFrame.")

def compute_cluster_distance(synthetic_points, real_points, metric="wasserstein", bins=20):
    if metric.lower() == "wasserstein":
        n = synthetic_points.shape[0]
        m = real_points.shape[0]
        weights = np.ones(n) / n
        weights_real = np.ones(m) / m
        M = ot.dist(synthetic_points, real_points, metric='euclidean')
        return ot.emd2(weights, weights_real, M)
    elif metric.lower() == "euclidean":
        center_syn = np.mean(synthetic_points, axis=0)
        center_real = np.mean(real_points, axis=0)
        return np.linalg.norm(center_syn - center_real)
    elif metric.lower() == "kl":
        # Para KL usamos histogramas multidimensionales con límites globales en cada dimensión
        all_points = np.vstack([synthetic_points, real_points])
        edges = [
            np.linspace(np.min(all_points[:, i]), np.max(all_points[:, i]), bins+1)
            for i in range(all_points.shape[1])
        ]
        H_syn, _ = np.histogramdd(synthetic_points, bins=edges)
        H_real, _ = np.histogramdd(real_points, bins=edges)
        eps = 1e-10
        P = H_syn + eps
        Q = H_real + eps
        P = P / P.sum()
        Q = Q / Q.sum()
        kl = np.sum(P * np.log(P / Q))
        return kl
    else:
        raise ValueError("Métrica desconocida. Usa 'wasserstein', 'euclidean' o 'kl'.")

def compute_cluster_distances_synthetic_individual(synthetic_df: pd.DataFrame, df_real: pd.DataFrame, real_labels: list, metric="wasserstein", bins=20) -> pd.DataFrame:
    distances = {}
    groups = synthetic_df.groupby(['source', 'label'])
    for (source, label), group in groups:
        key = f"{label} ({source})"
        data = get_embedding_from_df(group)
        distances[key] = {}
        for real_label in real_labels:
            real_data = get_embedding_from_df(df_real[df_real['label'] == real_label])
            d = compute_cluster_distance(data, real_data, metric=metric, bins=bins)
            distances[key][real_label] = d
    for source, group in synthetic_df.groupby('source'):
        key = f"Global ({source})"
        data = get_embedding_from_df(group)
        distances[key] = {}
        for real_label in real_labels:
            real_data = get_embedding_from_df(df_real[df_real['label'] == real_label])
            d = compute_cluster_distance(data, real_data, metric=metric, bins=bins)
            distances[key][real_label] = d
    return pd.DataFrame(distances).T

def compute_continuity(X, X_embedded, n_neighbors=5):
    n = X.shape[0]
    D_high = pairwise_distances(X, metric='euclidean')
    D_low = pairwise_distances(X_embedded, metric='euclidean')
    indices_high = np.argsort(D_high, axis=1)
    indices_low = np.argsort(D_low, axis=1)
    k_high = indices_high[:, 1:n_neighbors+1]
    k_low = indices_low[:, 1:n_neighbors+1]
    total = 0.0
    for i in range(n):
        set_high = set(k_high[i])
        set_low = set(k_low[i])
        missing = set_high - set_low
        for j in missing:
            rank = np.where(indices_low[i] == j)[0][0]
            total += (rank - n_neighbors)
    norm = 2.0 / (n * n_neighbors * (2*n - 3*n_neighbors - 1))
    continuity_value = 1 - norm * total
    return continuity_value

def create_table(df_distances):
    df_table = df_distances.copy()
    df_table.reset_index(inplace=True)
    df_table.rename(columns={'index': 'Synthetic'}, inplace=True)
    min_row = {"Synthetic": "Min."}
    mean_row = {"Synthetic": "Mean"}
    max_row = {"Synthetic": "Max."}
    for col in df_table.columns:
        if col != "Synthetic":
            min_row[col] = df_table[col].min()
            mean_row[col] = df_table[col].mean()
            max_row[col] = df_table[col].max()
    df_table = pd.concat([df_table, pd.DataFrame([min_row, mean_row, max_row])], ignore_index=True)
    source_table = ColumnDataSource(df_table)
    columns = [TableColumn(field='Synthetic', title='Synthetic')]
    for col in df_table.columns:
        if col != 'Synthetic':
            columns.append(TableColumn(field=col, title=col))
    total_height = 30 + len(df_table)*28
    data_table = DataTable(source=source_table, columns=columns, sizing_mode='stretch_width', height=total_height)
    return data_table, df_table, source_table

def create_figure(dfs, unique_subsets, color_maps, model_name):
    # Se crea el plot para el embedding reducido (asumiendo que es 2D)
    fig = figure(width=600, height=600, tools="wheel_zoom,pan,reset,save", active_scroll="wheel_zoom", tooltips=TOOLTIPS, title="")
    
    # Renderizar datos reales
    real_renderers = add_dataset_to_fig(fig, dfs["real"], unique_subsets["real"],
                                        marker="circle", color_mapping=color_maps["real"],
                                        group_label="Real")
    
    # Renderizar datos sintéticos (por fuente)
    marker_mapping = {
        "es-digital-paragraph-degradation-seq": "x",
        "es-digital-line-degradation-seq": "cross",
        "es-digital-seq": "triangle",
        "es-digital-rotation-degradation-seq": "diamond",
        "es-digital-zoom-degradation-seq": "asterisk",
        "es-render-seq": "inverted_triangle"
    }
    synthetic_renderers = {}
    synth_df = dfs["synthetic"]
    for source in unique_subsets["synthetic"]:
        df_source = synth_df[synth_df["source"] == source]
        marker = marker_mapping.get(source, "square")
        renderers = add_synthetic_dataset_to_fig(fig, df_source, unique_subsets["synthetic"][source],
                                                  marker=marker,
                                                  color_mapping=color_maps["synthetic"][source],
                                                  group_label=source)
        synthetic_renderers.update(renderers)
    
    # Agregar el subset pretrained (se puede usar un marcador distinto, por ejemplo, "triangle")
    pretrained_renderers = add_dataset_to_fig(fig, dfs["pretrained"], unique_subsets["pretrained"],
                                               marker="triangle", color_mapping=color_maps["pretrained"],
                                               group_label="Pretrained")
    
    fig.legend.location = "top_right"
    fig.legend.click_policy = "hide"
    show_legend = st.checkbox("Show Legend", value=False, key=f"legend_{model_name}")
    fig.legend.visible = show_legend
    return fig, real_renderers, synthetic_renderers, pretrained_renderers


def add_dataset_to_fig(fig, df, selected_labels, marker, color_mapping, group_label):
    renderers = {}
    for label in selected_labels:
        subset = df[df['label'] == label]
        if subset.empty:
            continue
        source = ColumnDataSource(data=dict(
            x=subset['x'],
            y=subset['y'],
            label=subset['label'],
            img=subset.get('img', "")
        ))
        color = color_mapping[label]
        legend_label = f"{label} ({group_label})"
        if marker == "circle":
            r = fig.circle('x', 'y', size=10, source=source,
                           fill_color=color, line_color=color,
                           legend_label=legend_label)
        elif marker == "square":
            r = fig.square('x', 'y', size=10, source=source,
                           fill_color=color, line_color=color,
                           legend_label=legend_label)
        elif marker == "triangle":
            r = fig.triangle('x', 'y', size=12, source=source,
                             fill_color=color, line_color=color,
                             legend_label=legend_label)
        renderers[label + f" ({group_label})"] = r
    return renderers

def add_synthetic_dataset_to_fig(fig, df, labels, marker, color_mapping, group_label):
    renderers = {}
    for label in labels:
        subset = df[df['label'] == label]
        if subset.empty:
            continue
        source_obj = ColumnDataSource(data=dict(
            x=subset['x'],
            y=subset['y'],
            label=subset['label'],
            img=subset.get('img', "")
        ))
        color = color_mapping[label]
        legend_label = group_label
        if marker == "square":
            r = fig.square('x', 'y', size=10, source=source_obj,
                           fill_color=color, line_color=color,
                           legend_label=legend_label)
        elif marker == "triangle":
            r = fig.triangle('x', 'y', size=12, source=source_obj,
                             fill_color=color, line_color=color,
                             legend_label=legend_label)
        elif marker == "inverted_triangle":
            r = fig.inverted_triangle('x', 'y', size=12, source=source_obj,
                                      fill_color=color, line_color=color,
                                      legend_label=legend_label)
        elif marker == "diamond":
            r = fig.diamond('x', 'y', size=10, source=source_obj,
                            fill_color=color, line_color=color,
                            legend_label=legend_label)
        elif marker == "cross":
            r = fig.cross('x', 'y', size=12, source=source_obj,
                          fill_color=color, line_color=color,
                          legend_label=legend_label)
        elif marker == "x":
            r = fig.x('x', 'y', size=12, source=source_obj,
                      fill_color=color, line_color=color,
                      legend_label=legend_label)
        elif marker == "asterisk":
            r = fig.asterisk('x', 'y', size=12, source=source_obj,
                             fill_color=color, line_color=color,
                             legend_label=legend_label)
        else:
            r = fig.circle('x', 'y', size=10, source=source_obj,
                           fill_color=color, line_color=color,
                           legend_label=legend_label)
        renderers[label + f" ({group_label})"] = r
    return renderers

def get_color_maps(unique_subsets):
    color_map = {}
    num_real = len(unique_subsets["real"])
    red_palette = Reds9[:num_real] if num_real <= 9 else (Reds9 * ((num_real // 9) + 1))[:num_real]
    color_map["real"] = {label: red_palette[i] for i, label in enumerate(sorted(unique_subsets["real"]))}
    
    color_map["synthetic"] = {}
    for source, labels in unique_subsets["synthetic"].items():
        if source == "es-digital-seq":
            palette = Blues9[:len(labels)] if len(labels) <= 9 else (Blues9 * ((len(labels)//9)+1))[:len(labels)]
        elif source == "es-digital-line-degradation-seq":
            palette = Purples9[:len(labels)] if len(labels) <= 9 else (Purples9 * ((len(labels)//9)+1))[:len(labels)]
        elif source == "es-digital-paragraph-degradation-seq":
            palette = BuGn9[:len(labels)] if len(labels) <= 9 else (BuGn9 * ((len(labels)//9)+1))[:len(labels)]
        elif source == "es-digital-rotation-degradation-seq":
            palette = Greys9[:len(labels)] if len(labels) <= 9 else (Greys9 * ((len(labels)//9)+1))[:len(labels)]
        elif source == "es-digital-zoom-degradation-seq":
            palette = Oranges9[:len(labels)] if len(labels) <= 9 else (Oranges9 * ((len(labels)//9)+1))[:len(labels)]
        elif source == "es-render-seq":
            palette = Greens9[:len(labels)] if len(labels) <= 9 else (Greens9 * ((len(labels)//9)+1))[:len(labels)]
        else:
            palette = Blues9[:len(labels)] if len(labels) <= 9 else (Blues9 * ((len(labels)//9)+1))[:len(labels)]
        color_map["synthetic"][source] = {label: palette[i] for i, label in enumerate(sorted(labels))}
    
    # Asignar colores al subset pretrained usando, por ejemplo, la paleta Purples9
    num_pretrained = len(unique_subsets["pretrained"])
    purple_palette = Purples9[:num_pretrained] if num_pretrained <= 9 else (Purples9 * ((num_pretrained // 9) + 1))[:num_pretrained]
    color_map["pretrained"] = {label: purple_palette[i] for i, label in enumerate(sorted(unique_subsets["pretrained"]))}
    
    return color_map


def calculate_cluster_centers(df, labels):
    centers = {}
    for label in labels:
        subset = df[df['label'] == label]
        if not subset.empty and 'x' in subset.columns and 'y' in subset.columns:
            centers[label] = (subset['x'].mean(), subset['y'].mean())
    return centers

def compute_global_regression(df_combined, embedding_cols, tsne_params, df_f1, reduction_method="t-SNE", distance_metric="wasserstein"):
    if reduction_method == "PCA":
        reducer = PCA(n_components=N_COMPONENTS)
    else:
        reducer = TSNE(n_components=2, random_state=42, 
                         perplexity=tsne_params["perplexity"], 
                         learning_rate=tsne_params["learning_rate"])
    
    reduced = reducer.fit_transform(df_combined[embedding_cols].values)
    # Guardamos el embedding completo (por ejemplo, 4 dimensiones en PCA)
    df_combined['embedding'] = list(reduced)
    # Si el embedding es 2D, asignamos x e y para visualización
    if reduced.shape[1] == 2:
        df_combined['x'] = reduced[:, 0]
        df_combined['y'] = reduced[:, 1]
    
    explained_variance = None
    if reduction_method == "PCA":
        explained_variance = reducer.explained_variance_ratio_
    
    trust = None
    cont = None
    if reduction_method == "t-SNE":
        X = df_combined[embedding_cols].values
        trust = trustworthiness(X, reduced, n_neighbors=TSNE_NEIGHBOURS)
        cont = compute_continuity(X, reduced, n_neighbors=TSNE_NEIGHBOURS)
    
    dfs_reduced, unique_subsets = split_versions(df_combined, reduced)
    
    df_distances = compute_cluster_distances_synthetic_individual(
        dfs_reduced["synthetic"],
        dfs_reduced["real"],
        unique_subsets["real"],
        metric=distance_metric
    )
    
    global_distances = {}
    for idx in df_distances.index:
        if idx.startswith("Global"):
            source = idx.split("(")[1].rstrip(")")
            global_distances[source] = df_distances.loc[idx].values
            
    all_x = []
    all_y = []
    for source in df_f1.columns:
        if source in global_distances:
            x_vals = global_distances[source]
            y_vals = df_f1[source].values
            all_x.extend(x_vals)
            all_y.extend(y_vals)
    all_x_arr = np.array(all_x).reshape(-1, 1)
    all_y_arr = np.array(all_y)
    
    model_global = LinearRegression().fit(all_x_arr, all_y_arr)
    r2 = model_global.score(all_x_arr, all_y_arr)
    slope = model_global.coef_[0]
    intercept = model_global.intercept_
    
    scatter_fig = figure(width=600, height=600, tools="pan,wheel_zoom,reset,save", 
                         title="Scatter Plot: Distance vs F1")
    source_colors = {
        "es-digital-paragraph-degradation-seq": "blue",
        "es-digital-line-degradation-seq": "green",
        "es-digital-seq": "red",
        "es-digital-zoom-degradation-seq": "orange",
        "es-digital-rotation-degradation-seq": "purple",
        "es-digital-rotation-zoom-degradation-seq": "brown",
        "es-render-seq": "cyan"
    }
    for source in df_f1.columns:
        if source in global_distances:
            x_vals = global_distances[source]
            y_vals = df_f1[source].values
            data = {"x": x_vals, "y": y_vals, "Fuente": [source]*len(x_vals)}
            cds = ColumnDataSource(data=data)
            scatter_fig.circle('x', 'y', size=8, alpha=0.7, source=cds,
                               fill_color=source_colors.get(source, "gray"),
                               line_color=source_colors.get(source, "gray"),
                               legend_label=source)
    scatter_fig.xaxis.axis_label = "Distance (Global, por Colegio)"
    scatter_fig.yaxis.axis_label = "F1 Score"
    scatter_fig.legend.location = "top_right"
    hover_tool = HoverTool(tooltips=[("Distance", "@x"), ("F1", "@y"), ("Subset", "@Fuente")])
    scatter_fig.add_tools(hover_tool)
    
    x_line = np.linspace(all_x_arr.min(), all_x_arr.max(), 100)
    y_line = model_global.predict(x_line.reshape(-1, 1))
    scatter_fig.line(x_line, y_line, line_width=2, line_color="black", legend_label="Global Regression")
    
    results = {
        "R2": r2,
        "slope": slope,
        "intercept": intercept,
        "scatter_fig": scatter_fig,
        "dfs_reduced": dfs_reduced,
        "unique_subsets": unique_subsets,
        "df_distances": df_distances,
        "explained_variance": explained_variance,
        "trustworthiness": trust,
        "continuity": cont
    }
    
    if reduction_method == "PCA":
        results["pca_model"] = reducer  # Agregamos el objeto PCA para usarlo luego en los plots
    
    return results

def optimize_tsne_params(df_combined, embedding_cols, df_f1, distance_metric):
    perplexity_range = np.linspace(30, 50, 10)
    learning_rate_range = np.linspace(200, 1000, 20)
    
    best_R2 = -np.inf
    best_params = None
    total_steps = len(perplexity_range) * len(learning_rate_range)
    step = 0

    progress_text = st.empty()
    
    for p in perplexity_range:
        for lr in learning_rate_range:
            step += 1
            progress_text.text(f"Evaluating: Perplexity={p:.2f}, Learning Rate={lr:.2f} (Step {step}/{total_steps})")
            
            tsne_params = {"perplexity": p, "learning_rate": lr}
            result = compute_global_regression(df_combined, embedding_cols, tsne_params, df_f1, reduction_method="t-SNE", distance_metric=distance_metric)
            r2_temp = result["R2"]
            st.write(f"Parameters: Perplexity={p:.2f}, Learning Rate={lr:.2f} -> R²={r2_temp:.4f}")
            
            if r2_temp > best_R2:
                best_R2 = r2_temp
                best_params = (p, lr)
    
    progress_text.text("Optimization completed!")
    return best_params, best_R2

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}")
    # Se asigna el prefijo correspondiente
    # prefijo_embedding = "weighted_" if embedding_computation == "weighted" else "averaged_"

    if embedding_computation == "weighted":
        # prefijo_embedding = "weighted_"
        weight_factor = f"{WEIGHT_FACTOR}_"
    else:
        # prefijo_embedding = "averaged_"
        weight_factor = ""
    
    embeddings = load_embeddings(model_name, version, embedding_computation, weight_factor)
    if embeddings is None:
        return

    # Nuevo selector para incluir o excluir el dataset pretrained
    include_pretrained = st.checkbox("Incluir dataset pretrained", value=True)
    if not include_pretrained:
        # Removemos la entrada pretrained del diccionario, si existe.
        embeddings.pop("pretrained", None)
    
    # Extraer columnas de embedding de los datos "real"
    embedding_cols = [col for col in embeddings["real"].columns if col.startswith("dim_")]
    # Concatenamos los datasets disponibles (ahora, sin pretrained si se deseleccionó)
    df_combined = pd.concat(list(embeddings.values()), ignore_index=True)
    
    try:
        df_f1 = pd.read_csv("data/f1-donut.csv", sep=';', index_col=0)
    except Exception as e:
        st.error(f"Error loading f1-donut.csv: {e}")
        return

    st.markdown('<h6 class="sub-title">Select Dimensionality Reduction Method</h6>', unsafe_allow_html=True)
    reduction_method = st.selectbox("", options=["t-SNE", "PCA"], key=f"reduction_{model_name}")
    
    distance_metric = st.selectbox("Select Distance Metric:", 
                                   options=["Wasserstein", "Euclidean", "KL"], 
                                   key=f"distance_metric_{model_name}")
    
    tsne_params = {}
    if reduction_method == "t-SNE":
        if st.button("Optimize TSNE parameters", key=f"optimize_tsne_{model_name}"):
            st.info("Running optimization, this can take a while...")
            best_params, best_R2 = optimize_tsne_params(df_combined, embedding_cols, df_f1, distance_metric.lower())
            st.success(f"Best parameters: Perplexity = {best_params[0]:.2f}, Learning Rate = {best_params[1]:.2f} with R² = {best_R2:.4f}")
            tsne_params = {"perplexity": best_params[0], "learning_rate": best_params[1]}
        else:
            perplexity_val = st.number_input(
                "Perplexity", 
                min_value=5.0, 
                max_value=50.0, 
                value=30.0, 
                step=1.0, 
                format="%.2f",
                key=f"perplexity_{model_name}"
            )
            learning_rate_val = st.number_input(
                "Learning Rate", 
                min_value=10.0, 
                max_value=1000.0, 
                value=200.0, 
                step=10.0, 
                format="%.2f",
                key=f"learning_rate_{model_name}"
            )
            tsne_params = {"perplexity": perplexity_val, "learning_rate": learning_rate_val}
    
    result = compute_global_regression(df_combined, embedding_cols, tsne_params, df_f1, reduction_method=reduction_method, distance_metric=distance_metric.lower())
    
    reg_metrics = pd.DataFrame({
        "Slope": [result["slope"]],
        "Intercept": [result["intercept"]],
        "R2": [result["R2"]]
    })
    st.table(reg_metrics)
    
    if reduction_method == "PCA" and result["explained_variance"] is not None:
        st.subheader("Explained Variance Ratio")
        component_names = [f"PC{i+1}" for i in range(len(result["explained_variance"]))]
        variance_df = pd.DataFrame({
            "Component": component_names,
            "Explained Variance": result["explained_variance"]
        })
        st.table(variance_df)
    elif reduction_method == "t-SNE":
        st.subheader("t-SNE Quality Metrics")
        st.write(f"Trustworthiness: {result['trustworthiness']:.4f}")
        st.write(f"Continuity: {result['continuity']:.4f}")

    # Si se usó PCA, se muestran los plots de loadings con Bokeh (con hover para ver la etiqueta)
    if reduction_method == "PCA" and result.get("pca_model") is not None:
        pca_model = result["pca_model"]
        components = pca_model.components_  # Shape: (n_components, n_features)
        
        st.subheader("Pesos de las Componentes Principales (Loadings)")
        # Se crea un plot de barras por cada componente
        for i, comp in enumerate(components):
            source = ColumnDataSource(data=dict(
                dimensions=embedding_cols,
                weight=comp
            ))
            p = figure(x_range=embedding_cols, title=f"Componente Principal {i+1}",
                       plot_height=400, plot_width=600,
                       toolbar_location=None, tools="")
            p.vbar(x='dimensions', top='weight', width=0.8, source=source)
            # Ocultar etiquetas del eje x para un aspecto más limpio
            p.xaxis.major_label_text_font_size = '0pt'
            # Agregar HoverTool para mostrar la dimensión y su peso
            hover = HoverTool(tooltips=[("Dimensión", "@dimensions"), ("Peso", "@weight")])
            p.add_tools(hover)
            p.xaxis.axis_label = "Dimensiones originales"
            p.yaxis.axis_label = "Peso"
            st.bokeh_chart(p)
    
    data_table, df_table, source_table = create_table(result["df_distances"])
    real_subset_names = list(df_table.columns[1:])
    real_select = Select(title="", value=real_subset_names[0], options=real_subset_names)
    reset_button = Button(label="Reset Colors", button_type="primary")
    line_source = ColumnDataSource(data={'x': [], 'y': []})
    
    if (reduction_method == "t-SNE" and N_COMPONENTS == 2) or (reduction_method == "PCA" and N_COMPONENTS == 2):
        fig, real_renderers, synthetic_renderers, pretrained_renderers = create_figure(
            result["dfs_reduced"],
            result["unique_subsets"],
            get_color_maps(result["unique_subsets"]),
            model_name
        )
        fig.line('x', 'y', source=line_source, line_width=2, line_color='black')
        centers_real = calculate_cluster_centers(result["dfs_reduced"]["real"], result["unique_subsets"]["real"])
        real_centers_js = {k: [v[0], v[1]] for k, v in centers_real.items()}
        synthetic_centers = {}
        synth_labels = sorted(result["dfs_reduced"]["synthetic"]['label'].unique().tolist())
        for label in synth_labels:
            subset = result["dfs_reduced"]["synthetic"][result["dfs_reduced"]["synthetic"]['label'] == label]
            if 'x' in subset.columns and 'y' in subset.columns:
                synthetic_centers[label] = [subset['x'].mean(), subset['y'].mean()]
        callback = CustomJS(args=dict(source=source_table, line_source=line_source,
                                  synthetic_centers=synthetic_centers,
                                  real_centers=real_centers_js,
                                  real_select=real_select),
        code="""
            var selected = source.selected.indices;
            if (selected.length > 0) {
                var idx = selected[0];
                var data = source.data;
                var synth_label = data['Synthetic'][idx];
                var real_label = real_select.value;
                var syn_coords = synthetic_centers[synth_label];
                var real_coords = real_centers[real_label];
                line_source.data = {'x': [syn_coords[0], real_coords[0]], 'y': [syn_coords[1], real_coords[1]]};
                line_source.change.emit();
            } else {
                line_source.data = {'x': [], 'y': []};
                line_source.change.emit();
            }
        """)
        source_table.selected.js_on_change('indices', callback)
        real_select.js_on_change('value', callback)
    
        reset_callback = CustomJS(args=dict(line_source=line_source),
        code="""
            line_source.data = {'x': [], 'y': []};
            line_source.change.emit();
        """)
        reset_button.js_on_event("button_click", reset_callback)
        layout = column(fig, result["scatter_fig"], column(real_select, reset_button, data_table))
    else:
        layout = column(result["scatter_fig"], column(real_select, reset_button, data_table))
    
    st.bokeh_chart(layout, use_container_width=True)
    
    buffer = io.BytesIO()
    df_table.to_excel(buffer, index=False)
    buffer.seek(0)
    
    st.download_button(
        label="Export Table",
        data=buffer,
        file_name=f"cluster_distances_{model_name}.xlsx",
        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
        key=f"download_button_excel_{model_name}"
    )

def main():
    config_style()
    tabs = st.tabs(["Donut", "Idefics2"])
    with tabs[0]:
        st.markdown('<h2 class="sub-title">Donut 🤗</h2>', unsafe_allow_html=True)
        run_model("Donut")
    with tabs[1]:
        st.markdown('<h2 class="sub-title">Idefics2 🤗</h2>', unsafe_allow_html=True)
        run_model("Idefics2")

if __name__ == "__main__":
    main()