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

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)

# =============================================================================
# Funciones de carga de datos y procesamiento (sin cambios en su mayoría)
# =============================================================================

def load_embeddings(model, version):
    if model == "Donut":
        df_real = pd.read_csv(f"data/donut_{version}_de_Rodrigo_merit_secret_all_embeddings.csv")
        df_par = pd.read_csv(f"data/donut_{version}_de_Rodrigo_merit_es-digital-paragraph-degradation-seq_embeddings.csv")
        df_line = pd.read_csv(f"data/donut_{version}_de_Rodrigo_merit_es-digital-line-degradation-seq_embeddings.csv")
        df_seq  = pd.read_csv(f"data/donut_{version}_de_Rodrigo_merit_es-digital-seq_embeddings.csv")
        df_rot  = pd.read_csv(f"data/donut_{version}_de_Rodrigo_merit_es-digital-rotation-degradation-seq_embeddings.csv")
        df_zoom = pd.read_csv(f"data/donut_{version}_de_Rodrigo_merit_es-digital-zoom-degradation-seq_embeddings.csv")
        df_render = pd.read_csv(f"data/donut_{version}_de_Rodrigo_merit_es-render-seq_embeddings.csv")
        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_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"
        return {"real": df_real, "synthetic": pd.concat([df_seq, df_line, df_par, df_rot, df_zoom, df_render], ignore_index=True)}
    
    elif model == "Idefics2":
        df_real = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_secret_britanico_embeddings.csv")
        df_par = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_es-digital-paragraph-degradation-seq_embeddings.csv")
        df_line = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_es-digital-line-degradation-seq_embeddings.csv")
        df_seq  = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_es-digital-seq_embeddings.csv")
        df_rot  = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_es-digital-rotation-degradation-seq_embeddings.csv")
        df_zoom = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_es-digital-zoom-degradation-seq_embeddings.csv")
        df_render = pd.read_csv(f"data/idefics2_{version}_de_Rodrigo_merit_es-render-seq_embeddings.csv")
        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_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"
        return {"real": df_real, "synthetic": pd.concat([df_seq, df_line, df_par, df_rot, df_zoom, df_render], ignore_index=True)}
    
    else:
        st.error("Modelo no reconocido")
        return None

def split_versions(df_combined, reduced):
    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()
    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())
    df_dict = {"real": df_real, "synthetic": df_synth}
    unique_subsets = {"real": unique_real, "synthetic": unique_synth}
    return df_dict, unique_subsets

# =============================================================================
# Funciones para calcular distancias entre clusters según la métrica seleccionada
# (Wasserstein, Euclidean o KL)
# =============================================================================

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":
        all_points = np.vstack([synthetic_points, real_points])
        x_min, y_min = np.min(all_points, axis=0)
        x_max, y_max = np.max(all_points, axis=0)
        x_bins = np.linspace(x_min, x_max, bins+1)
        y_bins = np.linspace(y_min, y_max, bins+1)
        H_syn, _, _ = np.histogram2d(synthetic_points[:,0], synthetic_points[:,1], bins=[x_bins, y_bins])
        H_real, _, _ = np.histogram2d(real_points[:,0], real_points[:,1], bins=[x_bins, y_bins])
        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 = group[['x', 'y']].values
        distances[key] = {}
        for real_label in real_labels:
            real_data = df_real[df_real['label'] == real_label][['x','y']].values
            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 = group[['x','y']].values
        distances[key] = {}
        for real_label in real_labels:
            real_data = df_real[df_real['label'] == real_label][['x','y']].values
            d = compute_cluster_distance(data, real_data, metric=metric, bins=bins)
            distances[key][real_label] = d
    return pd.DataFrame(distances).T

# =============================================================================
# Función para calcular continuidad (mide la preservación de la vecindad original en el embedding)
# =============================================================================

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

# =============================================================================
# Funciones de visualización (sin cambios)
# =============================================================================

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):
    fig = figure(width=600, height=600, tools="wheel_zoom,pan,reset,save", active_scroll="wheel_zoom", tooltips=TOOLTIPS, title="")
    real_renderers = add_dataset_to_fig(fig, dfs["real"], unique_subsets["real"],
                                        marker="circle", color_mapping=color_maps["real"],
                                        group_label="Real")
    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)
    
    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

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))}
    return color_map

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

# =============================================================================
# Pipeline central: reducción, cálculo de distancias y regresión global.
# Se agrega el parámetro distance_metric.
# Además, si se utiliza t-SNE, se calculan trustworthiness y continuity.
# =============================================================================

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=2)
    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)
    
    # Para PCA se captura la explained variance ratio
    explained_variance = None
    if reduction_method == "PCA":
        explained_variance = reducer.explained_variance_ratio_
    
    # Si se usa t-SNE, calculamos trustworthiness y continuity
    trust = None
    cont = None
    if reduction_method == "t-SNE":
        X = df_combined[embedding_cols].values
        trust = trustworthiness(X, reduced, n_neighbors=5)
        cont = compute_continuity(X, reduced, n_neighbors=5)
    
    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")
    
    return {
        "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,  # Solo para PCA
        "trustworthiness": trust,                  # Solo para t-SNE
        "continuity": cont                         # Solo para t-SNE
    }

# =============================================================================
# Optimización de parámetros para TSNE (se propaga también la métrica de distancia)
# =============================================================================

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

# =============================================================================
# Función principal run_model: incluye selector de versión, método de reducción, métrica de distancia,
# y, si se usa t-SNE, muestra trustworthiness y continuity.
# =============================================================================

def run_model(model_name):
    version = st.selectbox("Select Model Version:", options=["vanilla", "finetuned_real"], key=f"version_{model_name}")
    
    embeddings = load_embeddings(model_name, version)
    if embeddings is None:
        return
    embedding_cols = [col for col in embeddings["real"].columns if col.startswith("dim_")]
    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")
        variance_df = pd.DataFrame({
            "Component": ["PC1", "PC2"],
            "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}")
    
    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': []})
    
    fig, real_renderers, synthetic_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]
        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)
    
    buffer = io.BytesIO()
    df_table.to_excel(buffer, index=False)
    buffer.seek(0)
    
    layout = column(fig, result["scatter_fig"], column(real_select, reset_button, data_table))
    st.bokeh_chart(layout, use_container_width=True)
    
    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()