Update app.py

app.py

@@ -1,314 +1,29 @@
-import streamlit as st
-import tensorflow as tf
-from tensorflow import keras
-import numpy as np
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-from sklearn.model_selection import train_test_split, GridSearchCV
-from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder
-from sklearn.decomposition import PCA, TruncatedSVD
-from sklearn.manifold import TSNE
-import umap.umap_ as umap
-import shap
-import joblib
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, auc, mean_squared_error, mean_absolute_error, r2_score, classification_report, silhouette_score
-from sklearn.pipeline import Pipeline
-from sklearn.compose import ColumnTransformer
-from sklearn.impute import SimpleImputer
-from sklearn.cluster import KMeans, DBSCAN
-from sklearn.mixture import GaussianMixture
-from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
-from sklearn.linear_model import LogisticRegression, LinearRegression
-from sklearn.svm import SVC, SVR
-from xgboost import XGBClassifier, XGBRegressor
-import matplotlib.pyplot as plt
-from io import BytesIO
-import time
-from PIL import Image
-import zipfile
-import os
-
-# Set page config
-st.set_page_config(page_title="Neural-Vision Enhanced", layout="wide")
-
-# Helper Functions for Image Processing
-def preprocess_image(image_path, target_size=(224, 224)):
-    """Preprocess an image by resizing and normalizing it."""
-    img = Image.open(image_path).convert("RGB")
-    img = img.resize(target_size)
-    img_array = np.array(img) / 255.0  # Normalize pixel values to [0, 1]
-    return img_array
-
-def load_image_dataset(zip_path, target_size=(224, 224), problem_type="Classification"):
-    """Load and preprocess an image dataset from a zip file."""
-    # Check file size (5GB = 5 * 1024 * 1024 * 1024 bytes)
-    file_size = os.path.getsize(zip_path) if isinstance(zip_path, str) else zip_path.size
-    max_size = 5 * 1024 * 1024 * 1024  # 5GB in bytes
-    if file_size > max_size:
-        raise ValueError(f"Uploaded file size ({file_size / (1024 * 1024):.2f} MB) exceeds the 5GB limit.")
-    
-    # Extract zip file to a temporary directory
-    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
-        zip_ref.extractall('temp_images')
-    
-    if problem_type == "Classification":
-        image_paths = []
-        labels = []
-        class_names = sorted(os.listdir('temp_images'))
-        for label, class_name in enumerate(class_names):
-            class_dir = os.path.join('temp_images', class_name)
-            if os.path.isdir(class_dir):
-                for img_name in os.listdir(class_dir):
-                    image_path = os.path.join(class_dir, img_name)
-                    if os.path.isfile(image_path):
-                        image_paths.append(image_path)
-                        labels.append(label)
-        images = [preprocess_image(path, target_size) for path in image_paths]
-        images = np.array(images)
-        labels = np.array(labels)
-        data = (images, labels, class_names)
-    else:  # Compression or Clustering
-        image_dir = 'temp_images'
-        image_paths = [os.path.join(image_dir, img_name) for img_name in os.listdir(image_dir) if os.path.isfile(os.path.join(image_dir, img_name))]
-        images = [preprocess_image(path, target_size) for path in image_paths]
-        images = np.array(images)
-        data = (images, None, None)
-    
-    # Clean up temporary directory
-    for root, dirs, files in os.walk('temp_images', topdown=False):
-        for name in files:
-            os.remove(os.path.join(root, name))
-        for name in dirs:
-            os.rmdir(os.path.join(root, name))
-    os.rmdir('temp_images')
-    
-    return data
-
-# Model Building Functions
-def get_model_config(model_type, problem_type):
-    configs = {
-        "Random Forest": {
-            "Regression": {"model_class": RandomForestRegressor, "params": {"n_estimators": 100, "random_state": 42},
-                           "grid_params": {"n_estimators": [50, 100, 200], "max_depth": [None, 10, 20]}},
-            "Binary Classification": {"model_class": RandomForestClassifier, "params": {"n_estimators": 100, "random_state": 42},
-                                     "grid_params": {"n_estimators": [50, 100, 200], "max_depth": [None, 10, 20]}},
-            "Multi-Class": {"model_class": RandomForestClassifier, "params": {"n_estimators": 100, "random_state": 42},
-                            "grid_params": {"n_estimators": [50, 100, 200], "max_depth": [None, 10, 20]}}
-        },
-        "XGBoost": {
-            "Regression": {"model_class": XGBRegressor, "params": {"n_estimators": 100, "random_state": 42},
-                           "grid_params": {"n_estimators": [50, 100, 200], "max_depth": [3, 5, 7], "learning_rate": [0.01, 0.1, 0.3]}},
-            "Binary Classification": {"model_class": XGBClassifier, "params": {"n_estimators": 100, "random_state": 42, "use_label_encoder": False, "eval_metric": 'logloss'},
-                                     "grid_params": {"n_estimators": [50, 100, 200], "max_depth": [3, 5, 7], "learning_rate": [0.01, 0.1, 0.3]}},
-            "Multi-Class": {"model_class": XGBClassifier, "params": {"n_estimators": 100, "random_state": 42, "use_label_encoder": False, "eval_metric": 'mlogloss'},
-                            "grid_params": {"n_estimators": [50, 100, 200], "max_depth": [3, 5, 7], "learning_rate": [0.01, 0.1, 0.3]}}
-        },
-        "Logistic Regression": {
-            "Binary Classification": {"model_class": LogisticRegression, "params": {"max_iter": 1000, "random_state": 42},
-                                     "grid_params": {"C": [0.1, 1.0, 10.0], "solver": ["lbfgs", "liblinear"]}}
-        },
-        "Linear Regression": {
-            "Regression": {"model_class": LinearRegression, "params": {}, "grid_params": {}}
-        },
-        "SVM": {
-            "Regression": {"model_class": SVR, "params": {"kernel": "rbf"}, "grid_params": {"C": [0.1, 1, 10], "gamma": ["scale", "auto"]}},
-            "Binary Classification": {"model_class": SVC, "params": {"kernel": "rbf", "random_state": 42}, "grid_params": {"C": [0.1, 1, 10], "gamma": ["scale", "auto"]}},
-            "Multi-Class": {"model_class": SVC, "params": {"kernel": "rbf", "random_state": 42}, "grid_params": {"C": [0.1, 1, 10], "gamma": ["scale", "auto"]}}
-        },
-        "K-Means": {
-            "Clustering": {"model_class": KMeans, "params": {"n_clusters": 3, "random_state": 42},
-                           "grid_params": {"n_clusters": [2, 3, 4, 5]}}
-        },
-        "DBSCAN": {
-            "Clustering": {"model_class": DBSCAN, "params": {"eps": 0.5, "min_samples": 5},
-                           "grid_params": {"eps": [0.3, 0.5, 0.7], "min_samples": [3, 5, 10]}}
-        },
-        "Gaussian Mixture": {
-            "Clustering": {"model_class": GaussianMixture, "params": {"n_components": 3, "random_state": 42},
-                           "grid_params": {"n_components": [2, 3, 4, 5]}}
-        }
-    }
-    return configs.get(model_type, {}).get(problem_type, {"model_class": None, "params": {}, "grid_params": {}})
-
-def preprocess_data(X_train, X_test, numerical_features, categorical_features):
-    numeric_transformer = Pipeline(steps=[
-        ('imputer', SimpleImputer(strategy='mean')),
-        ('scaler', StandardScaler())])
-    categorical_transformer = Pipeline(steps=[
-        ('imputer', SimpleImputer(strategy='most_frequent')),
-        ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))])
-    preprocessor = ColumnTransformer(
-        transformers=[
-            ('num', numeric_transformer, numerical_features),
-            ('cat', categorical_transformer, categorical_features)],
-        remainder='drop')
-    X_train_processed = preprocessor.fit_transform(X_train)
-    X_test_processed = preprocessor.transform(X_test)
-    if categorical_features:
-        onehot_encoder = preprocessor.named_transformers_['cat'].named_steps['onehot']
-        categorical_feature_names = onehot_encoder.get_feature_names_out(categorical_features)
-        feature_names = numerical_features + list(categorical_feature_names)
-    else:
-        feature_names = numerical_features
-    return X_train_processed, X_test_processed, feature_names, preprocessor
-
-def build_neural_network(input_shape, output_units, problem_type, layers_config, optimizer_name="Adam", learning_rate=0.001):
-    model = keras.Sequential()
-    model.add(keras.layers.InputLayer(input_shape=input_shape))
-    for layer in layers_config:
-        if layer['type'] == 'dense':
-            model.add(keras.layers.Dense(layer['units'], activation=layer['activation']))
-        elif layer['type'] == 'dropout':
-            model.add(keras.layers.Dropout(layer['rate']))
-        elif layer['type'] == 'conv2d':
-            model.add(keras.layers.Conv2D(layer['filters'], tuple(layer['kernel_size']), activation=layer['activation'], padding='same'))
-        elif layer['type'] == 'maxpooling2d':
-            model.add(keras.layers.MaxPooling2D(pool_size=tuple(layer['pool_size'])))
-        elif layer['type'] == 'flatten':
-            model.add(keras.layers.Flatten())
-    if problem_type == "Regression":
-        model.add(keras.layers.Dense(1))
-        loss_function = "mse"
-        metrics = ["mse"]
-    elif problem_type == "Binary Classification":
-        model.add(keras.layers.Dense(1, activation='sigmoid'))
-        loss_function = "binary_crossentropy"
-        metrics = ["accuracy"]
-    elif problem_type == "Multi-Class" or problem_type == "Image Classification":
-        model.add(keras.layers.Dense(output_units, activation='softmax'))
-        loss_function = "sparse_categorical_crossentropy" if problem_type == "Image Classification" else "categorical_crossentropy"
-        metrics = ["accuracy"]
-    else:
-        raise ValueError("Unsupported problem type")
-    optimizer = {"Adam": keras.optimizers.Adam, "SGD": keras.optimizers.SGD, "RMSprop": keras.optimizers.RMSprop}.get(optimizer_name)(learning_rate=learning_rate)
-    model.compile(optimizer=optimizer, loss=loss_function, metrics=metrics)
-    return model
-
-def build_autoencoder(input_shape, encoding_dim, layers_config, autoencoder_type="Standard", optimizer_name="Adam", learning_rate=0.001):
-    if autoencoder_type == "Variational":
-        inputs = keras.layers.Input(shape=input_shape)
-        x = keras.layers.Flatten()(inputs) if len(input_shape) > 1 else inputs
-        for layer in layers_config:
-            if layer['type'] == 'dense':
-                x = keras.layers.Dense(layer['units'], activation=layer['activation'])(x)
-        z_mean = keras.layers.Dense(encoding_dim, name='z_mean')(x)
-        z_log_var = keras.layers.Dense(encoding_dim, name='z_log_var')(x)
-        
-        def sampling(args):
-            z_mean, z_log_var = args
-            epsilon = keras.backend.random_normal(shape=(keras.backend.shape(z_mean)[0], encoding_dim))
-            return z_mean + keras.backend.exp(0.5 * z_log_var) * epsilon
-        
-        z = keras.layers.Lambda(sampling, name='z')([z_mean, z_log_var])
-        encoder = keras.Model(inputs, [z_mean, z_log_var, z], name='encoder')
-        
-        decoder_input = keras.layers.Input(shape=(encoding_dim,))
-        x = decoder_input
-        for layer in reversed(layers_config):
-            if layer['type'] == 'dense':
-                x = keras.layers.Dense(layer['units'], activation=layer['activation'])(x)
-        x = keras.layers.Dense(np.prod(input_shape), activation='sigmoid')(x)
-        outputs = keras.layers.Reshape(input_shape)(x) if len(input_shape) > 1 else x
-        decoder = keras.Model(decoder_input, outputs, name='decoder')
-        
-        vae_outputs = decoder(encoder(inputs)[2])
-        autoencoder = keras.Model(inputs, vae_outputs, name='vae')
-        
-        reconstruction_loss = keras.losses.binary_crossentropy(keras.backend.flatten(inputs), keras.backend.flatten(vae_outputs))
-        reconstruction_loss *= np.prod(input_shape)
-        kl_loss = 1 + z_log_var - keras.backend.square(z_mean) - keras.backend.exp(z_log_var)
-        kl_loss = keras.backend.sum(kl_loss, axis=-1) * -0.5
-        vae_loss = keras.backend.mean(reconstruction_loss + kl_loss)
-        autoencoder.add_loss(vae_loss)
-    else:  # Standard or Denoising
-        encoder = keras.Sequential([keras.layers.InputLayer(input_shape=input_shape)])
-        for layer in layers_config:
-            if layer['type'] == 'dense':
-                encoder.add(keras.layers.Dense(layer['units'], activation=layer['activation']))
-            elif layer['type'] == 'dropout':
-                encoder.add(keras.layers.Dropout(layer['rate']))
-        encoder.add(keras.layers.Dense(encoding_dim, activation='relu', name='encoded'))
-        
-        decoder = keras.Sequential([keras.layers.InputLayer(input_shape=(encoding_dim,))])
-        for layer in reversed(layers_config):
-            if layer['type'] == 'dense':
-                decoder.add(keras.layers.Dense(layer['units'], activation=layer['activation']))
-        decoder.add(keras.layers.Dense(np.prod(input_shape), activation='sigmoid'))
-        decoder.add(keras.layers.Reshape(input_shape) if len(input_shape) > 1 else keras.layers.Lambda(lambda x: x))
-        
-        autoencoder_input = keras.layers.Input(shape=input_shape)
-        encoded = encoder(autoencoder_input)
-        decoded = decoder(encoded)
-        autoencoder = keras.Model(autoencoder_input, decoded)
-    
-    optimizer = {"Adam": keras.optimizers.Adam, "SGD": keras.optimizers.SGD, "RMSprop": keras.optimizers.RMSprop}.get(optimizer_name)(learning_rate=learning_rate)
-    autoencoder.compile(optimizer=optimizer, loss='mse', metrics=['mse'])
-    return autoencoder, encoder, decoder
-
-class StreamlitCallback(keras.callbacks.Callback):
-    def __init__(self, placeholder):
-        super().__init__()
-        self.placeholder = placeholder
-        self.epoch_data = []
-
-    def on_epoch_end(self, epoch, logs=None):
-        # Append the logs for the current epoch
-        self.epoch_data.append(logs)
-        
-        # Create a DataFrame from the logs
-        df = pd.DataFrame(self.epoch_data)
-        
-        # Create the Plotly figure
-        fig = go.Figure()
-        
-        # Add training loss trace
-        fig.add_trace(go.Scatter(
-            x=df.index, y=df['loss'], mode='lines', name='Training Loss'
-        ))
-        
-        # Add validation loss trace (if available)
-        if 'val_loss' in df.columns:
-            fig.add_trace(go.Scatter(
-                x=df.index, y=df['val_loss'], mode='lines', name='Validation Loss'
-            ))
-        
-        # Add metric trace (e.g., accuracy or MSE)
-        metric_name = 'accuracy' if 'accuracy' in df.columns else 'mse'
-        if metric_name in df.columns:
-            fig.add_trace(go.Scatter(
-                x=df.index, y=df[metric_name], mode='lines', name=metric_name.capitalize()
-            ))
-        
-        # Update the layout
-        fig.update_layout(
-            title="Training Progress",
-            xaxis_title="Epoch",
-            yaxis_title="Value",
-            legend_title="Metrics"
-        )
-        
-        # Update the placeholder with the new figure
-        self.placeholder.plotly_chart(fig, use_container_width=True)
-
-def train_model(model, X_train, y_train, X_test, y_test, epochs, batch_size, problem_type, input_data=None, target_data=None, do_grid_search=False, params=None, grid_params=None):
+def train_model(model, X_train, y_train, X_test, y_test, epochs, batch_size, problem_type, input_data=None, target_data=None, do_grid_search=False, params=None, grid_params=None, training_placeholder=None):
+    """Train the model and optionally display live training progress for Keras models."""
     start_time = time.time()
     history = None
     
-    # Create a placeholder for the training progress chart
-    training_placeholder = st.empty()
-    
     if isinstance(model, keras.Model):
-        streamlit_callback = StreamlitCallback(training_placeholder)
-        history = model.fit(
-            input_data if input_data is not None else X_train,
-            target_data if target_data is not None else y_train,
-            epochs=epochs,
-            batch_size=batch_size,
-            validation_data=(X_test, y_test if y_test is not None else X_test),
-            verbose=0,
-            callbacks=[streamlit_callback]
-        )
+        if training_placeholder is not None:
+            streamlit_callback = StreamlitCallback(training_placeholder)
+            history = model.fit(
+                input_data if input_data is not None else X_train,
+                target_data if target_data is not None else y_train,
+                epochs=epochs,
+                batch_size=batch_size,
+                validation_data=(X_test, y_test if y_test is not None else X_test),
+                verbose=0,
+                callbacks=[streamlit_callback]
+            )
+        else:
+            history = model.fit(
+                input_data if input_data is not None else X_train,
+                target_data if target_data is not None else y_train,
+                epochs=epochs,
+                batch_size=batch_size,
+                validation_data=(X_test, y_test if y_test is not None else X_test),
+                verbose=1
+            )
     else:
         if do_grid_search and grid_params:
             grid_search = GridSearchCV(model, grid_params, cv=3, n_jobs=-1, scoring='accuracy' if problem_type in ["Binary Classification", "Multi-Class"] else 'neg_mean_squared_error')
@@ -322,464 +37,106 @@ def train_model(model, X_train, y_train, X_test, y_test, epochs, batch_size, pro
     training_time = time.time() - start_time
     return history, model, training_time
 
-def evaluate_model(model, X_test, y_test, problem_type, le=None, encoder=None):
-    y_pred = model.predict(X_test)
-    metrics = {}
-    if problem_type == "Regression":
-        metrics['mse'] = mean_squared_error(y_test, y_pred)
-        metrics['mae'] = mean_absolute_error(y_test, y_pred)
-        metrics['rmse'] = np.sqrt(metrics['mse'])
-        metrics['r2'] = r2_score(y_test, y_pred)
-        return metrics, y_pred.flatten()
-    elif problem_type in ["Binary Classification", "Multi-Class", "Image Classification"]:
-        if problem_type == "Image Classification":
-            y_pred_classes = np.argmax(y_pred, axis=1)
-            y_test_classes = y_test
-        else:
-            y_pred_classes = (y_pred > 0.5).astype(int).flatten() if problem_type == "Binary Classification" else np.argmax(y_pred, axis=1)
-            y_test_classes = y_test if problem_type == "Binary Classification" else np.argmax(y_test, axis=1)
-        metrics['accuracy'] = accuracy_score(y_test_classes, y_pred_classes)
-        metrics['precision'] = precision_score(y_test_classes, y_pred_classes, average='weighted', zero_division=0)
-        metrics['recall'] = recall_score(y_test_classes, y_pred_classes, average='weighted', zero_division=0)
-        metrics['f1'] = f1_score(y_test_classes, y_pred_classes, average='weighted', zero_division=0)
-        return metrics, y_pred_classes
-    elif problem_type == "Clustering":
-        labels = model.labels_ if hasattr(model, 'labels_') else model.predict(X_test)
-        metrics["n_clusters"] = len(np.unique(labels))
-        if len(np.unique(labels)) > 1:
-            metrics["silhouette"] = silhouette_score(X_test, labels)
-        return metrics, labels
-    elif problem_type == "Compression":
-        metrics['mse'] = mean_squared_error(X_test, y_pred)
-        metrics['mae'] = mean_absolute_error(X_test, y_pred)
-        metrics['rmse'] = np.sqrt(metrics['mse'])
-        compressed_data = encoder.predict(X_test) if encoder else None
-        return metrics, y_pred, compressed_data
-
-def save_model(model, preprocessor, features, target, problem_type, filename="model.pkl"):
-    model_data = {
-        'model': model,
-        'preprocessor': preprocessor,
-        'features': features,
-        'target': target,
-        'problem_type': problem_type,
-        'timestamp': time.strftime("%Y%m%d_%H%M%S")
-    }
-    if isinstance(model, keras.Model):
-        model.save("temp_model.h5")
-        model_data['model_path'] = "temp_model.h5"
-    joblib.dump(model_data, filename)
-    return filename
-
-def load_model(model_file):
-    model_data = joblib.load(model_file)
-    if 'model_path' in model_data:
-        model_data['model'] = keras.models.load_model(model_data['model_path'])
-    return model_data
-
-# Sidebar Navigation
-with st.sidebar:
-    st.title("🔮 Neural-Vision Enhanced")
-    st.markdown("Your AI-powered model toolbox.")
-    st.markdown("---")
-    app_mode = st.selectbox("Navigation", ["Data Upload", "Model Training", "Validation & Exploration"])
-    data_type = st.selectbox("Data Type", ["Tabular", "Image"])
-    st.markdown("---")
-    st.markdown("**Dependencies**: `tensorflow`, `shap`, `umap-learn`, `joblib`, `scikit-learn`, `plotly`, `xgboost`, `pillow`")
-    st.markdown("Created by Calvin Allen-Crawford | v1.3 | © 2025")
-
-# Main App Sections
-if app_mode == "Data Upload":
-    st.title("📤 Data Upload")
-    col1, col2, col3 = st.columns([1, 2, 1])
-    with col2:
+# In the "Model Training" section, update the training block:
+if st.button("Train Model"):
+    with st.spinner("Preparing data..."):
         if data_type == "Tabular":
-            uploaded_file = st.file_uploader("Upload CSV Dataset", type=["csv"])
-            if uploaded_file:
-                df = pd.read_csv(uploaded_file)
-                st.session_state.df = df
-                st.write("---")
-                st.subheader("Dataset Preview")
-                st.dataframe(df.head(10))
-                st.write("---")
-                st.subheader("Statistics")
-                col1, col2, col3 = st.columns(3)
-                with col1: st.metric("Rows", df.shape[0])
-                with col2: st.metric("Columns", df.shape[1])
-                with col3: st.metric("Missing Values", df.isna().sum().sum())
+            X = df[features]
+            y = df[target] if problem_type not in ["Clustering", "Compression"] else None
+            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) if problem_type not in ["Clustering", "Compression"] else (X, X.copy(), None, None)
+            numerical_features = X.select_dtypes(include=np.number).columns.tolist()
+            categorical_features = X.select_dtypes(exclude=np.number).columns.tolist()
+            X_train_processed, X_test_processed, feature_names, preprocessor = preprocess_data(X_train, X_test, numerical_features, categorical_features)
+            le = None
+            if problem_type in ["Binary Classification", "Multi-Class"] and y is not None:
+                le = LabelEncoder()
+                y_train = le.fit_transform(y_train)
+                y_test = le.transform(y_test)
+                if problem_type == "Multi-Class":
+                    y_train = tf.keras.utils.to_categorical(y_train)
+                    y_test = tf.keras.utils.to_categorical(y_test)
         else:  # Image
-            uploaded_file = st.file_uploader("Upload Zip File with Images (Max 5GB)", type=["zip"])
-            if uploaded_file:
-                # Save uploaded file temporarily to check size
-                with open("temp_upload.zip", "wb") as f:
-                    f.write(uploaded_file.getbuffer())
-                try:
-                    problem_type = st.selectbox("Problem Type for Image Data", ["Image Classification", "Compression", "Clustering"])
-                    images, labels, class_names = load_image_dataset("temp_upload.zip", problem_type=problem_type)
-                    st.session_state.images = images
-                    st.session_state.labels = labels
-                    st.session_state.class_names = class_names if problem_type == "Image Classification" else None
-                    st.write(f"Loaded {len(images)} images.")
-                    if problem_type == "Image Classification":
-                        st.write(f"Classes: {class_names}")
-                    st.image(images[:5], caption=["Sample " + str(i+1) for i in range(min(5, len(images)))], width=100)
-                except ValueError as e:
-                    st.error(str(e))
-                finally:
-                    os.remove("temp_upload.zip")
-
-elif app_mode == "Model Training":
-    st.title("🧠 Model Training")
-    if data_type == "Tabular" and 'df' not in st.session_state:
-        st.warning("Please upload a tabular dataset first.")
-        st.stop()
-    elif data_type == "Image" and 'images' not in st.session_state:
-        st.warning("Please upload an image dataset first.")
-        st.stop()
-
-    if data_type == "Tabular":
-        df = st.session_state.df
-        problem_type = st.selectbox("Problem Type", ["Regression", "Binary Classification", "Multi-Class", "Clustering", "Compression"])
-        features = st.multiselect("Select Features", df.columns)
-        target = st.selectbox("Select Target", df.columns) if problem_type not in ["Clustering", "Compression"] else None
-    else:
-        problem_type = st.selectbox("Problem Type", ["Image Classification", "Compression", "Clustering"])
-        features = ["images"]
-        target = "labels" if problem_type == "Image Classification" else None
-
-    if problem_type not in ["Clustering", "Compression"] and data_type == "Tabular" and target:
-        unique_target_values = df[target].nunique()
-        if problem_type == "Binary Classification" and unique_target_values != 2:
-            st.error("Binary Classification requires exactly 2 unique target values.")
-            st.stop()
-        elif problem_type == "Multi-Class" and unique_target_values < 2:
-            st.error("Multi-Class Classification requires at least 2 unique target values.")
-            st.stop()
-        elif problem_type == "Regression" and not pd.api.types.is_numeric_dtype(df[target]):
-            st.error("Regression requires a numerical target variable.")
-            st.stop()
-
-    model_types = {
-        "Regression": ["Neural Network", "Random Forest", "XGBoost", "Linear Regression", "SVM"],
-        "Binary Classification": ["Neural Network", "Random Forest", "XGBoost", "Logistic Regression", "SVM"],
-        "Multi-Class": ["Neural Network", "Random Forest", "XGBoost", "SVM"],
-        "Clustering": ["K-Means", "DBSCAN", "Gaussian Mixture"],
-        "Compression": ["Autoencoder"],
-        "Image Classification": ["Neural Network", "SVM"]
-    }[problem_type]
-    model_type = st.selectbox("Model Type", model_types)
-
-    if model_type == "Neural Network":
-        st.subheader("Neural Network Configuration")
-        optimizer_name = st.selectbox("Optimizer", ["Adam", "SGD", "RMSprop"])
-        layers_config = st.session_state.get('layers_config', [])
-        layer_type = st.selectbox("Layer Type", ["Dense", "Dropout"] if data_type == "Tabular" else ["Conv2D", "MaxPooling2D", "Flatten", "Dense", "Dropout"])
-        if layer_type == "Dense":
-            units = st.number_input("Units", min_value=1, value=64)
-            activation = st.selectbox("Activation", ["relu", "sigmoid", "tanh"])
-            if st.button("Add Layer"):
-                layers_config.append({"type": "dense", "units": units, "activation": activation})
-        elif layer_type == "Dropout":
-            rate = st.number_input("Dropout Rate", 0.0, 1.0, 0.2)
-            if st.button("Add Layer"):
-                layers_config.append({"type": "dropout", "rate": rate})
-        elif layer_type == "Conv2D":
-            filters = st.number_input("Filters", min_value=1, value=32)
-            kernel_size = st.multiselect("Kernel Size", options=[1, 3, 5], default=[3])
-            activation = st.selectbox("Activation", ["relu", "sigmoid", "tanh"])
-            if st.button("Add Layer"):
-                layers_config.append({"type": "conv2d", "filters": filters, "kernel_size": kernel_size, "activation": activation})
-        elif layer_type == "MaxPooling2D":
-            pool_size = st.multiselect("Pool Size", options=[2, 3], default=[2])
-            if st.button("Add Layer"):
-                layers_config.append({"type": "maxpooling2d", "pool_size": pool_size})
-        elif layer_type == "Flatten":
-            if st.button("Add Layer"):
-                layers_config.append({"type": "flatten"})
-        if layers_config:
-            st.write("Current Layers:", layers_config)
-            if st.button("Clear Layers"):
-                layers_config.clear()
-                st.session_state.layers_config = layers_config
-                st.rerun()
-        st.session_state.layers_config = layers_config
-    elif model_type == "Autoencoder":
-        st.subheader("Autoencoder Configuration")
-        encoding_dim = st.number_input("Encoding Dimension", min_value=1, value=32)
-        optimizer_name = st.selectbox("Optimizer", ["Adam", "SGD", "RMSprop"])
-        autoencoder_type = st.selectbox("Autoencoder Type", ["Standard", "Variational", "Denoising"])
-        if autoencoder_type == "Denoising":
-            noise_level = st.slider("Noise Level", 0.0, 1.0, 0.1)
-        layers_config = st.session_state.get('layers_config', [])
-        layer_type = st.selectbox("Layer Type (Encoder)", ["Dense", "Dropout"])
-        if layer_type == "Dense":
-            units = st.number_input("Units", min_value=1, value=64)
-            activation = st.selectbox("Activation", ["relu", "sigmoid", "tanh"])
-            if st.button("Add Layer"):
-                layers_config.append({"type": "dense", "units": units, "activation": activation})
-        elif layer_type == "Dropout":
-            rate = st.number_input("Dropout Rate", 0.0, 1.0, 0.2)
-            if st.button("Add Layer"):
-                layers_config.append({"type": "dropout", "rate": rate})
-        if layers_config:
-            st.write("Current Encoder Layers:", layers_config)
-            if st.button("Clear Layers"):
-                layers_config.clear()
-                st.session_state.layers_config = layers_config
-                st.rerun()
-        st.session_state.layers_config = layers_config
-    else:
-        st.subheader("Model Hyperparameters")
-        config = get_model_config(model_type, problem_type)
-        params = {}
-        for param_name, param_values in config["grid_params"].items():
-            if isinstance(param_values[0], (int, float)) and len(param_values) > 2:
-                slider_value = st.slider(param_name, min_value=float(min(param_values)), max_value=float(max(param_values)), value=float(param_values[1]))
-                if param_name in {'n_estimators', 'n_clusters', 'min_samples', 'n_components', 'max_depth'}:
-                    params[param_name] = int(slider_value)
-                else:
-                    params[param_name] = slider_value
-            else:
-                params[param_name] = st.selectbox(param_name, param_values)
-        do_grid_search = st.checkbox("Use Grid Search for Tuning", value=False)
-
-    col1, col2, col3 = st.columns(3)
-    with col1: epochs = st.number_input("Epochs", min_value=1, value=10) if model_type in ["Neural Network", "Autoencoder"] else 10
-    with col2: batch_size = st.number_input("Batch Size", min_value=1, value=32) if model_type in ["Neural Network", "Autoencoder"] else 32
-    with col3: learning_rate = st.number_input("Learning Rate", min_value=0.0, value=0.001, step=0.0001) if model_type in ["Neural Network", "Autoencoder"] else 0.001
-
-    uploaded_model = st.file_uploader("Upload Pre-trained Model (.h5)", type=["h5"]) if model_type in ["Neural Network", "Autoencoder"] else None
-    base_model = keras.models.load_model(uploaded_model) if uploaded_model else None
-
-    if st.button("Train Model"):
-        with st.spinner("Preparing data..."):
-            if data_type == "Tabular":
-                X = df[features]
-                y = df[target] if problem_type not in ["Clustering", "Compression"] else None
-                X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) if problem_type not in ["Clustering", "Compression"] else (X, X.copy(), None, None)
-                numerical_features = X.select_dtypes(include=np.number).columns.tolist()
-                categorical_features = X.select_dtypes(exclude=np.number).columns.tolist()
-                X_train_processed, X_test_processed, feature_names, preprocessor = preprocess_data(X_train, X_test, numerical_features, categorical_features)
-                le = None
-                if problem_type in ["Binary Classification", "Multi-Class"] and y is not None:
-                    le = LabelEncoder()
-                    y_train = le.fit_transform(y_train)
-                    y_test = le.transform(y_test)
-                    if problem_type == "Multi-Class":
-                        y_train = tf.keras.utils.to_categorical(y_train)
-                        y_test = tf.keras.utils.to_categorical(y_test)
-            else:  # Image
-                X_train, X_test, y_train, y_test = train_test_split(st.session_state.images, st.session_state.labels, test_size=0.2, random_state=42) if problem_type == "Image Classification" else (st.session_state.images, st.session_state.images.copy(), None, None)
-                preprocessor = None
-                feature_names = ["image_features"]
-                le = None
-
-        with st.spinner("Training model..."):
-            training_placeholder = st.empty()
-            if model_type == "Neural Network":
-                if not layers_config and not base_model:
-                    st.error("Please add layers or upload a pre-trained model.")
-                    st.stop()
-                input_shape = (X_train_processed.shape[1],) if data_type == "Tabular" else (224, 224, 3)
-                output_units = len(st.session_state.class_names) if problem_type == "Image Classification" else (y_train.shape[1] if problem_type == "Multi-Class" else 1)
-                model = base_model if base_model else build_neural_network(input_shape, output_units, problem_type, layers_config, optimizer_name, learning_rate)
-                history, model, training_time = train_model(model, X_train_processed if data_type == "Tabular" else X_train, y_train, 
-                                                            X_test_processed if data_type == "Tabular" else X_test, y_test, 
-                                                            epochs, batch_size, problem_type, training_placeholder=training_placeholder)
-            elif model_type == "Autoencoder":
-                if not layers_config and not base_model:
-                    st.error("Please add layers to the encoder or upload a pre-trained model.")
-                    st.stop()
-                input_shape = (X_train_processed.shape[1],) if data_type == "Tabular" else (224, 224, 3)
-                model, encoder, decoder = (base_model, None, None) if base_model else build_autoencoder(input_shape, encoding_dim, layers_config, autoencoder_type, optimizer_name, learning_rate)
-                if autoencoder_type == "Denoising":
-                    X_train_noisy = X_train_processed + noise_level * np.random.normal(size=X_train_processed.shape) if data_type == "Tabular" else X_train + noise_level * np.random.normal(size=X_train.shape)
-                    input_data = X_train_noisy
-                    target_data = X_train_processed if data_type == "Tabular" else X_train
-                else:
-                    input_data = X_train_processed if data_type == "Tabular" else X_train
-                    target_data = X_train_processed if data_type == "Tabular" else X_train
-                history, model, training_time = train_model(model, X_train_processed if data_type == "Tabular" else X_train, None, 
-                                                            X_test_processed if data_type == "Tabular" else X_test, None, 
-                                                            epochs, batch_size, problem_type, input_data=input_data, target_data=target_data, 
-                                                            training_placeholder=training_placeholder)
-                st.session_state.encoder = encoder
-                st.session_state.decoder = decoder
-            else:
-                config = get_model_config(model_type, problem_type)
-                model = config['model_class'](**config['params'])
-                X_train_flat = X_train_processed if data_type == "Tabular" else X_train.reshape(X_train.shape[0], -1)
-                X_test_flat = X_test_processed if data_type == "Tabular" else X_test.reshape(X_test.shape[0], -1)
-                history, model, training_time = train_model(model, X_train_flat, y_train, X_test_flat, y_test, epochs, batch_size, problem_type,
-                                                            do_grid_search=do_grid_search, params=params, grid_params=config['grid_params'], 
-                                                            training_placeholder=training_placeholder)
-
-            st.session_state.model = model
-            st.session_state.preprocessor = preprocessor
-            st.session_state.features = features
-            st.session_state.target = target
-            st.session_state.problem_type = problem_type
-            st.session_state.le = le
-
-            filename = save_model(model, preprocessor, features, target, problem_type)
-            with open(filename, 'rb') as f:
-                st.download_button("Download Model", f, file_name=filename)
-            st.success(f"Model trained in {training_time:.2f}s and saved!")
-
-elif app_mode == "Validation & Exploration":
-    st.title("🔍 Validation & Exploration")
-    if data_type == "Tabular" and ('model' not in st.session_state or 'df' not in st.session_state):
-        st.warning("Please upload a tabular dataset and train a model first.")
-        st.stop()
-    elif data_type == "Image" and ('model' not in st.session_state or 'images' not in st.session_state):
-        st.warning("Please upload an image dataset and train a model first.")
-        st.stop()
-
-    if data_type == "Tabular":
-        df = st.session_state.df
-        X = df[st.session_state.features]
-        y = df[st.session_state.target] if st.session_state.problem_type not in ["Clustering", "Compression"] else None
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) if st.session_state.problem_type not in ["Clustering", "Compression"] else (X, X.copy(), None, None)
-        numerical_features = X.select_dtypes(include=np.number).columns.tolist()
-        categorical_features = X.select_dtypes(exclude=np.number).columns.tolist()
-        X_train_processed, X_test_processed, feature_names, _ = preprocess_data(X_train, X_test, numerical_features, categorical_features)
-        if st.session_state.problem_type in ["Binary Classification", "Multi-Class"] and y is not None:
-            y_train = st.session_state.le.transform(y_train) if st.session_state.le else y_train
-            y_test = st.session_state.le.transform(y_test) if st.session_state.le else y_test
-            if st.session_state.problem_type == "Multi-Class":
-                y_train = tf.keras.utils.to_categorical(y_train)
-                y_test = tf.keras.utils.to_categorical(y_test)
-    else:
-        X_train, X_test, y_train, y_test = train_test_split(st.session_state.images, st.session_state.labels, test_size=0.2, random_state=42) if st.session_state.problem_type == "Image Classification" else (st.session_state.images, st.session_state.images.copy(), None, None)
-        X_train_processed, X_test_processed = X_train, X_test
-        feature_names = ["image_features"]
-        if st.session_state.problem_type == "Image Classification":
+            X_train, X_test, y_train, y_test = train_test_split(st.session_state.images, st.session_state.labels, test_size=0.2, random_state=42) if problem_type == "Image Classification" else (st.session_state.images, st.session_state.images.copy(), None, None)
+            preprocessor = None
+            feature_names = ["image_features"]
             le = None
 
-    model = st.session_state.model
-    problem_type = st.session_state.problem_type
-    encoder = st.session_state.get('encoder', None)
-
-    # Validation
-    st.subheader("Model Validation")
-    if problem_type == "Compression":
-        metrics, y_pred, compressed_data = evaluate_model(model, X_test_processed, None, problem_type, None, encoder)
-    else:
-        metrics, y_pred = evaluate_model(model, X_test_processed, y_test, problem_type, st.session_state.le if data_type == "Tabular" else None)
-
-    col1, col2 = st.columns(2)
-    with col1:
-        for metric, value in metrics.items():
-            st.metric(metric, f"{value:.4f}" if isinstance(value, float) else value)
-    with col2:
-        if problem_type == "Regression":
-            fig = px.scatter(x=y_test, y=y_pred, labels={"x": "Actual", "y": "Predicted"}, title="Predicted vs Actual")
-            st.plotly_chart(fig)
-        elif problem_type in ["Binary Classification", "Image Classification"]:
-            if problem_type == "Binary Classification":
-                y_pred_proba = model.predict_proba(X_test_processed)[:, 1] if hasattr(model, 'predict_proba') else y_pred
-                fpr, tpr, _ = roc_curve(y_test, y_pred_proba)
-                roc_auc = auc(fpr, tpr)
-                fig = px.area(x=fpr, y=tpr, title=f"ROC Curve (AUC = {roc_auc:.2f})", labels={"x": "False Positive Rate", "y": "True Positive Rate"})
-                st.plotly_chart(fig)
+    with st.spinner("Training model..."):
+        training_placeholder = st.empty()  # Define the placeholder here
+        if model_type == "Neural Network":
+            if not layers_config and not base_model:
+                st.error("Please add layers or upload a pre-trained model.")
+                st.stop()
+            input_shape = (X_train_processed.shape[1],) if data_type == "Tabular" else (224, 224, 3)
+            output_units = len(st.session_state.class_names) if problem_type == "Image Classification" else (y_train.shape[1] if problem_type == "Multi-Class" else 1)
+            model = base_model if base_model else build_neural_network(input_shape, output_units, problem_type, layers_config, optimizer_name, learning_rate)
+            history, model, training_time = train_model(
+                model, 
+                X_train_processed if data_type == "Tabular" else X_train, 
+                y_train, 
+                X_test_processed if data_type == "Tabular" else X_test, 
+                y_test, 
+                epochs, 
+                batch_size, 
+                problem_type, 
+                training_placeholder=training_placeholder
+            )
+        elif model_type == "Autoencoder":
+            if not layers_config and not base_model:
+                st.error("Please add layers to the encoder or upload a pre-trained model.")
+                st.stop()
+            input_shape = (X_train_processed.shape[1],) if data_type == "Tabular" else (224, 224, 3)
+            model, encoder, decoder = (base_model, None, None) if base_model else build_autoencoder(input_shape, encoding_dim, layers_config, autoencoder_type, optimizer_name, learning_rate)
+            if autoencoder_type == "Denoising":
+                X_train_noisy = X_train_processed + noise_level * np.random.normal(size=X_train_processed.shape) if data_type == "Tabular" else X_train + noise_level * np.random.normal(size=X_train.shape)
+                input_data = X_train_noisy
+                target_data = X_train_processed if data_type == "Tabular" else X_train
             else:
-                cm = np.zeros((len(st.session_state.class_names), len(st.session_state.class_names)))
-                for i, j in zip(y_test, y_pred):
-                    cm[i, j] += 1
-                fig = px.imshow(cm, title="Confusion Matrix", labels={"x": "Predicted", "y": "Actual"})
-                st.plotly_chart(fig)
-                report = classification_report(y_test, y_pred, target_names=st.session_state.class_names, zero_division=0)
-                st.text("Classification Report:\n" + report)
-        elif problem_type == "Multi-Class":
-            y_pred_classes = np.argmax(model.predict(X_test_processed), axis=1)
-            y_test_classes = np.argmax(y_test, axis=1)
-            cm = np.zeros((y_train.shape[1], y_train.shape[1]))
-            for i, j in zip(y_test_classes, y_pred_classes):
-                cm[i, j] += 1
-            fig = px.imshow(cm, title="Confusion Matrix", labels={"x": "Predicted", "y": "Actual"})
-            st.plotly_chart(fig)
-            report = classification_report(y_test_classes, y_pred_classes, target_names=st.session_state.le.classes_ if st.session_state.le else [str(i) for i in range(y_train.shape[1])], zero_division=0)
-            st.text("Classification Report:\n" + report)
-        elif problem_type == "Clustering":
-            labels = y_pred
-            fig = px.scatter(x=X_test_processed[:, 0] if X_test_processed.shape[-1] == 1 else X_test_processed.reshape(X_test_processed.shape[0], -1)[:, 0], 
-                            y=X_test_processed[:, 1] if X_test_processed.shape[-1] == 1 else X_test_processed.reshape(X_test_processed.shape[0], -1)[:, 1], 
-                            color=labels, title="Cluster Visualization")
-            st.plotly_chart(fig)
-        elif problem_type == "Compression":
-            st.image([X_test_processed[0], y_pred[0]], caption=["Original", "Reconstructed"], width=200) if data_type == "Image" else None
-            fig = px.scatter(x=X_test_processed.flatten()[:1000], y=y_pred.flatten()[:1000], labels={"x": "Original", "y": "Reconstructed"}, title="Original vs Reconstructed (First 1000 Values)")
-            st.plotly_chart(fig)
-
-    # Dimensionality Reduction
-    st.subheader("Dimensionality Reduction")
-    methods = ["PCA", "SVD", "t-SNE", "UMAP"]
-    if problem_type == "Compression" and 'encoder' in st.session_state:
-        methods.append("Autoencoder")
-    method = st.selectbox("Method", methods)
-    n_components = st.slider("Components", 2, min(10 if data_type == "Tabular" else X_train_processed.shape[1], 10), 2)
-
-    if method == "Autoencoder" and 'encoder' in st.session_state:
-        X_reduced = st.session_state.encoder.predict(X_train_processed)
-        if X_reduced.shape[1] < n_components:
-            st.warning(f"Autoencoder encoding dimension is {X_reduced.shape[1]}, using that instead of {n_components}.")
-            n_components = X_reduced.shape[1]
-    else:
-        X_flat = X_train_processed if data_type == "Tabular" else X_train_processed.reshape(X_train_processed.shape[0], -1)
-        if method == "PCA":
-            reducer = PCA(n_components=n_components)
-            X_reduced = reducer.fit_transform(X_flat)
-            fig = px.bar(x=range(n_components), y=reducer.explained_variance_ratio_, title="Explained Variance Ratio")
-            st.plotly_chart(fig)
-        elif method == "SVD":
-            reducer = TruncatedSVD(n_components=n_components)
-            X_reduced = reducer.fit_transform(X_flat)
-            fig = px.bar(x=range(n_components), y=reducer.explained_variance_ratio_, title="Explained Variance Ratio")
-            st.plotly_chart(fig)
-        elif method == "t-SNE":
-            with st.spinner("Running t-SNE..."):
-                X_reduced = TSNE(n_components=n_components, random_state=42).fit_transform(X_flat)
-        elif method == "UMAP":
-            with st.spinner("Running UMAP..."):
-                X_reduced = umap.UMAP(n_components=n_components, random_state=42).fit_transform(X_flat)
-
-    if n_components >= 2:
-        if n_components == 2:
-            fig = px.scatter(x=X_reduced[:, 0], y=X_reduced[:, 1], color=y_train if problem_type not in ["Clustering", "Compression"] else y_pred,
-                            title=f"{method} Visualization")
-        elif n_components == 3:
-            fig = px.scatter_3d(x=X_reduced[:, 0], y=X_reduced[:, 1], z=X_reduced[:, 2], color=y_train if problem_type not in ["Clustering", "Compression"] else y_pred,
-                               title=f"{method} Visualization")
-        st.plotly_chart(fig)
-
-    # Interpretability
-    if problem_type not in ["Compression", "Clustering"]:
-        st.subheader("Interpretability")
-        try:
-            X_flat = X_test_processed if data_type == "Tabular" else X_test_processed.reshape(X_test_processed.shape[0], -1)
-            if isinstance(model, keras.Model):
-                explainer = shap.DeepExplainer(model, X_train_processed[:50] if data_type == "Tabular" else X_train_processed[:50])
-                shap_values = explainer.shap_values(X_flat[:50])
-            else:
-                explainer = shap.Explainer(model, X_flat)
-                shap_values = explainer.shap_values(X_flat[:50])
-            if problem_type == "Regression":
-                shap_fig, ax = plt.subplots()
-                shap.summary_plot(shap_values, X_flat[:50], feature_names=feature_names, show=False)
-                st.pyplot(shap_fig)
-            elif problem_type in ["Binary Classification", "Multi-Class", "Image Classification"]:
-                class_names = st.session_state.le.classes_ if st.session_state.le and data_type == "Tabular" else st.session_state.class_names if problem_type == "Image Classification" else [str(i) for i in range(y_train.shape[1])]
-                for i in range(min(len(class_names), len(shap_values))):
-                    shap_fig, ax = plt.subplots()
-                    shap.summary_plot(shap_values[i] if isinstance(shap_values, list) else shap_values, X_flat[:50], feature_names=feature_names, class_names=class_names, show=False)
-                    st.pyplot(shap_fig)
-        except Exception as e:
-            st.error(f"Error generating SHAP plot: {e}")
-
-# Custom CSS
-st.markdown("""
-    <style>
-    .stButton>button {background-color: #4CAF50; color: white;}
-    h1, h2 {color: #1e3a8a;}
-    </style>
-""", unsafe_allow_html=True)
+                input_data = X_train_processed if data_type == "Tabular" else X_train
+                target_data = X_train_processed if data_type == "Tabular" else X_train
+            history, model, training_time = train_model(
+                model, 
+                X_train_processed if data_type == "Tabular" else X_train, 
+                None, 
+                X_test_processed if data_type == "Tabular" else X_test, 
+                None, 
+                epochs, 
+                batch_size, 
+                problem_type, 
+                input_data=input_data, 
+                target_data=target_data, 
+                training_placeholder=training_placeholder
+            )
+            st.session_state.encoder = encoder
+            st.session_state.decoder = decoder
+        else:
+            config = get_model_config(model_type, problem_type)
+            model = config['model_class'](**config['params'])
+            X_train_flat = X_train_processed if data_type == "Tabular" else X_train.reshape(X_train.shape[0], -1)
+            X_test_flat = X_test_processed if data_type == "Tabular" else X_test.reshape(X_test.shape[0], -1)
+            history, model, training_time = train_model(
+                model, 
+                X_train_flat, 
+                y_train, 
+                X_test_flat, 
+                y_test, 
+                epochs, 
+                batch_size, 
+                problem_type,
+                do_grid_search=do_grid_search, 
+                params=params, 
+                grid_params=config['grid_params'],
+                training_placeholder=training_placeholder
+            )
+
+        st.session_state.model = model
+        st.session_state.preprocessor = preprocessor
+        st.session_state.features = features
+        st.session_state.target = target
+        st.session_state.problem_type = problem_type
+        st.session_state.le = le
+
+        filename = save_model(model, preprocessor, features, target, problem_type)
+        with open(filename, 'rb') as f:
+            st.download_button("Download Model", f, file_name=filename)
+        st.success(f"Model trained in {training_time:.2f}s and saved!")