Create app_recovery.py

app_recovery.py

@@ -0,0 +1,594 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Nov 24 12:47:37 2024
+
+@author: Ashmitha
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Nov 24 12:25:57 2024
+
+@author: Ashmitha
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Nov  9 15:44:40 2024
+
+@author: Ashmitha
+"""
+
+import pandas as pd
+import numpy as np
+import gradio as gr
+from sklearn.metrics import mean_squared_error,r2_score
+from scipy.stats import pearsonr
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import KFold
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import GRU,Dense,Dropout,BatchNormalization,LeakyReLU
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras import regularizers
+from tensorflow.keras.callbacks import ReduceLROnPlateau,EarlyStopping
+import os
+from sklearn.preprocessing import MinMaxScaler
+from keras.layers import Conv1D,MaxPooling1D,Dense,Flatten,Dropout,LeakyReLU
+from keras.callbacks import ReduceLROnPlateau,EarlyStopping
+from sklearn.ensemble import RandomForestRegressor
+from xgboost import XGBRegressor
+import io
+from sklearn.feature_selection import SelectFromModel
+import tempfile
+
+#-------------------------------------Feature selection---------------------------------------------------------------------------------------------
+
+def RandomForestFeatureSelection(trainX, trainy, num_features=60):
+    rf = RandomForestRegressor(n_estimators=1000, random_state=50)
+    rf.fit(trainX, trainy)
+    
+    # Get feature importances
+    importances = rf.feature_importances_
+    
+    # Select the top N important features
+    indices = np.argsort(importances)[-num_features:]
+    return indices 
+#----------------------------------------------------------GRU Model---------------------------------------------------------------------
+import numpy as np
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import GRU, Dense, BatchNormalization, Dropout, LeakyReLU
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras import regularizers
+from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.feature_selection import SelectFromModel
+
+def GRUModel(trainX, trainy, testX, testy, epochs=1000, batch_size=64, learning_rate=0.0001, l1_reg=0.001, l2_reg=0.001, dropout_rate=0.2, feature_selection=True):
+    
+    # Apply feature selection using Random Forest Regressor
+    if feature_selection:
+        # Use RandomForestRegressor to rank features by importance
+        rf = RandomForestRegressor(n_estimators=100, random_state=42)
+        rf.fit(trainX, trainy)
+        
+        # Select features with importance greater than a threshold (e.g., mean importance)
+        selector = SelectFromModel(rf, threshold="mean", prefit=True)
+        trainX = selector.transform(trainX)
+        if testX is not None:
+            testX = selector.transform(testX)
+        print(f"Selected {trainX.shape[1]} features based on feature importance.")
+    
+    # Scale the input data using MinMaxScaler to normalize the feature range
+    scaler = MinMaxScaler()
+    trainX_scaled = scaler.fit_transform(trainX)
+    if testX is not None:
+        testX_scaled = scaler.transform(testX)
+    
+    # Scale the target variable using MinMaxScaler
+    target_scaler = MinMaxScaler()
+    trainy_scaled = target_scaler.fit_transform(trainy.reshape(-1, 1))  # Reshape to 2D for scaler
+
+    # Reshape trainX and testX to be 3D: (samples, timesteps, features)
+    trainX = trainX_scaled.reshape((trainX.shape[0], 1, trainX.shape[1]))  # Adjusted for general feature count
+    if testX is not None:
+        testX = testX_scaled.reshape((testX.shape[0], 1, testX.shape[1]))  # Reshape testX if it exists
+    
+    model = Sequential()
+    
+    # GRU Layer
+    model.add(GRU(512, input_shape=(trainX.shape[1], trainX.shape[2]), return_sequences=False, kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    
+    # Dense Layers with Batch Normalization, Dropout, LeakyReLU
+    model.add(Dense(256, kernel_initializer='he_normal', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    model.add(BatchNormalization())
+    model.add(Dropout(dropout_rate))
+    model.add(LeakyReLU(alpha=0.1))
+
+    model.add(Dense(128, kernel_initializer='he_normal', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    model.add(BatchNormalization())
+    model.add(Dropout(dropout_rate))
+    model.add(LeakyReLU(alpha=0.1))
+    
+    model.add(Dense(64, kernel_initializer='he_normal', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    model.add(BatchNormalization())
+    model.add(Dropout(dropout_rate))
+    model.add(LeakyReLU(alpha=0.1))
+    
+    model.add(Dense(32, kernel_initializer='he_normal', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    model.add(BatchNormalization())
+    model.add(Dropout(dropout_rate))
+    model.add(LeakyReLU(alpha=0.1))
+    
+    # Output Layer with ReLU activation to prevent negative predictions
+    model.add(Dense(1, activation="relu"))
+    
+    # Compile the model
+    model.compile(loss='mse', optimizer=Adam(learning_rate=learning_rate), metrics=['mse'])
+    
+    # Callbacks for learning rate reduction and early stopping
+    learning_rate_reduction = ReduceLROnPlateau(monitor='val_loss', patience=10, verbose=1, factor=0.5, min_lr=1e-6)
+    early_stopping = EarlyStopping(monitor='val_loss', verbose=1, restore_best_weights=True, patience=10)
+    
+    # Train the model
+    history = model.fit(trainX, trainy_scaled, epochs=epochs, batch_size=batch_size, validation_split=0.1, verbose=1, 
+                        callbacks=[learning_rate_reduction, early_stopping])
+    
+    # Predict train and test
+    predicted_train = model.predict(trainX)
+    predicted_test = model.predict(testX) if testX is not None else None
+
+    # Flatten predictions
+    predicted_train = predicted_train.flatten()
+    if predicted_test is not None:
+        predicted_test = predicted_test.flatten()
+    else:
+        predicted_test = np.zeros_like(predicted_train)
+
+    # Inverse scale the predictions to get them back to original range
+    predicted_train = target_scaler.inverse_transform(predicted_train.reshape(-1, 1)).flatten()
+    if predicted_test is not None:
+        predicted_test = target_scaler.inverse_transform(predicted_test.reshape(-1, 1)).flatten()
+
+    return predicted_train, predicted_test, history
+
+
+
+
+#-----------------------------------------------------------DeepMap-------------------------------------------------------------------------------
+def CNNModel(trainX, trainy, testX, testy, epochs=1000, batch_size=64, learning_rate=0.0001, l1_reg=0.0001, l2_reg=0.0001, dropout_rate=0.3,feature_selection=True):
+    if feature_selection:
+        rf=RandomForestRegressor(n_estimators=100,random_state=42)
+        rf.fit(trainX,trainy)
+        
+        selector=SelectFromModel(rf, threshold="mean",prefit=True)
+        trainX=selector.transform(trainX)
+        if testX is not None:
+            testX=selector.transform(testX)
+        print(f"Selected {trainX.shape[1]} feature based on the important feature")
+    
+   
+    
+    # Scaling the inputs
+    scaler = MinMaxScaler()
+    trainX_scaled = scaler.fit_transform(trainX)
+    if testX is not None:
+        testX_scaled = scaler.transform(testX)
+    
+    # Reshape for CNN input (samples, features, channels)
+    trainX = trainX_scaled.reshape((trainX.shape[0], trainX.shape[1], 1))
+    if testX is not None:
+        testX = testX_scaled.reshape((testX.shape[0], testX.shape[1], 1))
+    
+    model = Sequential()
+    
+    # Convolutional layers
+    model.add(Conv1D(256, kernel_size=3, activation='relu', input_shape=(trainX.shape[1], 1), kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    model.add(MaxPooling1D(pool_size=2))
+    model.add(Dropout(dropout_rate))
+    
+    model.add(Conv1D(128, kernel_size=3, activation='relu', kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    model.add(MaxPooling1D(pool_size=2))
+    model.add(Dropout(dropout_rate))
+    
+    # Flatten and Dense layers
+    model.add(Flatten())
+    model.add(Dense(64, kernel_regularizer=regularizers.l1_l2(l1=l1_reg, l2=l2_reg)))
+    model.add(LeakyReLU(alpha=0.1))
+    model.add(Dropout(dropout_rate))
+
+    model.add(Dense(1, activation='linear'))
+
+    # Compile the model
+    model.compile(loss='mse', optimizer=Adam(learning_rate=learning_rate), metrics=['mse'])
+
+    # Callbacks
+    learning_rate_reduction = ReduceLROnPlateau(monitor='val_loss', patience=5, verbose=1, factor=0.5, min_lr=1e-6)
+    early_stopping = EarlyStopping(monitor='val_loss', verbose=1, restore_best_weights=True, patience=10)
+    
+    # Train the model
+    history = model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, validation_split=0.1, verbose=1, 
+                        callbacks=[learning_rate_reduction, early_stopping])
+    
+    predicted_train = model.predict(trainX).flatten()
+    predicted_test = model.predict(testX).flatten() if testX is not None else None
+    
+    return predicted_train, predicted_test, history
+
+#-------------------------------------------------------------------------Random Forest----------------------------------------------------
+def RFModel(trainX, trainy, testX, testy, n_estimators=100, max_depth=None,feature_selection=True):
+    if feature_selection:
+        rf=RandomForestRegressor(n_estimators=100, random_state=42)
+        rf.fit(trainX, trainy)
+        selector=SelectFromModel(rf, threshold="mean", prefit=True)
+        trainX=selector.transform(trainX)
+        if testX is not None:
+            testX=selector.transform(testX)
+        print(f"Selected {trainX.shape[1]} feature based on the feature selection")
+        
+    
+    # Log transformation of the target variable
+   
+    # Scaling the feature data
+    scaler = MinMaxScaler()
+    trainX_scaled = scaler.fit_transform(trainX)
+    if testX is not None:
+        testX_scaled = scaler.transform(testX)
+    
+    # Define and train the RandomForest model
+    rf_model = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth, random_state=42)
+    history=rf_model.fit(trainX_scaled, trainy)
+    
+    
+    # Predictions
+    predicted_train = rf_model.predict(trainX_scaled)
+    predicted_test = rf_model.predict(testX_scaled) if testX is not None else None
+    
+    return predicted_train, predicted_test,history
+#------------------------------------------------------------------------------XGboost---------------------------------------------------------------
+def XGBoostModel(trainX, trainy, testX, testy,learning_rate,min_child_weight,feature_selection=True, n_estimators=100, max_depth=None):
+    if feature_selection:
+        rf=RandomForestRegressor(n_estimators=100,random_state=42)
+        rf.fit(trainX,trainy)
+        selector=SelectFromModel(rf,threshold="mean",prefit=True)
+        trainX=selector.transform(trainX)
+        if testX is not None:
+            testX=selector.transform(testX)
+        print(f"Selected {trainX.shape[1]} features based on feature importance")
+        
+    
+    #trainy_log = np.log1p(trainy)  # Log-transform to handle large phenotypic values
+    #if testy is not None:
+       # testy_log = np.log1p(testy)
+
+    # Scale the features
+    scaler = MinMaxScaler()
+    trainX_scaled = scaler.fit_transform(trainX)
+    if testX is not None:
+        testX_scaled = scaler.transform(testX)
+
+    # Define and train the XGBoost model
+   # xgb_model = XGBRegressor(n_estimators=n_estimators, max_depth=100, random_state=42)
+    #xgb_model = XGBRegressor(objective ='reg:linear', 
+               #   n_estimators = 100, seed = 100) 
+    xgb_model=XGBRegressor(objective="reg:squarederror",random_state=42)
+    history=xgb_model.fit(trainX, trainy)
+    param_grid={
+        "learning_rate":0.01,
+        "max_depth" : 10,
+         "n_estimators": 100,
+         "min_child_weight": 5
+        }
+    
+
+    # Predictions
+    predicted_train = xgb_model.predict(trainX_scaled)
+    predicted_test = xgb_model.predict(testX_scaled) if testX is not None else None
+    
+
+    return predicted_train, predicted_test,history
+
+
+
+
+
+
+#----------------------------------------reading file----------------------------------------------------------------------------------------
+
+
+
+
+
+# Helper function to read the uploaded CSV file
+def read_csv_file(uploaded_file):
+    if uploaded_file is not None:
+        if hasattr(uploaded_file, 'data'):  # For NamedBytes
+            return pd.read_csv(io.BytesIO(uploaded_file.data))
+        elif hasattr(uploaded_file, 'name'):  # For NamedString
+            return pd.read_csv(uploaded_file.name)
+    return None
+
+
+#-----------------------------------------------------------------calculate topsis score--------------------------------------------------------
+
+
+def calculate_topsis_score(df):
+    # Normalize the metrics
+    metrics = df[['Train_MSE', 'Train_RMSE', 'Train_R2', 'Train_Corr']].dropna()  # Ensure no NaN values
+    norm_metrics = metrics / np.sqrt((metrics ** 2).sum(axis=0))
+    
+    # Define ideal best and worst for each metric
+    ideal_best = pd.Series(index=norm_metrics.columns)
+    ideal_worst = pd.Series(index=norm_metrics.columns)
+
+    # For RMSE and MSE (minimization criteria): min is best, max is worst
+    for col in ['Train_MSE', 'Train_RMSE']:
+        ideal_best[col] = norm_metrics[col].min()
+        ideal_worst[col] = norm_metrics[col].max()
+
+    # For R2 and Corr (maximization criteria): max is best, min is worst
+    for col in ['Train_R2', 'Train_Corr']:
+        ideal_best[col] = norm_metrics[col].max()
+        ideal_worst[col] = norm_metrics[col].min()
+    
+    # Calculate Euclidean distance to ideal best and worst
+    dist_to_best = np.sqrt(((norm_metrics - ideal_best) ** 2).sum(axis=1))
+    dist_to_worst = np.sqrt(((norm_metrics - ideal_worst) ** 2).sum(axis=1))
+
+    # Calculate TOPSIS score
+    topsis_score = dist_to_worst / (dist_to_best + dist_to_worst)
+    df['TOPSIS_Score'] = np.nan  # Initialize with NaN
+    df.loc[metrics.index, 'TOPSIS_Score'] = topsis_score  # Assign TOPSIS scores
+    return df
+
+#--------------------------------------------------- Nested Cross validation---------------------------------------------------------------------------
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import KFold
+from sklearn.preprocessing import StandardScaler
+from sklearn.feature_selection import SelectFromModel
+from sklearn.metrics import mean_squared_error, r2_score
+from scipy.stats import pearsonr
+import numpy as np
+import pandas as pd
+
+def NestedKFoldCrossValidation(
+    training_data, training_additive, testing_data, testing_additive, 
+    training_dominance, testing_dominance, epochs, learning_rate, min_child_weight, 
+    batch_size=64, outer_n_splits=2, output_file='cross_validation_results.csv', 
+    predicted_phenotype_file='predicted_phenotype.csv', feature_selection=True
+):
+
+    if 'phenotypes' not in training_data.columns:
+        raise ValueError("Training data does not contain the 'phenotypes' column.")
+    
+    # Remove Sample ID columns from additive and dominance data
+    training_additive = training_additive.iloc[:, 1:]
+    testing_additive = testing_additive.iloc[:, 1:]
+    training_dominance = training_dominance.iloc[:, 1:]
+    testing_dominance = testing_dominance.iloc[:, 1:]
+
+    # Merge training and testing data with additive and dominance components
+    training_data_merged = pd.concat([training_data, training_additive, training_dominance], axis=1)
+    testing_data_merged = pd.concat([testing_data, testing_additive, testing_dominance], axis=1)
+
+    phenotypic_info = training_data['phenotypes'].values
+    phenotypic_test_info = testing_data['phenotypes'].values if 'phenotypes' in testing_data.columns else None
+    sample_ids = testing_data.iloc[:, 0].values
+
+    training_genotypic_data_merged = training_data_merged.iloc[:, 2:].values
+    testing_genotypic_data_merged = testing_data_merged.iloc[:, 2:].values
+
+    # Feature selection
+    if feature_selection:
+        rf = RandomForestRegressor(n_estimators=100, random_state=65)
+        rf.fit(training_genotypic_data_merged, phenotypic_info)
+        selector = SelectFromModel(rf, threshold="mean", prefit=True)
+        training_genotypic_data_merged = selector.transform(training_genotypic_data_merged)
+        testing_genotypic_data_merged = selector.transform(testing_genotypic_data_merged)
+        print(f"Selected {training_genotypic_data_merged.shape[1]} features based on importance.")
+
+    # Standardize the genotypic data
+    scaler = StandardScaler()
+    training_genotypic_data_merged = scaler.fit_transform(training_genotypic_data_merged)
+    testing_genotypic_data_merged = scaler.transform(testing_genotypic_data_merged)
+
+    outer_kf = KFold(n_splits=outer_n_splits)
+
+    results = []
+    all_predicted_phenotypes = []
+
+    def calculate_metrics(true_values, predicted_values):
+        mse = mean_squared_error(true_values, predicted_values)
+        rmse = np.sqrt(mse)
+        r2 = r2_score(true_values, predicted_values)
+        corr = pearsonr(true_values, predicted_values)[0]
+        return mse, rmse, r2, corr
+
+    models = [
+        ('GRUModel', GRUModel),
+        ('CNNModel', CNNModel),
+        ('RFModel', RFModel),
+        ('XGBoostModel', XGBoostModel)
+    ]
+
+    for outer_fold, (outer_train_index, outer_test_index) in enumerate(outer_kf.split(phenotypic_info), 1):
+        outer_trainX = training_genotypic_data_merged[outer_train_index]
+        outer_trainy = phenotypic_info[outer_train_index]
+
+        outer_testX = testing_genotypic_data_merged
+        outer_testy = phenotypic_test_info
+
+        for model_name, model_func in models:
+            print(f"Running model: {model_name} for fold {outer_fold}")
+            if model_name in ['GRUModel', 'CNNModel']:
+                predicted_train, predicted_test, history = model_func(outer_trainX, outer_trainy, outer_testX, outer_testy, epochs=epochs, batch_size=batch_size)
+            elif model_name in ['RFModel']:
+                predicted_train, predicted_test, history = model_func(outer_trainX, outer_trainy, outer_testX, outer_testy)
+            else:
+                predicted_train, predicted_test, history = model_func(outer_trainX, outer_trainy, outer_testX, outer_testy, learning_rate, min_child_weight)
+
+            # Calculate metrics
+            mse_train, rmse_train, r2_train, corr_train = calculate_metrics(outer_trainy, predicted_train)
+            mse_test, rmse_test, r2_test, corr_test = calculate_metrics(outer_testy, predicted_test) if outer_testy is not None else (None, None, None, None)
+
+            results.append({
+                'Model': model_name,
+                'Fold': outer_fold,
+                'Train_MSE': mse_train,
+                'Train_RMSE': rmse_train,
+                'Train_R2': r2_train,
+                'Train_Corr': corr_train,
+                'Test_MSE': mse_test,
+                'Test_RMSE': rmse_test,
+                'Test_R2': r2_test,
+                'Test_Corr': corr_test
+            })
+
+            if predicted_test is not None:
+                predicted_test_df = pd.DataFrame({
+                    'Sample_ID': sample_ids,
+                    'Predicted_Phenotype': predicted_test,
+                    'Model': model_name
+                })
+                all_predicted_phenotypes.append(predicted_test_df)
+
+    # Compile results
+    results_df = pd.DataFrame(results)
+    avg_results_df = results_df.groupby('Model').agg({
+        'Train_MSE': 'mean',
+        'Train_RMSE': 'mean',
+        'Train_R2': 'mean',
+        'Train_Corr': 'mean',
+        'Test_MSE': 'mean',
+        'Test_RMSE': 'mean',
+        'Test_R2': 'mean',
+        'Test_Corr': 'mean'
+    }).reset_index()
+
+    # Calculate the TOPSIS score for the average metrics (considering only MSE, RMSE, R², and Correlation)
+    def calculate_topsis_score(df):
+        # Normalize the data
+        norm_df = (df.iloc[:, 1:] - df.iloc[:, 1:].min()) / (df.iloc[:, 1:].max() - df.iloc[:, 1:].min())
+
+        # Calculate the positive and negative ideal solutions
+        ideal_positive = norm_df.max(axis=0)
+        ideal_negative = norm_df.min(axis=0)
+
+        # Calculate the Euclidean distances
+        dist_positive = np.sqrt(((norm_df - ideal_positive) ** 2).sum(axis=1))
+        dist_negative = np.sqrt(((norm_df - ideal_negative) ** 2).sum(axis=1))
+
+        # Calculate the TOPSIS score
+        topsis_score = dist_negative / (dist_positive + dist_negative)
+
+        # Add the TOPSIS score to the dataframe
+        df['TOPSIS_Score'] = topsis_score
+
+        return df
+
+    avg_results_df = calculate_topsis_score(avg_results_df)
+
+    # Save the results with TOPSIS scores to the file
+    avg_results_df.to_csv(output_file, index=False)
+
+    # Save predicted phenotypes
+    if all_predicted_phenotypes:
+        predicted_all_df = pd.concat(all_predicted_phenotypes, axis=0, ignore_index=True)
+        predicted_all_df.to_csv(predicted_phenotype_file, index=False)
+
+    return avg_results_df, predicted_all_df if all_predicted_phenotypes else None
+
+
+    # Save the results to the file
+    #results_df.to_csv(output_file, index=False)
+
+    # Save predicted phenotypes
+    #if all_predicted_phenotypes:
+       # predicted_all_df = pd.concat(all_predicted_phenotypes, axis=0, ignore_index=True)
+        #predicted_all_df.to_csv(predicted_phenotype_file, index=False)
+
+   # return results_df, predicted_all_df if all_predicted_phenotypes else None
+
+#--------------------------------------------------------------------Gradio interface---------------------------------------------------------------
+
+def run_cross_validation(training_file, training_additive_file, testing_file, testing_additive_file, 
+                         training_dominance_file, testing_dominance_file,feature_selection,learning_rate,min_child_weight):
+
+    # Default parameters
+    epochs = 1000
+    batch_size = 64
+    
+    inner_n_splits = 2
+    min_child_weight=5
+    learning_rate=0.001
+    #learning_rate=learning_rate
+   # min_child_weight=min_child_weight
+
+    # Load datasets
+    training_data = pd.read_csv(training_file.name)
+    training_additive = pd.read_csv(training_additive_file.name)
+    testing_data = pd.read_csv(testing_file.name)
+    testing_additive = pd.read_csv(testing_additive_file.name)
+    training_dominance = pd.read_csv(training_dominance_file.name)
+    testing_dominance = pd.read_csv(testing_dominance_file.name)
+
+    # Call the cross-validation function
+    results, predicted_phenotypes = NestedKFoldCrossValidation(
+        training_data=training_data,
+        training_additive=training_additive,
+        testing_data=testing_data,
+        testing_additive=testing_additive,
+        training_dominance=training_dominance,
+        testing_dominance=testing_dominance,
+        epochs=epochs,
+        batch_size=batch_size,
+        #outer_n_splits= outer_n_splits,
+        #outer_n_splits=outer_n_splits,
+        #inner_n_splits=inner_n_splits,
+        learning_rate=learning_rate,
+        min_child_weight=min_child_weight,
+        feature_selection=feature_selection
+    )
+
+    # Save outputs
+    results_file = "cross_validation_results.csv"
+    predicted_file = "predicted_phenotype.csv"
+    results.to_csv(results_file, index=False)
+    predicted_phenotypes.to_csv(predicted_file, index=False)
+
+    return results_file, predicted_file
+
+# Gradio interface
+with gr.Blocks() as interface:
+    gr.Markdown("# DeepMap - An Integrated GUI for Genotype to Phenotype Prediction")
+
+    with gr.Row():
+        training_file = gr.File(label="Upload Training Data (CSV)")
+        training_additive_file = gr.File(label="Upload Training Additive Data (CSV)")
+        training_dominance_file = gr.File(label="Upload Training Dominance Data (CSV)")
+
+    with gr.Row():
+        testing_file = gr.File(label="Upload Testing Data (CSV)")
+        testing_additive_file = gr.File(label="Upload Testing Additive Data (CSV)")
+        testing_dominance_file = gr.File(label="Upload Testing Dominance Data (CSV)")
+
+    with gr.Row():
+        feature_selection = gr.Checkbox(label="Enable Feature Selection", value=True)
+
+    output1 = gr.File(label="Cross-Validation Results (CSV)")
+    output2 = gr.File(label="Predicted Phenotypes (CSV)")
+
+    submit_btn = gr.Button("Run DeepMap")
+    submit_btn.click(
+        run_cross_validation,
+        inputs=[
+            training_file, training_additive_file, testing_file, 
+            testing_additive_file, training_dominance_file,testing_dominance_file, 
+            feature_selection
+        ],
+        outputs=[output1, output2]
+    )
+
+# Launch the interface
+interface.launch()