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| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| from sklearn.impute import SimpleImputer | |
| from sklearn.preprocessing import StandardScaler | |
| from sklearn.manifold import TSNE | |
| import gradio as gr | |
| import os | |
| training_tsne_path = "training_tsne.xlsx" | |
| if os.path.exists(training_tsne_path): | |
| training_tsne_df = pd.read_excel(training_tsne_path) | |
| print("Training t-SNE data loaded successfully!") | |
| else: | |
| training_tsne_df = None | |
| print("Error: training_tsne.xlsx not found. Please upload it.") | |
| columns_to_transform = ['Mn', 'Co', 'Ni', 'Cu', 'Zn', 'As', 'Se', 'Mo', 'Ag', 'Cd', 'Sb', 'Te', 'Tl', 'Pb', 'Bi'] | |
| imputer = SimpleImputer(strategy='mean') | |
| scaler = StandardScaler() | |
| def process_test_data(test_file): | |
| test_df = pd.read_excel(test_file.name) | |
| missing_columns = [col for col in columns_to_transform if col not in test_df.columns] | |
| if missing_columns: | |
| return f"Error: Missing columns in the uploaded data: {', '.join(missing_columns)}" | |
| test_df[columns_to_transform] = np.log1p(test_df[columns_to_transform]) | |
| X_test_imputed = imputer.fit_transform(test_df[columns_to_transform]) | |
| X_test_scaled = scaler.fit_transform(X_test_imputed) | |
| return X_test_scaled | |
| def visualize_tsne_interface(test_file): | |
| if training_tsne_df is None: | |
| return "Error: Training t-SNE data not found. Please upload `training_tsne.xlsx`." | |
| X_test_scaled = process_test_data(test_file) | |
| if isinstance(X_test_scaled, str): | |
| return X_test_scaled | |
| tsne_test = TSNE(n_components=2, random_state=42) | |
| X_tsne_test = tsne_test.fit_transform(X_test_scaled) | |
| plt.figure(figsize=(10, 7)) | |
| sns.scatterplot(data=training_tsne_df, | |
| x='x', | |
| y='y', | |
| hue='label', | |
| palette=sns.color_palette("hsv", len(training_tsne_df['label'].unique())), | |
| legend='full', | |
| alpha=0.6) | |
| plt.scatter(X_tsne_test[:, 0], X_tsne_test[:, 1], | |
| c='black', label='Test Data', marker='x') | |
| plt.title('t-SNE Visualization: Training Data with Test Data Points') | |
| plt.xlabel('t-SNE Component 1') | |
| plt.ylabel('t-SNE Component 2') | |
| plt.legend(title='Tectonic Environments') | |
| output_file = "tsne.png" | |
| plt.savefig(output_file) | |
| plt.close() | |
| return output_file | |
| css = """ | |
| #footer { | |
| position: fixed !important; | |
| left: 10px !important; | |
| bottom: 10px !important; | |
| font-size: 12px; | |
| color: #555 !important; | |
| z-index: 9999; | |
| } | |
| """ | |
| footer = """ | |
| <div id="footer"> | |
| <strong>Dr Muhammad Amar Gul</strong><br> | |
| School of Earth and Space Sciences<br> | |
| University of Science and Technology of China | |
| </div> | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| gr.HTML(""" | |
| <div style="text-align: center;"> | |
| <p style="font-size: 20px;"> | |
| Upload an Excel file containing the required features ('Mn', 'Co', 'Ni', 'Cu', 'Zn', 'As', 'Se', 'Mo', 'Ag', 'Cd', 'Sb', 'Te', 'Tl', 'Pb', 'Bi'). | |
| This application will provide t-SNE visualization. The t-SNE plot will show how your test data points relate to the training data. | |
| </p> | |
| </div> | |
| """) | |
| with gr.Row(): | |
| test_file = gr.File(label="Upload Test Data Excel File") | |
| image_output = gr.Image(type="filepath", label="t-SNE Plot") | |
| test_button = gr.Button("Generate Plot") | |
| test_button.click(visualize_tsne_interface, inputs=test_file, outputs=image_output) | |
| gr.HTML(footer) | |
| demo.launch(share=True) | |