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| import pandas as pd | |
| import streamlit as st | |
| from classification import ClassificationModels | |
| def main(): | |
| st.sidebar.title("Navigation") | |
| page_options = ["Classification", "Regressor", "NLP", "Image", "Voice", "Video", "LLMs"] | |
| choice = st.sidebar.radio("Go to", page_options) | |
| if choice == "Classification": | |
| st.title("Classification") | |
| spectra = st.file_uploader("Upload file", type={"csv", "txt"}) | |
| if spectra is not None: | |
| spectra_df = pd.read_csv(spectra) | |
| st.write(spectra_df.head(5)) | |
| st.write("Headers", spectra_df.columns.tolist()) | |
| st.write("Total Rows", spectra_df.shape[0]) | |
| option = st.text_input("Enter your text here:") | |
| if option: | |
| st.write("You entered:", option) | |
| y = spectra_df[option] | |
| X= spectra_df.drop(option, axis=1) | |
| st.write("X",X.head(5) ) | |
| st.write("y", y.head(5)) | |
| clf = ClassificationModels(X,y) | |
| # Split the data | |
| clf.split_data() | |
| # Train the models | |
| naive_bayes_model = clf.naive_bayes_classifier() | |
| logistic_regression_model = clf.logistic_regression() | |
| decision_tree_model = clf.decision_tree() | |
| random_forests_model = clf.random_forests() | |
| svm_model = clf.support_vector_machines() | |
| knn_model = clf.k_nearest_neighbour() | |
| # Evaluate the models | |
| naive_bayes_accuracy = clf.evaluate_model(naive_bayes_model) | |
| logistic_regression_accuracy = clf.evaluate_model(logistic_regression_model) | |
| decision_tree_accuracy = clf.evaluate_model(decision_tree_model) | |
| random_forests_accuracy = clf.evaluate_model(random_forests_model) | |
| svm_accuracy = clf.evaluate_model(svm_model) | |
| knn_accuracy = clf.evaluate_model(knn_model) | |
| # Evaluate classification model | |
| naive_bayes_classification_report = clf.evaluate_classification_report(naive_bayes_model) | |
| logistic_regression_classification_report = clf.evaluate_classification_report(logistic_regression_model) | |
| decision_tree_classification_report = clf.evaluate_classification_report(decision_tree_model) | |
| random_forest_classification_report = clf.evaluate_classification_report(random_forests_model) | |
| svm_classification_report = clf.evaluate_classification_report(svm_model) | |
| knn_classification_report = clf.evaluate_classification_report(knn_model) | |
| # Display the model prediction | |
| # st.write("Naive Bayes Model Prediction:", clf.predict_model(naive_bayes_model)) | |
| # Display the accuracies | |
| st.write("Naive Bayes Accuracy:", naive_bayes_accuracy) | |
| st.write("Logistic Regression Accuracy:", logistic_regression_accuracy) | |
| st.write("Decision Tree Accuracy:", decision_tree_accuracy) | |
| st.write("Random Forests Accuracy:", random_forests_accuracy) | |
| st.write("Support Vector Machines Accuracy:", svm_accuracy) | |
| st.write("K-Nearest Neighbors Accuracy:", knn_accuracy) | |
| # Display classification reports | |
| st.write("Naive Bayes Classification Report:", pd.DataFrame(naive_bayes_classification_report)) | |
| st.write("Logistic Regression Classification Report:", pd.DataFrame(logistic_regression_classification_report)) | |
| st.write("Decision Tree Classification Report:", pd.DataFrame(decision_tree_classification_report)) | |
| st.write("Random Forests Classification Report:", pd.DataFrame(random_forest_classification_report)) | |
| st.write("Support Vector Machines Classification Report:", pd.DataFrame(svm_classification_report)) | |
| st.write("K-Nearest Neighbors Classification Report:", pd.DataFrame(knn_classification_report)) | |
| # Display the confusion matrix | |
| if __name__ == "__main__": | |
| main() | |