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| #---IMPORTS--- | |
| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| import os | |
| from streamlit_option_menu import option_menu | |
| #---MODULES IMPORT--- | |
| from Modules.data_loader import DataLoader | |
| from Modules.data_analyzer import DataAnalyzer | |
| from Modules.data_filter import DataFilter | |
| from Modules.data_transformer import DataTransformer | |
| from Modules.data_visualizer import DataVisualizer | |
| from Modules.data_QA import DataQA | |
| from Modules.MLtoolkit import MLToolkit | |
| #---SKLEARN-IMPORT--- | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.metrics import mean_squared_error, accuracy_score, mean_absolute_error | |
| def main(): | |
| st.title('Insights 📶') | |
| data = pd.DataFrame() | |
| uploaded_file = st.file_uploader("Choose a CSV file", type="csv") | |
| if st.button('Load Data'): | |
| data_loader = DataLoader() | |
| data_loader.load_data(uploaded_file) | |
| try: | |
| data = pd.read_csv("data.csv") | |
| with st.sidebar: | |
| selected = option_menu( | |
| menu_title="Main Menu", | |
| options=["Data Loader", "Exploratory Data Analysis", "Data Cleaning", "Q/A", "MLtoolkit", "Data Party"]) | |
| # --- DATA LOADER --- | |
| if selected == "Data Loader": | |
| st.toast("Data Loaded") | |
| st.write(data.head()) | |
| # --- EDA --- | |
| if selected == "Exploratory Data Analysis": | |
| data = pd.read_csv("data.csv") | |
| data_analyzer = DataAnalyzer(data) | |
| data_analyzer.show_eda() | |
| data_analyzer.show_count_plots() | |
| data_visualizer = DataVisualizer(data) | |
| data_visualizer.visualize_data() | |
| # --- DATA CLEANING --- | |
| if selected == "Data Cleaning": | |
| st.header("Data Cleaning") | |
| data_transformer = DataTransformer(data) | |
| # modified_data = data_transformer.perform_column_operation() | |
| data = data_transformer.handle_null() | |
| data = data_transformer.categorical_to_numerical() | |
| data = data_transformer.remove_columns() | |
| # data_filter = DataFilter(modified_data) | |
| # data = data_filter.filter_rows() | |
| # --- QUESTION AND ANSWER --- | |
| if selected == "Q/A": | |
| data_QA = DataQA(data) | |
| data_QA.ask_csv() | |
| if selected == "MLtoolkit": | |
| try: | |
| ml_toolkit = MLToolkit(data) | |
| algorithm, algorithm_type = ml_toolkit.select_algorithm() | |
| X, Y = ml_toolkit.select_features_and_target() | |
| if (algorithm_type == "Regressor") and (algorithm == 'Decision Tree' or algorithm == 'Random Forest' or algorithm == "Linear Regression"): | |
| params = ml_toolkit.add_parameter_regressor() | |
| else: | |
| params = ml_toolkit.add_parameter_classifier_general() | |
| if algorithm_type == "Regressor": | |
| algo_model = ml_toolkit.model_regressor(params) | |
| else: | |
| algo_model = ml_toolkit.model_classifier(params) | |
| x_train, x_test, y_train, y_test = train_test_split(X, Y, train_size=0.8) | |
| algo_model.fit(x_train, y_train) | |
| predict = algo_model.predict(x_test) | |
| if algorithm != 'Linear Regression' and algorithm_type != 'Regressor': | |
| st.write("Training Accuracy is:", algo_model.score(x_train, y_train) * 100) | |
| st.write("Testing Accuracy is:", accuracy_score(y_test, predict) * 100) | |
| else: | |
| st.write("Mean Squared error is:", mean_squared_error(y_test, predict)) | |
| st.write("Mean Absolute error is:", mean_absolute_error(y_test, predict)) | |
| except ValueError as e: | |
| error_message = str(e) | |
| st.error("Value Error: "+error_message) | |
| except TypeError as e: | |
| error_message = str(e) | |
| st.error("Type Error: "+error_message) | |
| except Exception as e: | |
| error_message = str(e) | |
| st.error(error_message) | |
| # st.write("An error occurred:", e) | |
| # --- DATA PARTY --- | |
| if selected == "Data Party": | |
| st.write("To be Added)") | |
| except: | |
| st.write("Please upload a csv file") | |
| if __name__ == "__main__": | |
| main() | |
| # TO DO: | |
| # 1. automate categorical to numerical conversion | |
| # 2. toggle btn for data (original and modified) | |
| # 3. ask to save modified data before saving | |
| # 4. streamline prompts in llm_summary | |
| # 5. ml models |