| # import streamlit as st | |
| # import pandas as pd | |
| # from app_config import AppConfig # Import the configurations class | |
| # from data_processor import DataProcessor # Import the data analysis class | |
| # from visualization import Visualization # Import the data viz class | |
| # from ai_analysis import AIAnalysis # Import the ai analysis class | |
| # from sidebar import Sidebar # Import the Sidebar class | |
| # def main(): | |
| # # Initialize the app configuration | |
| # app_config = AppConfig() | |
| # # Initialize the sidebar | |
| # sidebar = Sidebar() | |
| # sidebar.display() | |
| # # Initialize the data processor | |
| # data_processor = DataProcessor() | |
| # # Initialize the visualization handler | |
| # visualization = Visualization() | |
| # # Initialize the AI analysis handler | |
| # ai_analysis = AIAnalysis(data_processor.client) | |
| # st.title("Literacy Implementation Record Data Analysis") | |
| # # Add the descriptive text | |
| # st.markdown(""" | |
| # This tool summarizes implementation record data for student attendance, engagement, and intervention dosage to address hypothesis #1: Have Students Received Adequate Instruction? | |
| # """) | |
| # # Date selection option | |
| # date_option = st.radio( | |
| # "Select data range:", | |
| # ("All Data", "Date Range") | |
| # ) | |
| # # Initialize start and end date variables | |
| # start_date = None | |
| # end_date = None | |
| # if date_option == "Date Range": | |
| # # Prompt user to enter start and end dates | |
| # start_date = st.date_input("Start Date") | |
| # end_date = st.date_input("End Date") | |
| # # Ensure start date is before end date | |
| # if start_date > end_date: | |
| # st.error("Start date must be before end date.") | |
| # return | |
| # # File uploader | |
| # uploaded_file = st.file_uploader("Upload your Excel file", type=["xlsx"]) | |
| # if uploaded_file is not None: | |
| # try: | |
| # # Read the Excel file into a DataFrame | |
| # df = data_processor.read_excel(uploaded_file) | |
| # # Format the session data | |
| # df = data_processor.format_session_data(df) | |
| # # Replace student names with initials | |
| # df = data_processor.replace_student_names_with_initials(df) | |
| # # Filter data if date range is selected | |
| # if date_option == "Date Range": | |
| # # Convert start_date and end_date to datetime | |
| # start_date = pd.to_datetime(start_date).date() | |
| # end_date = pd.to_datetime(end_date).date() | |
| # # Filter the DataFrame based on the selected date range | |
| # df = df[(df['Date of Session'] >= start_date) & (df['Date of Session'] <= end_date)] | |
| # st.subheader("Uploaded Data") | |
| # st.write(df) | |
| # # Ensure expected column is available | |
| # if DataProcessor.INTERVENTION_COLUMN not in df.columns: | |
| # st.error(f"Expected column '{DataProcessor.INTERVENTION_COLUMN}' not found.") | |
| # return | |
| # #MOVE | |
| # # Compute Intervention Session Statistics | |
| # intervention_stats = data_processor.compute_intervention_statistics(df) | |
| # st.subheader("Intervention Dosage") | |
| # st.write(intervention_stats) | |
| # # Plot and download intervention statistics | |
| # # intervention_fig = visualization.plot_intervention_statistics(intervention_stats) | |
| # # visualization.download_chart(intervention_fig, "intervention_statistics_chart.png") | |
| # # Plot and download intervention statistics: Two-column layout for the visualization and intervention frequency | |
| # col1, col2 = st.columns([3, 1]) # Set the column width ratio | |
| # with col1: | |
| # intervention_fig = visualization.plot_intervention_statistics(intervention_stats) | |
| # with col2: | |
| # intervention_frequency = intervention_stats['Intervention Dosage (%)'].values[0] | |
| # # Display the "Intervention Frequency (%)" text | |
| # st.markdown("<h3 style='color: #358E66;'>Intervention Dosage</h3>", unsafe_allow_html=True) | |
| # # Display the frequency value below it | |
| # st.markdown(f"<h1 style='color: #358E66;'>{intervention_frequency}%</h1>", unsafe_allow_html=True) | |
| # visualization.download_chart(intervention_fig, "intervention_statistics_chart.png") | |
| # # Compute Student Metrics | |
| # student_metrics_df = data_processor.compute_student_metrics(df) | |
| # st.subheader("Student Attendance and Engagement") | |
| # st.write(student_metrics_df) | |
| # # Compute Student Metric Averages | |
| # attendance_avg_stats, engagement_avg_stats = data_processor.compute_average_metrics(student_metrics_df) | |
| # # Plot and download student metrics | |
| # student_metrics_fig = visualization.plot_student_metrics(student_metrics_df, attendance_avg_stats, engagement_avg_stats) | |
| # visualization.download_chart(student_metrics_fig, "student_metrics_chart.png") | |
| # # Evaluate each student and build decision tree diagrams | |
| # student_metrics_df['Evaluation'] = student_metrics_df.apply( | |
| # lambda row: data_processor.evaluate_student(row), axis=1 | |
| # ) | |
| # st.subheader("Student Evaluations") | |
| # st.write(student_metrics_df[['Student', 'Evaluation']]) | |
| # # # Build and display decision tree diagrams for each student | |
| # # for index, row in student_metrics_df.iterrows(): | |
| # # tree_diagram = visualization.build_tree_diagram(row) | |
| # # st.graphviz_chart(tree_diagram.source) | |
| # # Build and display decision tree diagrams for each student | |
| # for index, row in student_metrics_df.iterrows(): | |
| # tree_diagram = visualization.build_tree_diagram(row) | |
| # # Get the student's name from the DataFrame | |
| # student_name = row['Student'] | |
| # # Use st.expander to wrap the graphviz chart with the student's name | |
| # with st.expander(f"{student_name} Decision Tree", expanded=False): | |
| # st.graphviz_chart(tree_diagram.source) | |
| # # Prepare input for the language model | |
| # llm_input = ai_analysis.prepare_llm_input(student_metrics_df) | |
| # # Generate Notes and Recommendations using Hugging Face LLM | |
| # with st.spinner("Generating AI analysis..."): | |
| # recommendations = ai_analysis.prompt_response_from_hf_llm(llm_input) | |
| # st.subheader("AI Analysis") | |
| # st.markdown(recommendations) | |
| # # Download AI output | |
| # ai_analysis.download_llm_output(recommendations, "llm_output.txt") | |
| # except Exception as e: | |
| # st.error(f"Error processing the file: {str(e)}") | |
| # if __name__ == '__main__': | |
| # main() | |
| import streamlit as st | |
| import pandas as pd | |
| from app_config import AppConfig # Import the configurations class | |
| from data_processor import DataProcessor # Import the data analysis class | |
| from visualization import Visualization # Import the data viz class | |
| from ai_analysis import AIAnalysis # Import the ai analysis class | |
| from sidebar import Sidebar # Import the Sidebar class | |
| def main(): | |
| # Initialize the app configuration | |
| app_config = AppConfig() | |
| # Initialize the sidebar | |
| sidebar = Sidebar() | |
| sidebar.display() | |
| # Initialize the data processor | |
| data_processor = DataProcessor() | |
| # Initialize the visualization handler | |
| visualization = Visualization() | |
| # Initialize the AI analysis handler | |
| ai_analysis = AIAnalysis(data_processor.client) | |
| st.title("Literacy Implementation Record Data Analysis") | |
| # Add the descriptive text | |
| st.markdown(""" | |
| This tool summarizes implementation record data for student attendance, engagement, and intervention dosage to address hypothesis #1: Have Students Received Adequate Instruction? | |
| """) | |
| # Date selection option | |
| date_option = st.radio( | |
| "Select data range:", | |
| ("All Data", "Date Range") | |
| ) | |
| # Initialize start and end date variables | |
| start_date = None | |
| end_date = None | |
| if date_option == "Date Range": | |
| # Prompt user to enter start and end dates | |
| start_date = st.date_input("Start Date") | |
| end_date = st.date_input("End Date") | |
| # Ensure start date is before end date | |
| if start_date > end_date: | |
| st.error("Start date must be before end date.") | |
| return | |
| # File uploader | |
| uploaded_file = st.file_uploader("Upload your Excel file", type=["xlsx"]) | |
| if uploaded_file is not None: | |
| try: | |
| # Read the Excel file into a DataFrame | |
| df = data_processor.read_excel(uploaded_file) | |
| # Format the session data | |
| df = data_processor.format_session_data(df) | |
| # Replace student names with initials | |
| df = data_processor.replace_student_names_with_initials(df) | |
| # Filter data if date range is selected | |
| if date_option == "Date Range": | |
| # Convert start_date and end_date to datetime | |
| start_date = pd.to_datetime(start_date).date() | |
| end_date = pd.to_datetime(end_date).date() | |
| # Identify the date column | |
| date_column = next((col for col in df.columns if col in ["Date of Session", "Date"]), None) | |
| if date_column: | |
| # Filter the DataFrame based on the selected date range | |
| df = df[(df[date_column] >= start_date) & (df[date_column] <= end_date)] | |
| else: | |
| st.error("Date column not found in the data.") | |
| return | |
| st.subheader("Uploaded Data") | |
| st.write(df) | |
| # Ensure the intervention column is determined | |
| intervention_column = data_processor.get_intervention_column(df) | |
| if intervention_column not in df.columns: | |
| st.error(f"Expected column '{intervention_column}' not found.") | |
| return | |
| # Compute Intervention Session Statistics | |
| intervention_stats = data_processor.compute_intervention_statistics(df) | |
| st.subheader("Intervention Dosage") | |
| st.write(intervention_stats) | |
| # Plot and download intervention statistics: Two-column layout for the visualization and intervention frequency | |
| col1, col2 = st.columns([3, 1]) # Set the column width ratio | |
| with col1: | |
| intervention_fig = visualization.plot_intervention_statistics(intervention_stats) | |
| with col2: | |
| intervention_frequency = intervention_stats['Intervention Dosage (%)'].values[0] | |
| # Display the "Intervention Dosage (%)" text | |
| st.markdown("<h3 style='color: #358E66;'>Intervention Dosage</h3>", unsafe_allow_html=True) | |
| # Display the frequency value below it | |
| st.markdown(f"<h1 style='color: #358E66;'>{intervention_frequency}%</h1>", unsafe_allow_html=True) | |
| visualization.download_chart(intervention_fig, "intervention_statistics_chart.png") | |
| # Compute Student Metrics | |
| student_metrics_df = data_processor.compute_student_metrics(df) | |
| st.subheader("Student Attendance and Engagement") | |
| st.write(student_metrics_df) | |
| # Compute Student Metric Averages | |
| attendance_avg_stats, engagement_avg_stats = data_processor.compute_average_metrics(student_metrics_df) | |
| # Plot and download student metrics | |
| student_metrics_fig = visualization.plot_student_metrics(student_metrics_df, attendance_avg_stats, engagement_avg_stats) | |
| visualization.download_chart(student_metrics_fig, "student_metrics_chart.png") | |
| # Evaluate each student and build decision tree diagrams | |
| student_metrics_df['Evaluation'] = student_metrics_df.apply( | |
| lambda row: data_processor.evaluate_student(row), axis=1 | |
| ) | |
| st.subheader("Student Evaluations") | |
| st.write(student_metrics_df[['Student', 'Evaluation']]) | |
| # Build and display decision tree diagrams for each student | |
| for index, row in student_metrics_df.iterrows(): | |
| tree_diagram = visualization.build_tree_diagram(row) | |
| # Get the student's name from the DataFrame | |
| student_name = row['Student'] | |
| # Use st.expander to wrap the graphviz chart with the student's name | |
| with st.expander(f"{student_name} Decision Tree", expanded=False): | |
| st.graphviz_chart(tree_diagram.source) | |
| # Prepare input for the language model | |
| llm_input = ai_analysis.prepare_llm_input(student_metrics_df) | |
| # Generate Notes and Recommendations using Hugging Face LLM | |
| with st.spinner("Generating AI analysis..."): | |
| recommendations = ai_analysis.prompt_response_from_hf_llm(llm_input) | |
| st.subheader("AI Analysis") | |
| st.markdown(recommendations) | |
| # Download AI output | |
| ai_analysis.download_llm_output(recommendations, "llm_output.txt") | |
| except Exception as e: | |
| st.error(f"Error processing the file: {str(e)}") | |
| if __name__ == '__main__': | |
| main() |