main.py

# import streamlit as st
# from app_config import AppConfig # Import the configerations 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("Intervention Program Analysis")

#     # 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)

#             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

#             # Compute Intervention Session Statistics
#             intervention_stats = data_processor.compute_intervention_statistics(df)
#             st.subheader("Intervention Session Statistics")
#             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")

#             # Compute Student Metrics
#             student_metrics_df = data_processor.compute_student_metrics(df)
#             st.subheader("Student Metrics")
#             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")

#             # 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 reading the file: {str(e)}")

# if __name__ == '__main__':
#     main()



import streamlit as st
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("Intervention Program Analysis")

    # 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)

            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

            # Compute Intervention Session Statistics
            intervention_stats = data_processor.compute_intervention_statistics(df)
            st.subheader("Intervention Session Statistics")
            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")

            # Compute Student Metrics
            student_metrics_df = data_processor.compute_student_metrics(df)
            st.subheader("Student Metrics")
            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)

            # 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()