Update main.py

main.py

@@ -1,192 +1,3 @@
-# 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
-# from report import ReportGenerator
-
-# def main():
-#     # Initialize the app configuration
-#     app_config = AppConfig()
-
-#     # Initialize the session state 
-#     if 'ai_recommendations' not in st.session_state:
-#         st.session_state.ai_recommendations = None
-
-#     # 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)
-
-#             # Generate Notes and Recommendations using LLM
-#             if st.session_state.ai_recommendations is None:
-#                 with st.spinner("Generating MTSS.ai Analysis..."):
-#                     llm_input = ai_analysis.prepare_llm_input(student_metrics_df)
-#                     # recommendations = ai_analysis.prompt_response_from_hf_llm(llm_input)
-#                     recommendations = ai_analysis.prompt_response_from_mistral_llm(llm_input)
-#                     st.session_state.ai_recommendations = recommendations
-            
-#             # Display the recommendations
-#             st.subheader("MTSS.ai Analysis")
-#             # st.markdown(recommendations)
-#             st.markdown(st.session_state.ai_recommendations)
-
-#             # Download AI output
-#             # ai_analysis.download_llm_output(recommendations, "MTSSai_Report.txt")
-#             ai_analysis.download_llm_output(st.session_state.ai_recommendations, "MTSSai_Report.txt")
-
-#             # Generate the PDF Report using the stored recommendations
-#             report_gen = ReportGenerator()
-#             combined_pdf = report_gen.create_combined_pdf(
-#                 intervention_fig, 
-#                 student_metrics_fig, 
-#                 st.session_state.ai_recommendations
-#             )
-                
-#             # Add the download button for the PDF
-#             st.download_button(
-#                 label="Download MTSS.ai Report (PDF)",
-#                 data=combined_pdf,
-#                 file_name="MTSSai_LIR_Report.pdf",
-#                 mime="application/pdf",
-#                 icon="📄", 
-#                 use_container_width=True
-#             )
-
-#         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