Update main.py

main.py

@@ -1,92 +1,3 @@
-# import streamlit as st
-# from app_config import AppConfig
-# from data_processor import DataProcessor
-# from visualization import Visualization
-# from ai_analysis import AIAnalysis
-# from sidebar import Sidebar  # Import the Sidebar class
-
-# def main():
-#     # Initialize the app configuration
-#     app_config = AppConfig()
-
-#     # Initialize the sidebar
-#     sidebar = Sidebar()
-#     sidebar.display()  # Display the sidebar
-
-#     # 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
 from data_processor import DataProcessor
@@ -149,19 +60,6 @@ def main():
             st.subheader("Student Metrics")
             st.write(student_metrics_df)
 
-            # Display decision tree diagrams for each student
-            st.subheader("Decision Path for Each Student")
-            for index, row in student_metrics_df.iterrows():
-                st.write(f"Decision Tree for Student {row['Student']}:")
-                
-                # Generate and display the tree diagram
-                try:
-                    dot = data_processor.build_tree_diagram(row)
-                    st.graphviz_chart(dot)
-                    st.write(f"Outcome: {row.get('Outcome', 'No outcome data')}")
-                except Exception as e:
-                    st.error(f"Error generating decision tree for {row['Student']}: {str(e)}")
-
             # Compute Student Metric Averages
             attendance_avg_stats, engagement_avg_stats = data_processor.compute_average_metrics(student_metrics_df)
 
@@ -186,4 +84,4 @@ def main():
             st.error(f"Error reading the file: {str(e)}")
 
 if __name__ == '__main__':
-    main()
+    main()