Update main.py

main.py

@@ -1,3 +1,92 @@
+# 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
@@ -60,6 +149,19 @@ 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)
 
@@ -84,4 +186,4 @@ def main():
             st.error(f"Error reading the file: {str(e)}")
 
 if __name__ == '__main__':
-    main()
+    main()