Update main.py

main.py

@@ -1,8 +1,98 @@
+# 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
-from data_processor import DataProcessor
-from visualization import Visualization
-from ai_analysis import AIAnalysis
+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():
@@ -11,7 +101,7 @@ def main():
 
     # Initialize the sidebar
     sidebar = Sidebar()
-    sidebar.display()  # Display the sidebar
+    sidebar.display()
 
     # Initialize the data processor
     data_processor = DataProcessor()
@@ -67,6 +157,18 @@ def main():
             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 = data_processor.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)
 
@@ -81,7 +183,7 @@ def main():
             ai_analysis.download_llm_output(recommendations, "llm_output.txt")
 
         except Exception as e:
-            st.error(f"Error reading the file: {str(e)}")
+            st.error(f"Error processing the file: {str(e)}")
 
 if __name__ == '__main__':
     main()