Update app.py

app.py

@@ -130,6 +130,9 @@ ENGAGED_STR = 'Engaged (Respect, Responsibility, Effort)'
 PARTIALLY_ENGAGED_STR = 'Partially Engaged (about 50%)'
 NOT_ENGAGED_STR = 'Not Engaged (less than 50%)'
 
+import streamlit as st
+import pandas as pd
+
 def main():
     st.title("Intervention Program Analysis")
 
@@ -138,67 +141,73 @@ def main():
 
     if uploaded_file is not None:
         try:
-            # Read the Excel file into a DataFrame
-            # df = pd.read_excel(uploaded_file)
+            # Step 1: Read the Excel file into a DataFrame
+            df = pd.read_excel(uploaded_file)
 
-            # Rename duplicate columns to make them unique
+            # Step 2: Rename duplicate columns to make them unique
+            # Using built-in pandas functionality to handle duplicate column names
             cols = pd.Series(df.columns)
             for dup in cols[cols.duplicated()].unique():
                 cols[cols[cols == dup].index.values.tolist()] = [f"{dup}_{i+1}" if i != 0 else dup for i in range(sum(cols == dup))]
             df.columns = cols
 
-            # Replace student names with initials
+            # Step 3: Replace student names with initials
             df = replace_student_names_with_initials(df)
 
+            # Step 4: Display the uploaded data
             st.subheader("Uploaded Data")
             st.write(df)
 
-            # Ensure expected column is available
+            # Step 5: Ensure expected column is available
             if INTERVENTION_COLUMN not in df.columns:
                 st.error(f"Expected column '{INTERVENTION_COLUMN}' not found.")
                 return
 
-            # Clean up column names
+            # Step 6: Clean up column names
             df.columns = df.columns.str.strip()
 
-            # Compute Intervention Session Statistics
+            # Step 7: Compute and Display Intervention Session Statistics
             intervention_stats = compute_intervention_statistics(df)
             st.subheader("Intervention Session Statistics")
             st.write(intervention_stats)
 
-            # Visualization for Intervention Session Statistics
+            # Step 8: Visualization for Intervention Session Statistics
             intervention_fig = plot_intervention_statistics(intervention_stats)
 
-            # Add download button for Intervention Session Statistics chart
+            # Step 9: Add download button for Intervention Session Statistics chart
             download_chart(intervention_fig, "intervention_statistics_chart.png")
 
-            # Compute Student Metrics
+            # Step 10: Compute Student Metrics
             student_metrics_df = compute_student_metrics(df)
             st.subheader("Student Metrics")
             st.write(student_metrics_df)
 
-            # Visualization for Student Metrics
+            # Step 11: Visualization for Student Metrics
             student_metrics_fig = plot_student_metrics(student_metrics_df)
 
-            # Add download button for Student Metrics chart
+            # Step 12: Add download button for Student Metrics chart
             download_chart(student_metrics_fig, "student_metrics_chart.png")
 
-            # Prepare input for the language model
+            # Step 13: Prepare input for the language model
             llm_input = prepare_llm_input(student_metrics_df)
 
-            # Generate Notes and Recommendations using Hugging Face LLM
+            # Step 14: Generate Notes and Recommendations using Hugging Face LLM
             with st.spinner("Generating AI analysis..."):
                 recommendations = prompt_response_from_hf_llm(llm_input)
 
+            # Step 15: Display AI Analysis
             st.subheader("AI Analysis")
             st.markdown(recommendations)
 
-            # Add download button for LLM output
+            # Step 16: Add download button for LLM output
             download_llm_output(recommendations, "llm_output.txt")
 
         except Exception as e:
             st.error(f"Error reading the file: {str(e)}")
 
+if __name__ == "__main__":
+    main()
+
 def replace_student_names_with_initials(df):
     """Replace student names in column headers with initials."""
     updated_columns = []