Update app.py

app.py

@@ -42,7 +42,7 @@ with st.sidebar:
     # Set the desired width in pixels
     image_width = 300  
     # Define the path to the image
-    image_path = "mimtss.png"
+    image_path = "/Users/cheynelevesseur/Desktop/Manual Library/Python_Code/LLM_Projects_1/intervention_analysis_app/mimtss_logo.png"
     # Display the image
     st.image(image_path, width=image_width)
     
@@ -130,81 +130,64 @@ 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")
 
-    uploaded_file = st.file_uploader("Upload your Excel file", type=["xlsx"], key="unique_excel_uploader")
+    # File uploader
+    uploaded_file = st.file_uploader("Upload your Excel file", type=["xlsx"])
 
     if uploaded_file is not None:
         try:
-            # Step 1: Read the Excel file into a DataFrame
-            df = pd.read_excel(uploaded_file, header=0)
-
-            # Step 2: Manually handle duplicate columns
-            def make_unique(column_names):
-                seen = {}
-                for i, column in enumerate(column_names):
-                    if column not in seen:
-                        seen[column] = 1
-                    else:
-                        seen[column] += 1
-                        column_names[i] = f"{column}_{seen[column]}"
-                return column_names
-
-            df.columns = make_unique(list(df.columns))
-
-            # Step 3: Replace student names with initials
+            # Read the Excel file into a DataFrame
+            df = pd.read_excel(uploaded_file)
+
+            # 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)
+            st.write(df  # Display only the first four rows
 
-            # Step 5: Ensure expected column is available
+            # Ensure expected column is available
             if INTERVENTION_COLUMN not in df.columns:
                 st.error(f"Expected column '{INTERVENTION_COLUMN}' not found.")
                 return
 
-            # Step 6: Clean up column names
+            # Clean up column names
             df.columns = df.columns.str.strip()
 
-            # Step 7: Compute and Display Intervention Session Statistics
+            # Compute Intervention Session Statistics
             intervention_stats = compute_intervention_statistics(df)
             st.subheader("Intervention Session Statistics")
             st.write(intervention_stats)
 
-            # Step 8: Visualization for Intervention Session Statistics
+            # Visualization for Intervention Session Statistics
             intervention_fig = plot_intervention_statistics(intervention_stats)
 
-            # Step 9: Add download button for Intervention Session Statistics chart
+            # Add download button for Intervention Session Statistics chart
             download_chart(intervention_fig, "intervention_statistics_chart.png")
 
-            # Step 10: Compute Student Metrics
+            # Compute Student Metrics
             student_metrics_df = compute_student_metrics(df)
             st.subheader("Student Metrics")
             st.write(student_metrics_df)
 
-            # Step 11: Visualization for Student Metrics
+            # Visualization for Student Metrics
             student_metrics_fig = plot_student_metrics(student_metrics_df)
 
-            # Step 12: Add download button for Student Metrics chart
+            # Add download button for Student Metrics chart
             download_chart(student_metrics_fig, "student_metrics_chart.png")
 
-            # Step 13: Prepare input for the language model
+            # Prepare input for the language model
             llm_input = prepare_llm_input(student_metrics_df)
 
-            # Step 14: Generate Notes and Recommendations using Hugging Face LLM
+            # 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)
 
-            # Step 16: Add download button for LLM output
+            # Add download button for LLM output
             download_llm_output(recommendations, "llm_output.txt")
 
         except Exception as e:
@@ -259,7 +242,6 @@ def compute_intervention_statistics(df):
     stats_df = pd.DataFrame(stats)
     return stats_df
 
-
 def plot_intervention_statistics(intervention_stats):
     # Create a stacked bar chart for sessions held and not held
     sessions_held = intervention_stats['Intervention Sessions Held'].values[0]
@@ -281,8 +263,6 @@ def plot_intervention_statistics(intervention_stats):
 
     return fig
 
-    
-
 def compute_student_metrics(df):
     # Filter DataFrame for sessions where intervention happened
     intervention_df = df[df[INTERVENTION_COLUMN].str.strip().str.lower() == 'yes']
@@ -294,12 +274,7 @@ def compute_student_metrics(df):
     student_metrics = {}
 
     for col in student_columns:
-        try:
-            # Extract student name or initials, removing any suffixes added for uniqueness
-            student_name = re.match(r'Student Attendance \[(.+?)\]', col.split('_')[0]).group(1)
-        except AttributeError:
-            st.warning(f"Unexpected column name format: {col}. Skipping this column.")
-            continue
+        student_name = col.replace('Student Attendance [', '').replace(']', '').strip()
         # Get the attendance data for the student
         student_data = intervention_df[[col]].copy()