Update sidebar.py

sidebar.py

@@ -75,6 +75,110 @@ import streamlit as st
 #             st.markdown(user_instructions)
 
 
+# class Sidebar:
+#     def __init__(self):
+#         self.main_body_logo = "mimtss.png"
+#         self.sidebar_logo = "mtss.ai_small.png"
+#         self.image_width = 200
+#         self.image_path = "mimtss.png"
+
+#     def display(self):
+#         st.logo(self.sidebar_logo, icon_image=self.main_body_logo, size="large")
+
+#         with st.sidebar:
+#             # Display the image
+#             st.image(self.image_path, width=self.image_width)
+
+#             # Toggle for Help and Report a Bug
+#             with st.expander("Powered by MTSS.ai"):
+#                 st.write("""
+#                 **Contact**: Cheyne LeVesseur, PhD
+#                 **Email**: [email protected]
+#                 """)
+            
+#             st.divider()
+            
+#             st.subheader('Spreadsheet Headers')
+            
+#             headers_info = """
+#             Your spreadsheet must include the following headers for proper analysis:
+
+#             1. **Date Column**: 
+#                - "Date of Session" or "Date"
+            
+#             2. **Intervention Column**:
+#                - "Did the intervention happen today?" or 
+#                - "Did the intervention take place today?"
+            
+#             3. **Student Attendance Columns**:
+#                - Format: "Student Attendance [student name]"
+#                - Options: Engaged, Partially Engaged, Not Engaged, Absent
+#                - Example: "Student Attendance [Charlie Gordon]"
+            
+#             #### Important Note on Student Names:
+#             - For students with the same initials, you must use a unique identifier to distinguish them.
+#             - Best practices for unique identifiers:
+#               -  Add a middle name: "Charlie Gordon" --> "Charlie A. Gordon"
+#               -  Use a unique identifier: "Charlie Gordon 1" and "Clarissa Gao 2"
+            
+#             This ensures that when names are truncated to initials, each student has a unique identifier.
+#             """
+            
+#             st.markdown(headers_info)
+            
+#             st.divider()
+            
+#             st.subheader('User Instructions')
+
+#             # Existing user instructions
+#             user_instructions = """
+#             - **Step 1**: Upload your Excel file.
+#             - **Step 2**: Anonymization – student names are replaced with initials for privacy.
+#             - **Step 3**: Review anonymized data.
+#             - **Step 4**: View **intervention session statistics**.
+#             - **Step 5**: Review **student attendance and engagement metrics**.
+#             - **Step 6**: Review AI-generated **insights and recommendations**.
+#             ### **Privacy Assurance**
+#             - **No full names** are ever displayed or sent to the AI model—only initials are used.
+#             - This ensures that sensitive data remains protected throughout the entire process.
+#             ### **Detailed Instructions**
+#             #### **1. Upload Your Excel File**
+#             - Start by uploading an Excel file that contains intervention data. 
+#             - Click on the **"Upload your Excel file"** button and select your `.xlsx` file from your computer.
+#             **Note**: Your file should have columns like "Did the intervention happen today?" and "Student Attendance [FirstName LastName]" for the analysis to work correctly.
+#             #### **2. Automated Name Anonymization**
+#             - Once the file is uploaded, the app will **automatically replace student names with initials** in the "Student Attendance" columns.
+#               - For example, **"Student Attendance [Cheyne LeVesseur]"** will be displayed as **"Student Attendance [CL]"**.
+#               - If the student only has a first name, like **"Student Attendance [Cheyne]"**, it will be displayed as **"Student Attendance [C]"**.
+#             - This anonymization helps to **protect student privacy**, ensuring that full names are not visible or sent to the AI language model.
+#             #### **3. Review the Uploaded Data**
+#             - You will see the entire table of anonymized data to verify that the information has been uploaded correctly and that names have been replaced with initials.
+#             #### **4. Intervention Session Statistics**
+#             - The app will calculate and display statistics related to intervention sessions, such as:
+#               - **Total Number of Days Available**
+#               - **Intervention Sessions Held**
+#               - **Intervention Sessions Not Held**
+#               - **Intervention Frequency (%)**
+#             - A **stacked bar chart** will be shown to visualize the number of sessions held versus not held.
+#             - If you need to save the visualization, click the **"Download Chart"** button to download it as a `.png` file.
+#             #### **5. Student Metrics Analysis**
+#             - The app will also calculate metrics for each student:
+#               - **Attendance (%)** – The percentage of intervention sessions attended.
+#               - **Engagement (%)** – The level of engagement during attended sessions.
+#             - These metrics will be presented in a **line graph** that shows attendance and engagement for each student.
+#             - You can click the **"Download Chart"** button to download the visualization as a `.png` file.
+#             #### **6. Generate AI Analysis and Recommendations**
+#             - The app will prepare data from the student metrics to provide notes, key takeaways, and suggestions for improving outcomes using an **AI language model**.
+#             - You will see a **spinner** labeled **"Generating AI analysis…"** while the AI processes the data.
+#               - This step may take a little longer, but the spinner ensures you know that the system is working.
+#             - Once the analysis is complete, the AI's recommendations will be displayed under **"AI Analysis"**.
+#             - You can click the **"Download LLM Output"** button to download the AI-generated recommendations as a `.txt` file for future reference.
+#             """
+#             st.markdown(user_instructions)
+
+
+import streamlit as st
+
 class Sidebar:
     def __init__(self):
         self.main_body_logo = "mimtss.png"
@@ -97,81 +201,96 @@ class Sidebar:
                 """)
             
             st.divider()
-            
-            st.subheader('Spreadsheet Headers')
-            
-            headers_info = """
-            Your spreadsheet must include the following headers for proper analysis:
 
-            1. **Date Column**: 
-               - "Date of Session" or "Date"
-            
-            2. **Intervention Column**:
-               - "Did the intervention happen today?" or 
-               - "Did the intervention take place today?"
-            
-            3. **Student Attendance Columns**:
-               - Format: "Student Attendance [student name]"
-               - Options: Engaged, Partially Engaged, Not Engaged, Absent
-               - Example: "Student Attendance [Charlie Gordon]"
-            
-            #### Important Note on Student Names:
-            - For students with the same initials, you must use a unique identifier to distinguish them.
-            - Best practices for unique identifiers:
-              -  Add a middle name: "Charlie Gordon" --> "Charlie A. Gordon"
-              -  Use a unique identifier: "Charlie Gordon 1" and "Clarissa Gao 2"
-            
-            This ensures that when names are truncated to initials, each student has a unique identifier.
-            """
-            
-            st.markdown(headers_info)
-            
-            st.divider()
-            
-            st.subheader('User Instructions')
-
-            # Existing user instructions
-            user_instructions = """
-            - **Step 1**: Upload your Excel file.
-            - **Step 2**: Anonymization – student names are replaced with initials for privacy.
-            - **Step 3**: Review anonymized data.
-            - **Step 4**: View **intervention session statistics**.
-            - **Step 5**: Review **student attendance and engagement metrics**.
-            - **Step 6**: Review AI-generated **insights and recommendations**.
-            ### **Privacy Assurance**
-            - **No full names** are ever displayed or sent to the AI model—only initials are used.
-            - This ensures that sensitive data remains protected throughout the entire process.
-            ### **Detailed Instructions**
-            #### **1. Upload Your Excel File**
-            - Start by uploading an Excel file that contains intervention data. 
-            - Click on the **"Upload your Excel file"** button and select your `.xlsx` file from your computer.
-            **Note**: Your file should have columns like "Did the intervention happen today?" and "Student Attendance [FirstName LastName]" for the analysis to work correctly.
-            #### **2. Automated Name Anonymization**
-            - Once the file is uploaded, the app will **automatically replace student names with initials** in the "Student Attendance" columns.
-              - For example, **"Student Attendance [Cheyne LeVesseur]"** will be displayed as **"Student Attendance [CL]"**.
-              - If the student only has a first name, like **"Student Attendance [Cheyne]"**, it will be displayed as **"Student Attendance [C]"**.
-            - This anonymization helps to **protect student privacy**, ensuring that full names are not visible or sent to the AI language model.
-            #### **3. Review the Uploaded Data**
-            - You will see the entire table of anonymized data to verify that the information has been uploaded correctly and that names have been replaced with initials.
-            #### **4. Intervention Session Statistics**
-            - The app will calculate and display statistics related to intervention sessions, such as:
-              - **Total Number of Days Available**
-              - **Intervention Sessions Held**
-              - **Intervention Sessions Not Held**
-              - **Intervention Frequency (%)**
-            - A **stacked bar chart** will be shown to visualize the number of sessions held versus not held.
-            - If you need to save the visualization, click the **"Download Chart"** button to download it as a `.png` file.
-            #### **5. Student Metrics Analysis**
-            - The app will also calculate metrics for each student:
-              - **Attendance (%)** – The percentage of intervention sessions attended.
-              - **Engagement (%)** – The level of engagement during attended sessions.
-            - These metrics will be presented in a **line graph** that shows attendance and engagement for each student.
-            - You can click the **"Download Chart"** button to download the visualization as a `.png` file.
-            #### **6. Generate AI Analysis and Recommendations**
-            - The app will prepare data from the student metrics to provide notes, key takeaways, and suggestions for improving outcomes using an **AI language model**.
-            - You will see a **spinner** labeled **"Generating AI analysis…"** while the AI processes the data.
-              - This step may take a little longer, but the spinner ensures you know that the system is working.
-            - Once the analysis is complete, the AI's recommendations will be displayed under **"AI Analysis"**.
-            - You can click the **"Download LLM Output"** button to download the AI-generated recommendations as a `.txt` file for future reference.
-            """
-            st.markdown(user_instructions)
+            # Download button for example spreadsheet
+            st.download_button(
+                label="Download Example Spreadsheet",
+                data=open("Example_LIR_Spreadsheet.xlsx", "rb").read(),
+                file_name="Example_LIR_Spreadsheet.xlsx",
+                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+            )
+
+            with st.expander("Spreadsheet Headers"):
+                headers_info = """
+                Your spreadsheet must include the following headers for proper analysis:
+
+                1. **Date Column**: 
+                   - "Date of Session" or "Date"
+                
+                2. **Intervention Column**:
+                   - "Did the intervention happen today?" or 
+                   - "Did the intervention take place today?"
+                
+                3. **Student Attendance Columns**:
+                   - Format: "Student Attendance [student name]"
+                   - Options: Engaged, Partially Engaged, Not Engaged, Absent
+                   - Example: "Student Attendance [Charlie Gordon]"
+                
+                #### Important Note on Student Names:
+                - For students with the same initials, you must use a unique identifier to distinguish them.
+                - Best practices for unique identifiers:
+                  -  Add a middle name: "Charlie Gordon" --> "Charlie A. Gordon"
+                  -  Use a unique identifier: "Charlie Gordon 1" and "Clarissa Gao 2"
+                
+                This ensures that when names are truncated to initials, each student has a unique identifier.
+                """
+                st.markdown(headers_info)
+
+            with st.expander("Privacy Assurance"):
+                privacy_assurance = """
+                - **No full names** are ever displayed or sent to the AI model—only initials are used.
+                - This ensures that sensitive data remains protected throughout the entire process.
+                """
+                st.markdown(privacy_assurance)
+
+            with st.expander("Instructions"):
+                instructions = """
+                - **Step 1**: Upload your Excel file.
+                - **Step 2**: Anonymization – student names are replaced with initials for privacy.
+                - **Step 3**: Review anonymized data.
+                - **Step 4**: View **intervention session statistics**.
+                - **Step 5**: Review **student attendance and engagement metrics**.
+                - **Step 6**: Review AI-generated **insights and recommendations**.
+                """
+                st.markdown(instructions)
+
+            with st.expander("Detailed Instructions"):
+                detailed_instructions = """
+                #### **1. Upload Your Excel File**
+                - Start by uploading an Excel file that contains intervention data. 
+                - Click on the **"Upload your Excel file"** button and select your `.xlsx` file from your computer.
+                **Note**: Your file should have columns like "Did the intervention happen today?" and "Student Attendance [FirstName LastName]" for the analysis to work correctly.
+
+                #### **2. Automated Name Anonymization**
+                - Once the file is uploaded, the app will **automatically replace student names with initials** in the "Student Attendance" columns.
+                  - For example, **"Student Attendance [Cheyne LeVesseur]"** will be displayed as **"Student Attendance [CL]"**.
+                  - If the student only has a first name, like **"Student Attendance [Cheyne]"**, it will be displayed as **"Student Attendance [C]"**.
+                - This anonymization helps to **protect student privacy**, ensuring that full names are not visible or sent to the AI language model.
+
+                #### **3. Review the Uploaded Data**
+                - You will see the entire table of anonymized data to verify that the information has been uploaded correctly and that names have been replaced with initials.
+
+                #### **4. Intervention Session Statistics**
+                - The app will calculate and display statistics related to intervention sessions, such as:
+                  - **Total Number of Days Available**
+                  - **Intervention Sessions Held**
+                  - **Intervention Sessions Not Held**
+                  - **Intervention Frequency (%)**
+                - A **stacked bar chart** will be shown to visualize the number of sessions held versus not held.
+                - If you need to save the visualization, click the **"Download Chart"** button to download it as a `.png` file.
+
+                #### **5. Student Metrics Analysis**
+                - The app will also calculate metrics for each student:
+                  - **Attendance (%)** – The percentage of intervention sessions attended.
+                  - **Engagement (%)** – The level of engagement during attended sessions.
+                - These metrics will be presented in a **line graph** that shows attendance and engagement for each student.
+                - You can click the **"Download Chart"** button to download the visualization as a `.png` file.
+
+                #### **6. Generate AI Analysis and Recommendations**
+                - The app will prepare data from the student metrics to provide notes, key takeaways, and suggestions for improving outcomes using an **AI language model**.
+                - You will see a **spinner** labeled **"Generating AI analysis…"** while the AI processes the data.
+                  - This step may take a little longer, but the spinner ensures you know that the system is working.
+                - Once the analysis is complete, the AI's recommendations will be displayed under **"AI Analysis"**.
+                - You can click the **"Download LLM Output"** button to download the AI-generated recommendations as a `.txt` file for future reference.
+                """
+                st.markdown(detailed_instructions)