Update sidebar.py

sidebar.py

@@ -1,32 +1,136 @@
 # import streamlit as st
 
+# 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)
+#         st.logo(self.sidebar_logo, icon_image=self.main_body_logo, size="large")
+
+#         with st.sidebar:
+#             # Password input field (commented out)
+#             # password = st.text_input("Enter Password:", type="password")
+
+#             # 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**: info@mtss.ai
+#                 """)
+#             st.divider()
+#             st.subheader('User Instructions')
+
+#             # Principles text with Markdown formatting
+#             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
+#             - 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)
+
+
 class Sidebar:
     def __init__(self):
         self.main_body_logo = "mimtss.png"
-        self.sidebar_logo = "mimtss_small.png"
-        self.image_width = 300
+        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)
+        st.logo(self.sidebar_logo, icon_image=self.main_body_logo, size="large")
 
         with st.sidebar:
-            # Password input field (commented out)
-            # password = st.text_input("Enter Password:", type="password")
-
             # 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:Help Center and Bug Reporting"):
+            with st.expander("Powered by MTSS.ai"):
                 st.write("""
-                **Contact**: MTSS.ai | Cheyne LeVesseur, PhD
+                **Contact**: Cheyne LeVesseur, PhD
                 **Email**: info@mtss.ai
                 """)
+            
+            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')
 
-            # Principles text with Markdown formatting
+            # Existing user instructions
             user_instructions = """
             - **Step 1**: Upload your Excel file.
             - **Step 2**: Anonymization – student names are replaced with initials for privacy.
@@ -40,7 +144,7 @@ class Sidebar:
             ### **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.
+            - 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.
@@ -56,19 +160,18 @@ class Sidebar:
               - **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.
+            - 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.
+            - 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.
+            - 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
             - 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.
+            - 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)