Create sidebar.py

sidebar.py

@@ -0,0 +1,74 @@
+import streamlit as st
+
+class Sidebar:
+    def __init__(self):
+        self.main_body_logo = "mimtss.png"
+        self.sidebar_logo = "mimtss_small.png"
+        self.image_width = 300
+        self.image_path = "mimtss.png"
+
+    def display(self):
+        st.logo(self.sidebar_logo, icon_image=self.main_body_logo)
+
+        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("Need help and report a bug"):
+                st.write("""
+                **Contact**: Cheyne LeVesseur, PhD  
+                **Email**: [email protected]
+                """)
+            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)