sidebar.py

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**: clevesseur@mimtss.org
                """)
            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)