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# 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**: [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)
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) |