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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) |