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 = "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**: 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') # # 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) 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, 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**: info@mtss.ai """) st.divider() # Download button for example spreadsheet st.download_button( label="Download Example Spreadsheet", data=open("Example_LIR_Spreadsheet.xlsx", "rb").read(), file_name="Example_LIR_Spreadsheet.xlsx", mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" ) with st.expander("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) with st.expander("Privacy Assurance"): 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. """ st.markdown(privacy_assurance) with st.expander("Instructions"): 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**. """ st.markdown(instructions) with st.expander("Detailed Instructions"): 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(detailed_instructions)