| """ |
| Interactive Montreal Cognitive Assessment (MoCA) v8.1 |
| Streamlit App for Hugging Face Spaces Deployment |
| |
| To deploy: |
| 1. Create a Hugging Face Space (https://huggingface.co/spaces) |
| 2. Choose "Streamlit" as SDK |
| 3. Upload this file as app.py |
| 4. Create a requirements.txt with: streamlit, pandas, matplotlib, numpy |
| """ |
|
|
| import streamlit as st |
| import pandas as pd |
| import matplotlib.pyplot as plt |
| import numpy as np |
| from datetime import datetime |
| import json |
|
|
| |
| st.set_page_config( |
| page_title="MoCA Assessment", |
| layout="wide", |
| initial_sidebar_state="expanded" |
| ) |
|
|
| |
| st.markdown(""" |
| <style> |
| .main { |
| padding: 1rem; |
| } |
| .stAlert { |
| margin: 1rem 0; |
| } |
| </style> |
| """, unsafe_allow_html=True) |
|
|
| st.title("🧠 Interactive Montreal Cognitive Assessment (MoCA) v8.1") |
|
|
| st.markdown(""" |
| --- |
| **📋 About this tool:** |
| - Digital implementation of the Montreal Cognitive Assessment |
| - Assessment takes approximately 10-15 minutes |
| - For research and educational purposes |
| |
| **⚠️ DISCLAIMER:** |
| Clinical interpretation must be done by a qualified healthcare professional. |
| Do NOT use this for diagnosis without professional oversight. |
| |
| **Normal Score:** ≥26/30 (add 1 point if education ≤12 years) |
| |
| --- |
| """) |
|
|
| |
| if 'test_data' not in st.session_state: |
| st.session_state.test_data = { |
| 'demographics': {}, |
| 'responses': {}, |
| 'scores': {}, |
| 'timestamp': datetime.now().isoformat() |
| } |
|
|
| test_data = st.session_state.test_data |
|
|
| |
| with st.sidebar: |
| st.header("📍 Assessment Steps") |
| step = st.radio( |
| "Select Step:", |
| [ |
| "1️⃣ Demographics", |
| "2️⃣ Visuospatial", |
| "3️⃣ Naming", |
| "4️⃣ Memory (Learning)", |
| "5️⃣ Attention", |
| "6️⃣ Language", |
| "7️⃣ Delayed Recall", |
| "8️⃣ Orientation", |
| "📊 Results" |
| ] |
| ) |
| |
| st.divider() |
| |
| |
| completed_sections = len([s for s in test_data['scores'].keys() if s != 'education_bonus' and s != 'mis']) |
| st.metric("Progress", f"{completed_sections}/8 sections") |
|
|
| |
| if "1️⃣" in step: |
| st.header("👤 Participant Information") |
| |
| col1, col2 = st.columns(2) |
| |
| with col1: |
| name = st.text_input( |
| "Name (optional):", |
| value=test_data['demographics'].get('name', ''), |
| help="For record keeping only" |
| ) |
| age = st.slider( |
| "Age:", |
| min_value=18, |
| max_value=100, |
| value=test_data['demographics'].get('age', 65), |
| step=1 |
| ) |
| |
| with col2: |
| education = st.slider( |
| "Years of Education:", |
| min_value=0, |
| max_value=20, |
| value=test_data['demographics'].get('education', 12), |
| step=1, |
| help="Total formal education completed" |
| ) |
| sex = st.selectbox( |
| "Sex:", |
| ["Not Specified", "Male", "Female", "Other"], |
| index=["Not Specified", "Male", "Female", "Other"].index( |
| test_data['demographics'].get('sex', "Not Specified") |
| ) |
| ) |
| |
| language = st.selectbox( |
| "Native Language:", |
| ["English", "Other"], |
| index=0 if test_data['demographics'].get('language') == "English" else 1 |
| ) |
| |
| if st.button("✅ Save Demographics & Continue", key="demo_btn", use_container_width=True): |
| test_data['demographics'] = { |
| 'name': name, |
| 'age': age, |
| 'education': education, |
| 'sex': sex, |
| 'language': language |
| } |
| st.session_state.test_data = test_data |
| st.success("✓ Demographics saved! Proceed to next step.") |
|
|
| |
| elif "2️⃣" in step: |
| st.header("🎨 Visuospatial & Executive Function") |
| |
| col1, col2 = st.columns(2) |
| |
| with col1: |
| st.subheader("1️⃣ Trail Making Test") |
| st.info(""" |
| **Instructions:** Draw a line from number to letter in ascending order: |
| 1 → A → 2 → B → 3 → C → 4 → D → 5 → E |
| |
| **Important:** Lines should not cross. Do not connect E back to 1. |
| """) |
| trail = st.radio( |
| "Result:", |
| ["✓ Completed correctly (no crossing lines)", "✗ Errors or lines crossed"], |
| key="trail_radio" |
| ) |
| |
| with col2: |
| st.subheader("2️⃣ Cube Drawing") |
| st.info(""" |
| **Instructions:** Copy this cube drawing as accurately as you can. |
| |
| **Scoring:** 3-dimensional, all lines drawn, lines meet with little space, |
| no extra lines, lines relatively parallel. |
| """) |
| cube = st.radio( |
| "Result:", |
| ["✓ Correctly executed", "✗ Errors in drawing"], |
| key="cube_radio" |
| ) |
| |
| st.subheader("3️⃣ Clock Drawing") |
| st.info("**Instructions:** Draw a clock. Put in all numbers and set time to 10 past 11.") |
| |
| col1, col2, col3 = st.columns(3) |
| with col1: |
| clock_contour = st.checkbox("✓ Contour correct (circle/square)") |
| with col2: |
| clock_numbers = st.checkbox("✓ All numbers present & correct order") |
| with col3: |
| clock_hands = st.checkbox("✓ Hands correct (short hour, long minute, centered)") |
| |
| if st.button("✅ Save Visuospatial & Continue", key="vis_btn", use_container_width=True): |
| trail_pts = 1 if "✓" in trail else 0 |
| cube_pts = 1 if "✓" in cube else 0 |
| clock_pts = sum([clock_contour, clock_numbers, clock_hands]) |
| |
| test_data['responses']['visuospatial'] = { |
| 'trail': trail, 'cube': cube, |
| 'clock_contour': clock_contour, |
| 'clock_numbers': clock_numbers, |
| 'clock_hands': clock_hands |
| } |
| test_data['scores']['visuospatial'] = trail_pts + cube_pts + clock_pts |
| st.session_state.test_data = test_data |
| st.success(f"✓ Visuospatial saved! Points: {trail_pts + cube_pts + clock_pts}/5") |
|
|
| |
| elif "3️⃣" in step: |
| st.header("🦁 Naming Test") |
| |
| st.info(""" |
| **Instructions:** The examiner points to three animals. |
| What name did the participant give for each? |
| """) |
| |
| st.markdown("### Animals:") |
| col1, col2, col3 = st.columns(3) |
| |
| with col1: |
| st.markdown("**Animal 1: Lion**") |
| animal1 = st.text_input("Participant's answer:", key="animal1", placeholder="e.g., lion") |
| |
| with col2: |
| st.markdown("**Animal 2: Rhinoceros**") |
| animal2 = st.text_input("Participant's answer:", key="animal2", placeholder="e.g., rhino") |
| |
| with col3: |
| st.markdown("**Animal 3: Camel**") |
| animal3 = st.text_input("Participant's answer:", key="animal3", placeholder="e.g., camel") |
| |
| if st.button("✅ Save Naming & Continue", key="naming_btn", use_container_width=True): |
| score = 0 |
| if 'lion' in animal1.lower(): |
| score += 1 |
| if any(x in animal2.lower() for x in ['rhino', 'rhinoceros']): |
| score += 1 |
| if any(x in animal3.lower() for x in ['camel', 'dromedary']): |
| score += 1 |
| |
| test_data['responses']['naming'] = { |
| 'animal1': animal1, 'animal2': animal2, 'animal3': animal3 |
| } |
| test_data['scores']['naming'] = score |
| st.session_state.test_data = test_data |
| st.success(f"✓ Naming saved! Points: {score}/3") |
|
|
| |
| elif "4️⃣" in step: |
| st.header("🧠 Memory Test - Learning Phase") |
| |
| st.warning(""" |
| ⚠️ **IMPORTANT:** The participant must REMEMBER these words. |
| They will be tested on them later in the assessment. |
| """) |
| |
| st.markdown(""" |
| ### 📝 WORDS TO READ (at rate of 1 word per second): |
| |
| # **FACE • VELVET • CHURCH • DAISY • RED** |
| """) |
| |
| st.info(""" |
| **Examiner Instructions:** |
| "This is a memory test. I am going to read a list of words that you will |
| have to remember now and later on. Listen carefully. When I am through, |
| tell me as many words as you can remember." |
| """) |
| |
| if st.button("✅ Participant Heard Words - Continue to Attention", |
| key="memory_btn", use_container_width=True): |
| test_data['responses']['memory_words'] = ['FACE', 'VELVET', 'CHURCH', 'DAISY', 'RED'] |
| st.session_state.test_data = test_data |
| st.success("✓ Memory encoding recorded. Proceed to Attention section.") |
|
|
| |
| elif "5️⃣" in step: |
| st.header("👁️ Attention Tests") |
| |
| with st.container(border=True): |
| st.subheader("1️⃣ Forward Digit Span") |
| st.markdown("**Read at 1 digit/second:** 2 1 8 5 4") |
| digit_forward = st.text_input( |
| "Participant repeated:", |
| placeholder="e.g., 2 1 8 5 4 or 2-1-8-5-4", |
| key="digit_forward" |
| ) |
| |
| with st.container(border=True): |
| st.subheader("2️⃣ Backward Digit Span") |
| st.markdown("**Read at 1 digit/second:** 7 4 2") |
| st.markdown("*Correct backward answer: 2 4 7*") |
| digit_backward = st.text_input( |
| "Participant repeated:", |
| placeholder="e.g., 2 4 7", |
| key="digit_backward" |
| ) |
| |
| with st.container(border=True): |
| st.subheader("3️⃣ Vigilance (Tap on Letter A)") |
| st.markdown("**Read letters at 1/second. Participant taps on every 'A':**") |
| st.markdown("F B A C M N A A J K L B A F A K D E A A A J A M O F A A B") |
| vig_errors = st.slider("Errors (tapped wrong or missed A):", 0, 10, value=0, key="vig_errors") |
| |
| with st.container(border=True): |
| st.subheader("4️⃣ Serial 7 Subtraction") |
| st.markdown(""" |
| **Instruction:** "Count backward by 7s starting from 100." |
| |
| Expected sequence: 93 → 86 → 79 → 72 → 65 |
| """) |
| serial7_correct = st.slider( |
| "Number of correct subtractions:", |
| 0, 5, value=0, key="serial7_correct" |
| ) |
| |
| if st.button("✅ Save Attention & Continue", key="attention_btn", use_container_width=True): |
| score = 0 |
| score += 1 if '2 1 8 5 4' in digit_forward.replace('-', ' ').replace(',', ' ') else 0 |
| score += 1 if '2 4 7' in digit_backward.replace('-', ' ').replace(',', ' ') else 0 |
| score += 1 if vig_errors <= 1 else 0 |
| |
| if serial7_correct >= 4: |
| score += 3 |
| elif serial7_correct >= 2: |
| score += 2 |
| elif serial7_correct >= 1: |
| score += 1 |
| |
| test_data['responses']['attention'] = { |
| 'digit_forward': digit_forward, |
| 'digit_backward': digit_backward, |
| 'vigilance_errors': vig_errors, |
| 'serial7_correct': serial7_correct |
| } |
| test_data['scores']['attention'] = score |
| st.session_state.test_data = test_data |
| st.success(f"✓ Attention saved! Points: {score}/8") |
|
|
| |
| elif "6️⃣" in step: |
| st.header("💬 Language Tests") |
| |
| with st.container(border=True): |
| st.subheader("1️⃣ Sentence Repetition") |
| st.markdown(""" |
| **Instruction:** "I am going to read you a sentence. |
| Repeat it exactly as I say it." |
| """) |
| |
| st.markdown("**Sentence 1:**") |
| st.markdown('> "I only know that John is the one to help today."') |
| sent1 = st.checkbox( |
| "✓ Repeated exactly (watch for omissions, substitutions, grammar errors)", |
| key="sent1" |
| ) |
| |
| st.markdown("**Sentence 2:**") |
| st.markdown('> "The cat always hid under the couch when dogs were in the room."') |
| sent2 = st.checkbox( |
| "✓ Repeated exactly", |
| key="sent2" |
| ) |
| |
| with st.container(border=True): |
| st.subheader("2️⃣ Verbal Fluency (Letter F)") |
| st.markdown(""" |
| **Instruction:** "Tell me as many words as you can think of |
| that begin with the letter F. I will tell you to stop after 60 seconds." |
| |
| *Rules: No proper nouns, numbers, or different verb forms* |
| """) |
| fluency = st.slider( |
| "Number of valid words starting with F:", |
| 0, 30, value=0, key="fluency" |
| ) |
| |
| with st.container(border=True): |
| st.subheader("3️⃣ Abstraction") |
| |
| st.markdown("**Pair 1: Train and Bicycle**") |
| st.markdown("*Acceptable answers: means of transportation, means of travel, you take trips*") |
| st.markdown("*NOT acceptable: they have wheels*") |
| abstract1 = st.radio( |
| "Response:", |
| ["✓ Correct (transportation/travel)", "✗ Incorrect or concrete"], |
| key="abstract1" |
| ) |
| |
| st.markdown("**Pair 2: Ruler and Watch**") |
| st.markdown("*Acceptable answers: measuring instruments, used to measure*") |
| st.markdown("*NOT acceptable: they have numbers*") |
| abstract2 = st.radio( |
| "Response:", |
| ["✓ Correct (measuring instruments)", "✗ Incorrect or concrete"], |
| key="abstract2" |
| ) |
| |
| if st.button("✅ Save Language & Continue", key="language_btn", use_container_width=True): |
| score = 0 |
| score += int(sent1) |
| score += int(sent2) |
| score += 1 if fluency >= 11 else 0 |
| score += 1 if "✓" in abstract1 else 0 |
| score += 1 if "✓" in abstract2 else 0 |
| |
| test_data['responses']['language'] = { |
| 'sent1': sent1, 'sent2': sent2, 'fluency': fluency, |
| 'abstract1': abstract1, 'abstract2': abstract2 |
| } |
| test_data['scores']['language'] = score |
| st.session_state.test_data = test_data |
| st.success(f"✓ Language saved! Points: {score}/5") |
|
|
| |
| elif "7️⃣" in step: |
| st.header("🧠 Delayed Recall & Memory Index Score") |
| |
| st.warning("⚠️ **Recall the 5 words from earlier:**") |
| st.markdown("# **FACE • VELVET • CHURCH • DAISY • RED**") |
| |
| with st.container(border=True): |
| st.subheader("Spontaneous Recall (WITHOUT CUES)") |
| st.markdown("Which words did participant recall without any help?") |
| |
| col1, col2, col3 = st.columns(3) |
| with col1: |
| recall_face = st.checkbox("FACE", key="recall_face") |
| recall_velvet = st.checkbox("VELVET", key="recall_velvet") |
| with col2: |
| recall_church = st.checkbox("CHURCH", key="recall_church") |
| recall_daisy = st.checkbox("DAISY", key="recall_daisy") |
| with col3: |
| recall_red = st.checkbox("RED", key="recall_red") |
| |
| with st.container(border=True): |
| st.subheader("With Category Cues (if needed)") |
| st.markdown("For words not recalled, give category hints:") |
| |
| col1, col2, col3 = st.columns(3) |
| with col1: |
| cue_face = st.checkbox("FACE (body part)", key="cue_face") |
| cue_velvet = st.checkbox("VELVET (fabric)", key="cue_velvet") |
| with col2: |
| cue_church = st.checkbox("CHURCH (building)", key="cue_church") |
| cue_daisy = st.checkbox("DAISY (flower)", key="cue_daisy") |
| with col3: |
| cue_red = st.checkbox("RED (color)", key="cue_red") |
| |
| with st.container(border=True): |
| st.subheader("With Multiple-Choice Only") |
| st.markdown("Words only recalled when given multiple choice options:") |
| |
| col1, col2, col3 = st.columns(3) |
| with col1: |
| mult_face = st.checkbox("FACE (MC only)", key="mult_face") |
| mult_velvet = st.checkbox("VELVET (MC only)", key="mult_velvet") |
| with col2: |
| mult_church = st.checkbox("CHURCH (MC only)", key="mult_church") |
| mult_daisy = st.checkbox("DAISY (MC only)", key="mult_daisy") |
| with col3: |
| mult_red = st.checkbox("RED (MC only)", key="mult_red") |
| |
| if st.button("✅ Save Delayed Recall & Continue", key="memory_recall_btn", use_container_width=True): |
| spontaneous = sum([ |
| recall_face, recall_velvet, recall_church, recall_daisy, recall_red |
| ]) |
| |
| category = sum([ |
| cue_face and not recall_face, |
| cue_velvet and not recall_velvet, |
| cue_church and not recall_church, |
| cue_daisy and not recall_daisy, |
| cue_red and not recall_red |
| ]) |
| |
| multiple = sum([ |
| mult_face and not recall_face and not cue_face, |
| mult_velvet and not recall_velvet and not cue_velvet, |
| mult_church and not recall_church and not cue_church, |
| mult_daisy and not recall_daisy and not cue_daisy, |
| mult_red and not recall_red and not cue_red |
| ]) |
| |
| mis = (spontaneous * 3) + (category * 2) + (multiple * 1) |
| |
| test_data['responses']['delayed_recall'] = { |
| 'spontaneous': spontaneous, |
| 'category': category, |
| 'multiple': multiple |
| } |
| test_data['scores']['delayed_recall'] = spontaneous |
| test_data['scores']['mis'] = mis |
| st.session_state.test_data = test_data |
| |
| st.success(f""" |
| ✓ Delayed Recall Saved! |
| - Spontaneous: {spontaneous}/5 points |
| - Memory Index Score (MIS): {mis}/15 points |
| """) |
|
|
| |
| elif "8️⃣" in step: |
| st.header("📍 Orientation") |
| |
| today = datetime.now() |
| st.info(f"Today's date should be: **{today.strftime('%A, %B %d, %Y')}**") |
| |
| st.markdown("### Check which orientation items are correct:") |
| |
| col1, col2, col3 = st.columns(3) |
| with col1: |
| orient_date = st.checkbox("✓ Date correct (exact, no ±1 day error)") |
| orient_month = st.checkbox("✓ Month correct") |
| orient_year = st.checkbox("✓ Year correct") |
| |
| with col2: |
| orient_day = st.checkbox("✓ Day of week correct") |
| orient_place = st.checkbox("✓ Place/Location correct") |
| |
| with col3: |
| orient_city = st.checkbox("✓ City correct") |
| |
| if st.button("✅ Complete Assessment", key="orient_btn", use_container_width=True): |
| score = sum([ |
| orient_date, orient_month, orient_year, |
| orient_day, orient_place, orient_city |
| ]) |
| test_data['scores']['orientation'] = score |
| st.session_state.test_data = test_data |
| st.success(f"✓ Orientation saved! Points: {score}/6") |
| st.balloons() |
|
|
| |
| elif "📊" in step: |
| st.header("📊 Assessment Results & Scoring") |
| |
| def calc_total(): |
| total = sum([ |
| test_data['scores'].get('visuospatial', 0), |
| test_data['scores'].get('naming', 0), |
| test_data['scores'].get('attention', 0), |
| test_data['scores'].get('language', 0), |
| test_data['scores'].get('delayed_recall', 0), |
| test_data['scores'].get('orientation', 0) |
| ]) |
| |
| if test_data['demographics'].get('education', 0) <= 12: |
| total += 1 |
| |
| return min(total, 30) |
| |
| total_score = calc_total() |
| is_normal = total_score >= 26 |
| |
| |
| summary_data = { |
| 'Domain': ['Visuospatial/Executive', 'Naming', 'Attention', 'Language', 'Delayed Recall', 'Orientation'], |
| 'Points': [ |
| test_data['scores'].get('visuospatial', 0), |
| test_data['scores'].get('naming', 0), |
| test_data['scores'].get('attention', 0), |
| test_data['scores'].get('language', 0), |
| test_data['scores'].get('delayed_recall', 0), |
| test_data['scores'].get('orientation', 0) |
| ], |
| 'Max': [5, 3, 8, 5, 5, 6] |
| } |
| |
| df = pd.DataFrame(summary_data) |
| |
| st.subheader("Score Breakdown by Domain") |
| st.dataframe(df, use_container_width=True, hide_index=True) |
| |
| |
| col1, col2, col3, col4 = st.columns(4) |
| |
| with col1: |
| st.metric("📊 Total Score", f"{total_score}/30", |
| delta="Normal" if is_normal else "Possible Impairment") |
| |
| with col2: |
| st.metric("📈 Status", "✓ Normal" if is_normal else "⚠ Below 26") |
| |
| with col3: |
| st.metric("🧠 MIS Score", f"{test_data['scores'].get('mis', 0)}/15", |
| help="Memory Index Score - shows type of memory deficit") |
| |
| with col4: |
| st.metric("👤 Participant", |
| test_data['demographics'].get('name', 'Not provided'), |
| test_data['demographics'].get('age', 'N/A')) |
| |
| |
| st.subheader("Score Visualization") |
| |
| col1, col2 = st.columns(2) |
| |
| with col1: |
| fig1, ax1 = plt.subplots(figsize=(10, 5)) |
| scores = df['Points'].tolist() |
| max_scores = df['Max'].tolist() |
| x = np.arange(len(df['Domain'])) |
| |
| ax1.bar(x - 0.2, scores, 0.4, label='Participant Score', color='steelblue', alpha=0.8) |
| ax1.bar(x + 0.2, max_scores, 0.4, label='Maximum', color='lightgray', alpha=0.5) |
| ax1.set_ylabel('Points', fontsize=11) |
| ax1.set_title('MoCA Score by Cognitive Domain', fontsize=12, fontweight='bold') |
| ax1.set_xticks(x) |
| ax1.set_xticklabels(df['Domain'], rotation=45, ha='right', fontsize=9) |
| ax1.legend() |
| ax1.grid(axis='y', alpha=0.3, linestyle='--') |
| ax1.set_ylim(0, 10) |
| |
| plt.tight_layout() |
| st.pyplot(fig1, use_container_width=True) |
| |
| with col2: |
| fig2, ax2 = plt.subplots(figsize=(8, 6)) |
| colors = ['#2ecc71' if is_normal else '#e74c3c', '#ecf0f1'] |
| explode = (0.1, 0) |
| |
| wedges, texts, autotexts = ax2.pie( |
| [total_score, 30 - total_score], |
| labels=[f'Scored: {total_score}', f'Remaining: {30 - total_score}'], |
| autopct='%1.1f%%', |
| colors=colors, |
| explode=explode, |
| startangle=90, |
| textprops={'fontsize': 11} |
| ) |
| |
| status_text = "✓ NORMAL COGNITION" if is_normal else "⚠ POSSIBLE IMPAIRMENT" |
| ax2.set_title(f'Overall Score\n{status_text}', fontsize=13, fontweight='bold') |
| |
| st.pyplot(fig2, use_container_width=True) |
| |
| |
| st.divider() |
| st.subheader("📋 Clinical Interpretation") |
| |
| if is_normal: |
| st.success(f""" |
| **✓ NORMAL COGNITION** |
| |
| Total Score: **{total_score}/30** |
| |
| The participant's performance falls within the normal range for cognitive function. |
| A score of 26 or above is considered normal on the MoCA. |
| """) |
| else: |
| st.warning(f""" |
| **⚠ POSSIBLE COGNITIVE IMPAIRMENT** |
| |
| Total Score: **{total_score}/30** |
| |
| The participant's score is below 26, suggesting possible mild cognitive impairment. |
| |
| **Areas of concern:** |
| """) |
| |
| for idx, row in df.iterrows(): |
| if row['Points'] < (row['Max'] * 0.75): |
| st.markdown(f"- **{row['Domain']}:** {row['Points']}/{row['Max']} points") |
| |
| |
| st.info(f""" |
| **Memory Index Score (MIS): {test_data['scores'].get('mis', 0)}/15** |
| |
| The MIS reveals the nature of memory problems: |
| - **High spontaneous recall:** Memory retrieval is intact |
| - **Improved with cues:** Retrieval problem (generally better prognosis) |
| - **No improvement with cues:** Encoding problem (suggests more severe impairment) |
| """) |
| |
| |
| st.divider() |
| st.subheader("💾 Export Results") |
| |
| |
| export_df = df.copy() |
| export_df.loc[len(export_df)] = ['TOTAL', total_score, 30] |
| export_df.loc[len(export_df)] = ['MIS Score', test_data['scores'].get('mis', 0), 15] |
| |
| csv = export_df.to_csv(index=False) |
| json_str = json.dumps(test_data, indent=2, default=str) |
| |
| col1, col2 = st.columns(2) |
| |
| with col1: |
| st.download_button( |
| label="📥 Download as CSV", |
| data=csv, |
| file_name=f"moca_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv", |
| mime="text/csv" |
| ) |
| |
| with col2: |
| st.download_button( |
| label="📥 Download as JSON", |
| data=json_str, |
| file_name=f"moca_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json", |
| mime="application/json" |
| ) |
| |
| |
| st.divider() |
| st.error(""" |
| ### ⚠️ CRITICAL DISCLAIMER |
| |
| **This digital assessment is for RESEARCH AND EDUCATIONAL PURPOSES ONLY.** |
| |
| Clinical interpretation must ONLY be performed by: |
| - A qualified healthcare professional (MD, DO, NP, PA) |
| - With training in cognitive assessment |
| - Who understands the participant's complete medical history |
| - Who knows about current medications and their cognitive effects |
| - Who can assess the overall clinical context and comorbidities |
| |
| **DO NOT USE THIS FOR DIAGNOSIS WITHOUT PROFESSIONAL OVERSIGHT.** |
| |
| Results should be reviewed with a healthcare provider before any clinical decisions are made. |
| """) |
|
|
| |
| st.divider() |
| st.markdown(""" |
| <div style='text-align: center; color: gray; font-size: 0.85em;'> |
| <p>Interactive MoCA v8.1 Assessment Tool | Based on the Montreal Cognitive Assessment</p> |
| <p>Original MoCA © Z. Nasreddine MD | www.mocatest.org</p> |
| <p>This implementation is for research and educational purposes only.</p> |
| </div> |
| """, unsafe_allow_html=True) |
|
|
