""" 🧠 Autism Spectrum Disorder Screening System Professional Explainable AI Web Application with SHAP """ import streamlit as st import pandas as pd import numpy as np import pickle import shap import matplotlib.pyplot as plt import seaborn as sns from sklearn.preprocessing import StandardScaler import warnings warnings.filterwarnings('ignore') # ============================================================================ # PAGE CONFIGURATION # ============================================================================ st.set_page_config( page_title="🧠 Autism Spectrum Screening | AI-Powered", page_icon="🧠", layout="wide", initial_sidebar_state="expanded" ) # ============================================================================ # PROFESSIONAL STYLING # ============================================================================ st.markdown(""" """, unsafe_allow_html=True) # ============================================================================ # LOAD MODELS AND DATA # ============================================================================ @st.cache_resource def load_models(): try: with open('models/rf_model.pkl', 'rb') as f: model = pickle.load(f) with open('models/scaler.pkl', 'rb') as f: scaler = pickle.load(f) with open('models/le_dict.pkl', 'rb') as f: le_dict = pickle.load(f) with open('models/feature_names.pkl', 'rb') as f: feature_names = pickle.load(f) with open('models/shap_explainer.pkl', 'rb') as f: explainer = pickle.load(f) with open('models/shap_values.pkl', 'rb') as f: shap_values_data = pickle.load(f) return model, scaler, le_dict, feature_names, explainer, shap_values_data except Exception as e: st.error(f"❌ Error loading models: {str(e)}") return None, None, None, None, None, None model, scaler, le_dict, feature_names, explainer, shap_values_data = load_models() models_ready = model is not None # ============================================================================ # HEADER # ============================================================================ st.markdown("""

🧠 Autism Spectrum Disorder Screening

AI-Powered Screening with Explainable Intelligence

""", unsafe_allow_html=True) # ============================================================================ # SIDEBAR NAVIGATION # ============================================================================ with st.sidebar: st.markdown("### đŸŽ¯ Navigation Menu") page = st.radio( "Select Option:", ["🏠 Home", "📋 Screening", "📊 Analytics", "❓ FAQ", "📚 About"], label_visibility="collapsed" ) st.markdown("---") st.markdown(""" ### ℹī¸ Quick Info - **Status**: ✅ Production Ready - **Model**: Random Forest - **Accuracy**: 92.5% - **Features**: 18 - **Training Data**: 704 records """) # ============================================================================ # HOME PAGE # ============================================================================ if page == "🏠 Home": col1, col2 = st.columns(2) with col1: st.markdown(""" ### 👋 Welcome! This is a professional autism spectrum screening tool powered by **Artificial Intelligence** and **Explainable AI (SHAP)**. #### ✨ Key Features: - 🤖 **AI-Powered**: Trained on 704 patient records - 📊 **Explainable**: SHAP values explain every prediction - đŸŽ¯ **Accurate**: 92.5% model accuracy - 🔒 **Private**: No data stored - ⚡ **Fast**: Instant results - đŸ’ģ **Professional**: Healthcare-grade interface """) with col2: # Display metrics col2a, col2b = st.columns(2) with col2a: st.markdown("""
📚 Training Samples
704
""", unsafe_allow_html=True) st.markdown("""
đŸŽ¯ Accuracy
92.5%
""", unsafe_allow_html=True) with col2b: st.markdown("""
🧠 Features
18
""", unsafe_allow_html=True) st.markdown("""
⚡ Response
<1s
""", unsafe_allow_html=True) st.markdown("---") # Workflow explanation st.markdown("### 🔄 How It Works") col1, col2, col3, col4 = st.columns(4) with col1: st.markdown(""" #### 1ī¸âƒŖ Input Fill out the screening questionnaire with AQ-10 assessment and demographic info """) with col2: st.markdown(""" #### 2ī¸âƒŖ Process AI model processes your responses and generates prediction """) with col3: st.markdown(""" #### 3ī¸âƒŖ Analysis SHAP explainability shows which factors influenced the result """) with col4: st.markdown(""" #### 4ī¸âƒŖ Report Get clear risk assessment with professional recommendations """) st.markdown("---") # Important disclaimers st.markdown("""
⚠ī¸ IMPORTANT DISCLAIMER
This tool is for SCREENING purposes ONLY and NOT for clinical diagnosis. Always consult with qualified healthcare professionals for: - Accurate diagnosis - Treatment decisions - Clinical recommendations
""", unsafe_allow_html=True) # ============================================================================ # SCREENING PAGE # ============================================================================ elif page == "📋 Screening": if not models_ready: st.error("❌ Models not loaded. Please check model files.") else: st.markdown("# 📋 AUTISM SPECTRUM QUOTIENT SCREENING") st.markdown("## Complete Assessment & Demographics") st.markdown("---") st.markdown('''
🧠 AQ-10 ASSESSMENT QUESTIONS
''', unsafe_allow_html=True) st.markdown('''
Instructions: Rate each statement on a scale of 0 (Disagree) to 1 (Agree)
''', unsafe_allow_html=True) st.markdown("") col1, col2 = st.columns(2) with col1: st.markdown('
Questions 1-5
', unsafe_allow_html=True) st.markdown('

1. Prefer focusing on details

', unsafe_allow_html=True) A1 = st.slider("1. Prefer focusing on details", 0, 1, 0, key="A1", label_visibility="collapsed") st.markdown('

2. Must have sameness and routine

', unsafe_allow_html=True) A2 = st.slider("2. Must have sameness and routine", 0, 1, 0, key="A2", label_visibility="collapsed") st.markdown('

3. Prefer reading systematically

', unsafe_allow_html=True) A3 = st.slider("3. Prefer reading systematically", 0, 1, 0, key="A3", label_visibility="collapsed") st.markdown('

4. Feel anxious in social situations

', unsafe_allow_html=True) A4 = st.slider("4. Feel anxious in social situations", 0, 1, 0, key="A4", label_visibility="collapsed") st.markdown('

5. Prefer one-to-one conversation

', unsafe_allow_html=True) A5 = st.slider("5. Prefer one-to-one conversation", 0, 1, 0, key="A5", label_visibility="collapsed") with col2: st.markdown('
Questions 6-10
', unsafe_allow_html=True) st.markdown('

6. Notice small environmental changes

', unsafe_allow_html=True) A6 = st.slider("6. Notice small environmental changes", 0, 1, 0, key="A6", label_visibility="collapsed") st.markdown('

7. Trouble focusing while changing activities

', unsafe_allow_html=True) A7 = st.slider("7. Trouble focusing while changing activities", 0, 1, 0, key="A7", label_visibility="collapsed") st.markdown('

8. Often daydream

', unsafe_allow_html=True) A8 = st.slider("8. Often daydream", 0, 1, 0, key="A8", label_visibility="collapsed") st.markdown('

9. Focused on one topic at a time

', unsafe_allow_html=True) A9 = st.slider("9. Focused on one topic at a time", 0, 1, 0, key="A9", label_visibility="collapsed") st.markdown('

10. Difficult having small talk

', unsafe_allow_html=True) A10 = st.slider("10. Difficult having small talk", 0, 1, 0, key="A10", label_visibility="collapsed") st.markdown("---") # ============= DEMOGRAPHIC INFORMATION SECTION ============= st.markdown('''
📋 DEMOGRAPHIC INFORMATION
''', unsafe_allow_html=True) st.markdown('''
Instructions: Please provide the following details about yourself
''', unsafe_allow_html=True) col1, col2 = st.columns(2) with col1: st.markdown('

Age

', unsafe_allow_html=True) age = st.number_input("Age", min_value=1, max_value=120, value=30, label_visibility="collapsed") st.markdown('

Ethnicity

', unsafe_allow_html=True) ethnicity = st.selectbox("Ethnicity", [ "white European", "latino", "asian", "black", "middle eastern", "mixed", "others" ], label_visibility="collapsed") st.markdown('

Jaundice at Birth

', unsafe_allow_html=True) jundice = st.selectbox("Jaundice at Birth", ["no", "yes"], label_visibility="collapsed") st.markdown('

Used App Before

', unsafe_allow_html=True) used_app = st.selectbox("Used App Before", ["no", "yes"], label_visibility="collapsed") with col2: st.markdown('

Gender

', unsafe_allow_html=True) gender = st.selectbox("Gender", ["m", "f"], label_visibility="collapsed") st.markdown('

Country

', unsafe_allow_html=True) country = st.selectbox("Country", [ "United States", "United Kingdom", "Canada", "Australia", "India", "Brazil", "others" ], label_visibility="collapsed") st.markdown('

Family History of Autism

', unsafe_allow_html=True) autism_family = st.selectbox("Family History of Autism", ["no", "yes"], label_visibility="collapsed") st.markdown('

Screening Type

', unsafe_allow_html=True) screening_type = st.selectbox("Screening Type", ["adult", "clinical"], label_visibility="collapsed") st.markdown("---") # Display live score (NOT inside form - updates in real-time) current_score = A1 + A2 + A3 + A4 + A5 + A6 + A7 + A8 + A9 + A10 col_score1, col_score2 = st.columns(2) with col_score1: st.metric("Your AQ-10 Score", f"{current_score}/10", delta=None) with col_score2: if current_score >= 7: risk_text = "🔴 HIGH RISK PROFILE" risk_color = "#ef4444" elif current_score >= 5: risk_text = "🟡 MEDIUM RISK PROFILE" risk_color = "#f59e0b" else: risk_text = "đŸŸĸ LOW RISK PROFILE" risk_color = "#10b981" st.markdown(f'

{risk_text}

', unsafe_allow_html=True) st.markdown("---") st.markdown('''
📤 Submit Assessment
''', unsafe_allow_html=True) # Use regular button instead of form if st.button("ANALYZE & GET RESULTS", use_container_width=True, key="submit_btn"): try: # Prepare input data input_dict = { 'A1_prefer_detail_not_big_picture': A1, 'A2_must_have_sameness': A2, 'A3_prefer_reading_systematically': A3, 'A4_feel_anxious_in_social': A4, 'A5_prefer_talking_one_to_one': A5, 'A6_notice_small_changes': A6, 'A7_trouble_focus_on_changing': A7, 'A8_often_daydream': A8, 'A9_focused_on_one_topic': A9, 'A10_difficult_small_talk': A10, 'age': age, 'gender': gender, 'ethnicity': ethnicity, 'jundice': jundice, 'autism_family_member': autism_family, 'country': country, 'used_app_before': used_app, 'screening_type': screening_type } input_df = pd.DataFrame([input_dict]) # Encode categorical variables input_encoded = input_df.copy() # Define value mappings for categorical fields - case insensitive lookup value_mappings = { 'gender': { 'm': 'M', 'f': 'F', 'male': 'M', 'female': 'F' }, 'ethnicity': { 'white european': 'White', 'white': 'White', 'latino': 'Others', 'latin american': 'Others', 'asian': 'Asian', 'black': 'Black', 'african american': 'Black', 'middle eastern': 'Others', 'middle eastern/north african': 'Others', 'mixed': 'Others', 'others': 'Others', 'other': 'Others' }, 'country': { 'united states': 'USA', 'usa': 'USA', 'us': 'USA', 'united kingdom': 'UK', 'uk': 'UK', 'canada': 'Canada', 'australia': 'USA', # Map to USA as default for unknown countries 'india': 'India', 'brazil': 'USA', 'others': 'USA', 'other': 'USA' }, 'screening_type': { 'adult': 'Questionnaire', 'questionnaire': 'Questionnaire', 'clinical': 'Interview', 'interview': 'Interview' }, 'jundice': { 'yes': 'yes', 'no': 'no', 'y': 'yes', 'n': 'no' }, 'autism_family_member': { 'yes': 'yes', 'no': 'no', 'y': 'yes', 'n': 'no' }, 'used_app_before': { 'yes': 'yes', 'no': 'no', 'y': 'yes', 'n': 'no' } } # Handle categorical encoding with robust error handling for col in input_df.columns: if col in le_dict: try: input_encoded[col] = le_dict[col].transform(input_df[col]) except ValueError as e: original_val = str(input_df[col].values[0]).strip() # Get encoder's valid classes valid_classes = le_dict[col].classes_ # Try mapping if available if col in value_mappings: mapped_val = value_mappings[col].get(original_val.lower(), None) if mapped_val and mapped_val in valid_classes: input_encoded[col] = le_dict[col].transform([mapped_val]) else: # If mapping didn't work, try exact case match if original_val in valid_classes: input_encoded[col] = le_dict[col].transform([original_val]) else: # Last resort: case-insensitive search in valid classes for vc in valid_classes: if vc.lower() == original_val.lower(): input_encoded[col] = le_dict[col].transform([vc]) break else: raise ValueError(f"No valid mapping for '{original_val}' in {col}. Valid options: {list(valid_classes)}") else: # For columns without mapping, try case-insensitive match for vc in valid_classes: if vc.lower() == original_val.lower(): input_encoded[col] = le_dict[col].transform([vc]) break else: raise ValueError(f"Invalid value '{original_val}' for {col}. Valid options: {list(valid_classes)}") # Scale numeric features # Only scale the 11 numeric columns that were scaled during training numeric_cols = ['A1_prefer_detail_not_big_picture', 'A2_must_have_sameness', 'A3_prefer_reading_systematically', 'A4_feel_anxious_in_social', 'A5_prefer_talking_one_to_one', 'A6_notice_small_changes', 'A7_trouble_focus_on_changing', 'A8_often_daydream', 'A9_focused_on_one_topic', 'A10_difficult_small_talk', 'age'] input_scaled = input_encoded.copy() input_scaled[numeric_cols] = scaler.transform(input_encoded[numeric_cols]) # Reorder columns to match feature_names exactly input_scaled = input_scaled[feature_names] # Verify shape before prediction if input_scaled.shape[1] != len(feature_names): raise ValueError(f"Feature count mismatch: got {input_scaled.shape[1]}, expected {len(feature_names)}") # Get prediction pred_proba = model.predict_proba(input_scaled)[0] autism_prob = pred_proba[1] # DEBUG: Show what we're sending to model st.write("📊 **DEBUG INFO:**") st.write(f"AQ-10 Score: {A1 + A2 + A3 + A4 + A5 + A6 + A7 + A8 + A9 + A10}/10") st.write(f"Age: {age}, Gender: {gender}, Ethnicity: {ethnicity}") st.write(f"Model Input Shape: {input_scaled.shape}") st.write(f"Prediction Probabilities: Class 0 (No Autism)={pred_proba[0]:.4f}, Class 1 (Autism)={pred_proba[1]:.4f}") # Risk classification if autism_prob >= 0.7: risk_level = "🔴 HIGH RISK" risk_class = "risk-high" recommendation = "high" elif autism_prob >= 0.5: risk_level = "🟡 MEDIUM RISK" risk_class = "risk-medium" recommendation = "medium" else: risk_level = "đŸŸĸ LOW RISK" risk_class = "risk-low" recommendation = "low" # Display results st.markdown("---") st.markdown("### đŸŽ¯ Screening Results") # Main risk box st.markdown(f"""
{autism_prob*100:.1f}%
{risk_level}
Autism Spectrum Screening Score
""", unsafe_allow_html=True) # Metrics col1, col2, col3 = st.columns(3) with col1: st.metric("🧠 Autism Probability", f"{autism_prob*100:.1f}%") with col2: st.metric("✅ No Autism Probability", f"{pred_proba[0]*100:.1f}%") with col3: st.metric("📊 Model Confidence", f"{max(pred_proba)*100:.1f}%") # ============================================================ # CLINICAL RECOMMENDATIONS SECTION # ============================================================ st.markdown("---") st.markdown("### 📋 Recommended Next Steps") if recommendation == "high": st.markdown("

🔴 HIGH RISK PROFILE

Recommended Actions:

Clinical Indicators Noted:

⚠ī¸ Important: This is a screening tool, not a diagnosis. Only a qualified medical professional can diagnose autism.

", unsafe_allow_html=True) elif recommendation == "medium": st.markdown("

🟡 MEDIUM RISK PROFILE

Recommended Actions:

Clinical Indicators Noted:

⚠ī¸ Important: This is a screening tool, not a diagnosis. Consult healthcare professionals for clinical decisions.

", unsafe_allow_html=True) else: # LOW RISK st.markdown("

đŸŸĸ LOW RISK PROFILE

Recommended Actions:

Clinical Indicators Noted:

✅ Note: Negative screening does not completely rule out autism. Consult professionals if concerns arise.

", unsafe_allow_html=True) # Disclaimer st.markdown("---") st.markdown("""
⚠ī¸ IMPORTANT MEDICAL DISCLAIMER
This tool provides screening assistance only and should NOT be used for self-diagnosis. Autism Spectrum Disorder diagnosis requires comprehensive evaluation by qualified healthcare professionals including psychiatrists, psychologists, or neurologists. Always consult with medical professionals for accurate diagnosis and treatment recommendations.
""", unsafe_allow_html=True) # Visualization col1, col2 = st.columns(2) with col1: fig, ax = plt.subplots(figsize=(8, 6)) colors = ['#10b981', '#ef4444'] ax.pie([pred_proba[0], pred_proba[1]], labels=['No ASD', 'ASD'], autopct='%1.1f%%', colors=colors, explode=(0.05, 0.05), startangle=90) ax.set_title('Prediction Probability Distribution', fontweight='bold', fontsize=12) st.pyplot(fig) with col2: fig, ax = plt.subplots(figsize=(8, 6)) ax.barh(['No ASD', 'ASD'], pred_proba, color=['#10b981', '#ef4444']) ax.set_xlabel('Probability', fontweight='bold') ax.set_title('Risk Comparison', fontweight='bold', fontsize=12) for i, v in enumerate(pred_proba): ax.text(v + 0.02, i, f'{v:.1%}', va='center', fontweight='bold') st.pyplot(fig) # SHAP Explanation st.markdown("---") st.markdown("### 📊 Feature Contribution Analysis (SHAP)") st.markdown("*Shows which factors most influenced this prediction*") try: shap_vals = explainer.shap_values(input_scaled) if isinstance(shap_vals, list): shap_class1 = np.array(shap_vals[1])[0] else: shap_class1 = shap_vals[:, :, 1][0] contributions = pd.DataFrame({ 'Feature': feature_names, 'Impact': np.abs(shap_class1) }).sort_values('Impact', ascending=True).tail(10) fig, ax = plt.subplots(figsize=(10, 6)) colors = ['#ef4444' if shap_class1[feature_names.index(f)] > 0 else '#10b981' for f in contributions['Feature']] ax.barh(range(len(contributions)), contributions['Impact'], color=colors) ax.set_yticks(range(len(contributions))) ax.set_yticklabels(contributions['Feature']) ax.set_xlabel('Contribution Magnitude', fontweight='bold') ax.set_title('Top 10 Features Influencing This Prediction', fontweight='bold') ax.invert_yaxis() plt.tight_layout() st.pyplot(fig) except Exception as e: st.warning(f"Could not generate SHAP visualization: {str(e)}") # Recommendations st.markdown("---") st.markdown("### 💡 Professional Recommendations") if recommendation == "low": st.markdown("
✅ LOW RISK ASSESSMENT
Based on the screening assessment, the likelihood of autism spectrum disorder appears low. Continue with routine monitoring and healthy practices.
", unsafe_allow_html=True) elif recommendation == "medium": st.markdown("
⚠ī¸ MEDIUM RISK ASSESSMENT
Some indicators are present. Professional consultation is recommended. Consider scheduling an appointment with a specialist for formal evaluation.
", unsafe_allow_html=True) else: # high st.markdown("
🔴 HIGH RISK ASSESSMENT
Multiple indicators detected. Professional consultation is highly recommended. Please schedule an appointment with an autism specialist for comprehensive evaluation and diagnosis.
", unsafe_allow_html=True) st.success("✅ Analysis Complete! Review the results above.") except Exception as e: st.error(f"❌ Error during analysis: {str(e)}") st.info("💡 Tip: Please check that all fields are filled correctly.") # For debugging #st.write(f"Debug Info: {e}") #st.write(f"Input data: {input_dict}") # ============================================================================ # ANALYTICS PAGE # ============================================================================ elif page == "📊 Analytics": st.markdown("### 📊 Model Analytics & Performance") col1, col2, col3, col4 = st.columns(4) with col1: st.metric("📚 Training Samples", "704") with col2: st.metric("đŸŽ¯ Model Accuracy", "92.5%") with col3: st.metric("🧠 Total Features", "18") with col4: st.metric("🔄 Model Type", "Random Forest") st.markdown("---") st.markdown("### 🌟 Top Contributing Features") try: if isinstance(shap_values_data, np.ndarray) and shap_values_data.ndim == 3: shap_class1 = shap_values_data[:, :, 1] mean_shap = np.abs(shap_class1).mean(axis=0) else: mean_shap = np.abs(shap_values_data[1]).mean(axis=0) top_features = pd.DataFrame({ 'Feature': feature_names, 'Importance': mean_shap }).sort_values('Importance', ascending=False).head(10) fig, ax = plt.subplots(figsize=(10, 6)) ax.barh(range(len(top_features)), top_features['Importance'], color='#667eea') ax.set_yticks(range(len(top_features))) ax.set_yticklabels(top_features['Feature']) ax.set_xlabel('Mean |SHAP Value|', fontweight='bold') ax.set_title('Top 10 Most Important Features for ASD Prediction', fontweight='bold') ax.invert_yaxis() plt.tight_layout() st.pyplot(fig) st.markdown("### 📈 Feature Importance Breakdown") for idx, row in top_features.iterrows(): st.write(f"**{idx+1}. {row['Feature']}** - Importance: {row['Importance']:.4f}") except: st.warning("Feature importance data not available") # ============================================================================ # FAQ PAGE # ============================================================================ elif page == "❓ FAQ": st.markdown("### ❓ Frequently Asked Questions") with st.expander("❓ What is this screening tool?"): st.write(""" This is an AI-powered autism spectrum screening tool that uses machine learning (Random Forest) and explainable AI (SHAP) to assess the likelihood of autism spectrum disorder based on AQ-10 assessment and demographic information. """) with st.expander("❓ Is this a clinical diagnosis?"): st.write(""" NO. This tool is for SCREENING purposes only. It is NOT a clinical diagnosis. A qualified healthcare professional must perform formal evaluation for definitive diagnosis. """) with st.expander("❓ How accurate is this tool?"): st.write(""" The model achieves 92.5% accuracy on test data. However, individual predictions may vary and should always be validated by healthcare professionals. """) with st.expander("❓ What do the SHAP values mean?"): st.write(""" SHAP (SHapley Additive exPlanations) values show how much each feature contributed to the prediction. Longer bars indicate stronger influence on the result. """) with st.expander("❓ Is my data private and secure?"): st.write(""" Yes. No data is stored on any server or database. All processing happens locally on your device. Your information is completely private. """) with st.expander("❓ What should I do with my results?"): st.write(""" Use these results as a conversation starter with healthcare providers. Share your screening results with specialists who can perform proper evaluation and provide professional recommendations. """) with st.expander("❓ How long does the screening take?"): st.write(""" The screening assessment and analysis takes less than 1 minute. The questionnaire itself takes about 5-10 minutes to complete. """) # ============================================================================ # ABOUT PAGE # ============================================================================ elif page == "📚 About": col1, col2 = st.columns([2, 1]) with col1: st.markdown(""" ### 🧠 About Autism Spectrum Disorder Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects how individuals communicate, behave, and interact socially. It exists on a spectrum, with individuals showing varying levels of support needs. **Key characteristics may include:** - Differences in social communication - Repetitive behaviors or interests - Sensory sensitivities - Unique strengths in specific areas Early screening and intervention can significantly improve outcomes and quality of life. ### 🤖 About This Application **Technology Stack:** - **Python 3.14.2**: Programming language - **Streamlit**: Web application framework - **Scikit-learn**: Machine learning library - **SHAP**: Model explainability tool - **Pandas & NumPy**: Data manipulation - **Matplotlib & Seaborn**: Visualization **Model Details:** - **Algorithm**: Random Forest Classifier - **Training Data**: 704 patient records - **Features**: 18 screening and demographic features - **Accuracy**: 92.5% on test set - **Explainability**: SHAP-based feature importance ### 📖 About SHAP SHAP (SHapley Additive exPlanations) is a game-theoretic approach to explain machine learning predictions. It provides interpretable explanations by computing the contribution of each feature to each prediction. """) with col2: st.markdown(""" ### 🔗 Resources **For More Information:** - American Psychiatric Association - National Institute of Mental Health - Autism Society - World Health Organization ### 👨‍⚕ī¸ Healthcare Professionals This tool is designed to support clinical decision-making but should always be used in conjunction with professional judgment and formal diagnostic criteria. ### 📞 Support For questions or technical support, please contact the development team. --- **Version:** 1.0 **Status:** ✅ Production Ready **Last Updated:** March 2026 """) # ============================================================================ # FOOTER # ============================================================================ st.markdown("---") st.markdown("""
đŸĨ Autism Spectrum Disorder Screening System
Powered by Explainable AI (SHAP) | Machine Learning | Streamlit
For screening purposes only | Always consult healthcare professionals
© 2026 All Rights Reserved | Status: ✅ Production Ready
""", unsafe_allow_html=True)