Upload src/demo/streamlit_app.py with huggingface_hub

src/demo/streamlit_app.py

@@ -1,1189 +1,1191 @@
-"""
-NEXUS Streamlit Demo Application
-
-Interactive demo for the NEXUS Maternal-Neonatal Care Platform.
-Built with Google HAI-DEF models for the MedGemma Impact Challenge.
-
-HAI-DEF Models Used:
-- MedSigLIP: Medical image analysis (anemia, jaundice detection)
-- HeAR: Health acoustic representations (cry analysis)
-- MedGemma: Clinical reasoning and synthesis
-"""
-
-import streamlit as st
-from pathlib import Path
-import sys
-import os
-import tempfile
-import json
-
-# Add parent directory to path for imports
-sys.path.insert(0, str(Path(__file__).parent.parent))
-
-# Page configuration
-st.set_page_config(
-    page_title="NEXUS - Maternal-Neonatal Care",
-    page_icon="👶",
-    layout="wide",
-    initial_sidebar_state="expanded",
-)
-
-# Custom CSS
-st.markdown("""
-<style>
-    .main-header {
-        font-size: 2.5rem;
-        font-weight: bold;
-        color: #1f77b4;
-        text-align: center;
-        margin-bottom: 1rem;
-    }
-    .sub-header {
-        font-size: 1.2rem;
-        color: #666;
-        text-align: center;
-        margin-bottom: 2rem;
-    }
-    .risk-high {
-        background-color: #ffcccc;
-        border: 2px solid #ff0000;
-        padding: 1rem;
-        border-radius: 10px;
-    }
-    .risk-medium {
-        background-color: #fff3cd;
-        border: 2px solid #ffc107;
-        padding: 1rem;
-        border-radius: 10px;
-    }
-    .risk-low {
-        background-color: #d4edda;
-        border: 2px solid #28a745;
-        padding: 1rem;
-        border-radius: 10px;
-    }
-    .metric-card {
-        background-color: #f8f9fa;
-        padding: 1rem;
-        border-radius: 10px;
-        text-align: center;
-    }
-    .model-badge {
-        display: inline-block;
-        padding: 2px 10px;
-        border-radius: 12px;
-        font-size: 0.78rem;
-        font-weight: 600;
-        color: white;
-        letter-spacing: 0.3px;
-    }
-    .stMetric > div {
-        background-color: #f8f9fa;
-        padding: 0.5rem;
-        border-radius: 8px;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-
-@st.cache_resource
-def load_anemia_detector():
-    """Load anemia detector model with error handling."""
-    try:
-        from nexus.anemia_detector import AnemiaDetector
-        detector = AnemiaDetector()
-        return detector, None
-    except Exception as e:
-        return None, str(e)
-
-
-@st.cache_resource
-def load_jaundice_detector():
-    """Load jaundice detector model with error handling."""
-    try:
-        from nexus.jaundice_detector import JaundiceDetector
-        detector = JaundiceDetector()
-        return detector, None
-    except Exception as e:
-        return None, str(e)
-
-
-@st.cache_resource
-def load_cry_analyzer():
-    """Load cry analyzer with error handling."""
-    try:
-        from nexus.cry_analyzer import CryAnalyzer
-        analyzer = CryAnalyzer()
-        return analyzer, None
-    except Exception as e:
-        return None, str(e)
-
-
-@st.cache_resource
-def load_clinical_synthesizer():
-    """Load clinical synthesizer (MedGemma) with error handling."""
-    try:
-        import os
-        from nexus.clinical_synthesizer import ClinicalSynthesizer
-        use_medgemma = os.environ.get("NEXUS_USE_MEDGEMMA", "true").lower() != "false"
-        synthesizer = ClinicalSynthesizer(use_medgemma=use_medgemma)
-        return synthesizer, None
-    except Exception as e:
-        return None, str(e)
-
-
-def get_hai_def_info():
-    """Get HAI-DEF models information with validated accuracy numbers."""
-    return {
-        "MedSigLIP": {
-            "name": "MedSigLIP (google/medsiglip-448)",
-            "use": "Image analysis for anemia and jaundice detection + bilirubin regression",
-            "method": "Zero-shot classification (max-similarity, 8 prompts/class) + trained SVM/LR classifiers on embeddings",
-            "accuracy": "Anemia: trained classifier on augmented data, Jaundice: trained classifier on 2,235 images, Bilirubin: MAE 2.67 mg/dL (r=0.77)",
-            "badge": "Vision",
-            "badge_color": "#388e3c",
-        },
-        "HeAR": {
-            "name": "HeAR (google/hear-pytorch)",
-            "use": "Infant cry analysis for asphyxia and cry type classification",
-            "method": "512-dim health acoustic embeddings + trained linear classifier on donate-a-cry dataset (5-class: hungry, belly_pain, burping, discomfort, tired)",
-            "accuracy": "Trained cry type classifier with asphyxia risk derivation from distress patterns",
-            "badge": "Audio",
-            "badge_color": "#f57c00",
-        },
-        "MedGemma": {
-            "name": "MedGemma 1.5 4B (google/medgemma-1.5-4b-it)",
-            "use": "Clinical reasoning and recommendation synthesis",
-            "method": "4-bit NF4 quantized inference with WHO IMNCI protocol-aligned synthesis and 6-agent reasoning traces",
-            "accuracy": "Protocol-aligned clinical recommendations with structured reasoning chains",
-            "badge": "Language",
-            "badge_color": "#1976d2",
-        },
-    }
-
-
-def main():
-    """Main application."""
-
-    # Header
-    st.markdown('<div class="main-header">NEXUS</div>', unsafe_allow_html=True)
-    st.markdown(
-        '<div class="sub-header">AI-Powered Maternal-Neonatal Care Platform</div>',
-        unsafe_allow_html=True
-    )
-
-    # Sidebar
-    with st.sidebar:
-        st.markdown("## 🏥 NEXUS")
-        st.markdown("---")
-
-        assessment_type = st.radio(
-            "Select Assessment Type",
-            [
-                "Maternal Anemia Screening",
-                "Neonatal Jaundice Detection",
-                "Cry Analysis",
-                "Combined Assessment",
-                "Agentic Workflow",
-                "HAI-DEF Models Info"
-            ],
-            index=0,
-        )
-
-        st.markdown("---")
-        st.markdown("### About NEXUS")
-        st.markdown("""
-        NEXUS uses AI to provide non-invasive screening for:
-        - **Maternal Anemia** via conjunctiva imaging
-        - **Neonatal Jaundice** via skin color analysis
-        - **Birth Asphyxia** via cry pattern analysis
-
-        Built with **Google HAI-DEF models** for the MedGemma Impact Challenge 2026.
-        """)
-
-        st.markdown("---")
-        st.markdown("### Edge AI Mode")
-        edge_mode = st.toggle("Enable Edge AI Mode", value=False, key="edge_mode")
-        if edge_mode:
-            st.success("Edge AI: INT8 quantized models + offline inference")
-        else:
-            st.info("Cloud mode: Full-precision HAI-DEF models")
-
-        st.markdown("---")
-        st.markdown("### HAI-DEF Models")
-        st.markdown("""
-        - **MedSigLIP**: Vision (trained classifiers)
-        - **HeAR**: Audio (trained cry classifier)
-        - **MedGemma 1.5**: Clinical AI (4-bit NF4)
-        """)
-
-    # Show Edge AI banner when enabled
-    if edge_mode:
-        render_edge_ai_banner()
-
-    # Main content based on selection
-    if assessment_type == "Maternal Anemia Screening":
-        render_anemia_screening()
-    elif assessment_type == "Neonatal Jaundice Detection":
-        render_jaundice_detection()
-    elif assessment_type == "Cry Analysis":
-        render_cry_analysis()
-    elif assessment_type == "Combined Assessment":
-        render_combined_assessment()
-    elif assessment_type == "Agentic Workflow":
-        render_agentic_workflow()
-    else:
-        render_hai_def_info()
-
-
-def render_edge_ai_banner():
-    """Show Edge AI mode status and model metrics."""
-    st.markdown("""
-    <div style="background: linear-gradient(135deg, #1a237e 0%, #0d47a1 100%);
-                color: white; padding: 1rem 1.5rem; border-radius: 10px; margin-bottom: 1rem;">
-        <h4 style="margin:0; color: white;">Edge AI Mode Active</h4>
-        <p style="margin: 0.3rem 0 0 0; opacity: 0.9; font-size: 0.9rem;">
-            Running INT8 quantized models for offline-capable inference on low-resource devices.
-        </p>
-    </div>
-    """, unsafe_allow_html=True)
-
-    col1, col2, col3, col4 = st.columns(4)
-    with col1:
-        st.metric("MedSigLIP INT8", "111.2 MB", "-86% memory")
-    with col2:
-        st.metric("Acoustic Model", "0.6 MB", "INT8 quantized")
-    with col3:
-        st.metric("Text Embeddings", "12 KB", "Pre-computed")
-    with col4:
-        st.metric("Total Edge Size", "~289 MB", "Offline-ready")
-
-    with st.expander("Edge AI Details"):
-        st.markdown("""
-        **Quantization**: Dynamic INT8 (PyTorch `quantize_dynamic`, qnnpack backend)
-
-        | Component | Cloud (FP32) | Edge (INT8) | Compression |
-        |-----------|-------------|-------------|-------------|
-        | MedSigLIP Vision | 812.6 MB | 111.2 MB | **7.31x** |
-        | Acoustic Model | 0.665 MB | 0.599 MB | 1.11x |
-        | CPU Latency | 97.7 ms | ~65 ms (ARM est.) | ~1.5x faster |
-
-        **Target Devices**: Android 8.0+, ARM Cortex-A53, 2GB RAM
-
-        **Offline Capabilities**:
-        - Image analysis via INT8 MedSigLIP + pre-computed binary text embeddings
-        - Audio analysis via INT8 acoustic feature extractor
-        - Clinical reasoning via rule-based WHO IMNCI protocols (no MedGemma required)
-        """)
-
-
-def _cleanup_temp(path: str) -> None:
-    """Safely remove a temporary file."""
-    try:
-        if path and os.path.exists(path):
-            os.unlink(path)
-    except OSError:
-        pass
-
-
-def _save_upload_to_temp(uploaded_file, suffix: str) -> str:
-    """Save an uploaded file to a temporary path and return the path."""
-    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=suffix)
-    try:
-        tmp.write(uploaded_file.getvalue())
-        tmp.close()
-        return tmp.name
-    except Exception:
-        tmp.close()
-        _cleanup_temp(tmp.name)
-        raise
-
-
-def _model_badge(name: str, color: str) -> str:
-    """Return an HTML badge for displaying which HAI-DEF model is active."""
-    return (
-        f'<span style="background:{color}; color:white; padding:2px 10px; '
-        f'border-radius:12px; font-size:0.78rem; font-weight:600; '
-        f'letter-spacing:0.3px;">{name}</span>'
-    )
-
-
-def render_anemia_screening():
-    """Render anemia screening interface."""
-    st.header("Maternal Anemia Screening")
-    st.markdown(
-        f"Upload a clear image of the inner eyelid (conjunctiva) for anemia screening. "
-        f'{_model_badge("MedSigLIP", "#388e3c")}',
-        unsafe_allow_html=True,
-    )
-
-    col1, col2 = st.columns([1, 1])
-
-    with col1:
-        st.subheader("Upload Image")
-        uploaded_file = st.file_uploader(
-            "Choose a conjunctiva image",
-            type=["jpg", "jpeg", "png"],
-            key="anemia_upload"
-        )
-
-        if uploaded_file:
-            st.image(uploaded_file, caption="Uploaded Image", use_container_width=True)
-
-    with col2:
-        st.subheader("Analysis Results")
-
-        if uploaded_file:
-            with st.spinner("Analyzing image..."):
-                tmp_path = None
-                try:
-                    detector, load_err = load_anemia_detector()
-                    if detector is None:
-                        st.error(f"Could not load model: {load_err}")
-                        return
-
-                    tmp_path = _save_upload_to_temp(uploaded_file, ".jpg")
-
-                    result = detector.detect(tmp_path)
-                    color_info = detector.analyze_color_features(tmp_path)
-
-                    # Display results
-                    risk_class = f"risk-{result['risk_level']}"
-                    st.markdown(f'<div class="{risk_class}">', unsafe_allow_html=True)
-
-                    if result["is_anemic"]:
-                        st.error("⚠️ ANEMIA DETECTED")
-                    else:
-                        st.success("✅ No Anemia Detected")
-
-                    st.markdown("</div>", unsafe_allow_html=True)
-
-                    # Metrics
-                    col_a, col_b, col_c = st.columns(3)
-                    with col_a:
-                        st.metric("Confidence", f"{result['confidence']:.1%}")
-                    with col_b:
-                        st.metric("Risk Level", result['risk_level'].upper())
-                    with col_c:
-                        st.metric("Est. Hemoglobin", f"{color_info['estimated_hemoglobin']} g/dL")
-
-                    # Recommendation
-                    st.markdown("### Recommendation")
-                    st.info(result["recommendation"])
-
-                    # Color analysis
-                    with st.expander("Technical Details"):
-                        st.json({
-                            "anemia_score": round(result["anemia_score"], 3),
-                            "healthy_score": round(result["healthy_score"], 3),
-                            "red_ratio": round(color_info["red_ratio"], 3),
-                            "pallor_index": round(color_info["pallor_index"], 3),
-                        })
-
-                except Exception as e:
-                    st.error(f"Error analyzing image: {e}")
-                finally:
-                    _cleanup_temp(tmp_path)
-        else:
-            st.info("👆 Upload an image to begin analysis")
-
-
-def render_jaundice_detection():
-    """Render jaundice detection interface."""
-    st.header("Neonatal Jaundice Detection")
-    st.markdown(
-        f"Upload an image of the newborn's skin or sclera for jaundice assessment. "
-        f'{_model_badge("MedSigLIP", "#388e3c")}',
-        unsafe_allow_html=True,
-    )
-
-    col1, col2 = st.columns([1, 1])
-
-    with col1:
-        st.subheader("Upload Image")
-        uploaded_file = st.file_uploader(
-            "Choose a neonatal image",
-            type=["jpg", "jpeg", "png"],
-            key="jaundice_upload"
-        )
-
-        if uploaded_file:
-            st.image(uploaded_file, caption="Uploaded Image", use_container_width=True)
-
-        # Patient info
-        st.subheader("Patient Information (Optional)")
-        age_days = st.number_input("Age (days)", min_value=0, max_value=28, value=3)
-        birth_weight = st.number_input("Birth weight (grams)", min_value=500, max_value=5000, value=3000)
-
-    with col2:
-        st.subheader("Analysis Results")
-
-        if uploaded_file:
-            with st.spinner("Analyzing image..."):
-                tmp_path = None
-                try:
-                    detector, load_err = load_jaundice_detector()
-                    if detector is None:
-                        st.error(f"Could not load model: {load_err}")
-                        return
-
-                    tmp_path = _save_upload_to_temp(uploaded_file, ".jpg")
-
-                    result = detector.detect(tmp_path)
-                    zone_info = detector.analyze_kramer_zones(tmp_path)
-
-                    # Display results
-                    risk_class = "risk-high" if result["needs_phototherapy"] else (
-                        "risk-medium" if result["severity"] in ["moderate", "mild"] else "risk-low"
-                    )
-                    st.markdown(f'<div class="{risk_class}">', unsafe_allow_html=True)
-
-                    if result["has_jaundice"]:
-                        st.warning(f"⚠️ JAUNDICE DETECTED - {result['severity'].upper()}")
-                    else:
-                        st.success("✅ No Significant Jaundice")
-
-                    st.markdown("</div>", unsafe_allow_html=True)
-
-                    # Metrics - show ML bilirubin if available
-                    col_a, col_b, col_c = st.columns(3)
-                    with col_a:
-                        bili_value = result.get('estimated_bilirubin_ml', result.get('estimated_bilirubin', 0))
-                        bili_method = result.get('bilirubin_method', 'Color Analysis')
-                        st.metric("Est. Bilirubin", f"{bili_value} mg/dL")
-                        st.caption(f"Method: {bili_method}")
-                    with col_b:
-                        st.metric("Severity", result['severity'].upper())
-                    with col_c:
-                        st.metric("Kramer Zone", zone_info['kramer_zone'])
-
-                    # Phototherapy indicator
-                    if result["needs_phototherapy"]:
-                        st.error("🔆 PHOTOTHERAPY RECOMMENDED")
-
-                    # Recommendation
-                    st.markdown("### Recommendation")
-                    st.info(result["recommendation"])
-
-                    # Zone analysis
-                    with st.expander("Kramer Zone Analysis"):
-                        st.write(f"**Zone**: {zone_info['kramer_zone']} - {zone_info['zone_description']}")
-                        st.write(f"**Yellow Index**: {zone_info['yellow_index']}")
-                        st.progress(min(zone_info['yellow_index'] * 2, 1.0))
-
-                    # Technical details
-                    with st.expander("Technical Details"):
-                        details = {
-                            "jaundice_score": round(result["jaundice_score"], 3),
-                            "confidence": round(result["confidence"], 3),
-                            "model": result.get("model", "unknown"),
-                            "model_type": result.get("model_type", "unknown"),
-                            "bilirubin_method": result.get("bilirubin_method", "Color Analysis"),
-                        }
-                        if result.get("estimated_bilirubin_ml") is not None:
-                            details["bilirubin_ml"] = result["estimated_bilirubin_ml"]
-                            details["bilirubin_color"] = result["estimated_bilirubin"]
-                        st.json(details)
-
-                except Exception as e:
-                    st.error(f"Error analyzing image: {e}")
-                finally:
-                    _cleanup_temp(tmp_path)
-        else:
-            st.info("👆 Upload an image to begin analysis")
-
-
-def render_cry_analysis():
-    """Render cry analysis interface."""
-    st.header("Infant Cry Analysis")
-    st.markdown(
-        f"Upload an audio recording of the infant's cry for analysis. "
-        f'{_model_badge("HeAR", "#f57c00")}',
-        unsafe_allow_html=True,
-    )
-
-    col1, col2 = st.columns([1, 1])
-
-    with col1:
-        st.subheader("Upload Audio")
-        uploaded_file = st.file_uploader(
-            "Choose a cry audio file",
-            type=["wav", "mp3", "ogg"],
-            key="cry_upload"
-        )
-
-        if uploaded_file:
-            st.audio(uploaded_file)
-
-    with col2:
-        st.subheader("Analysis Results")
-
-        if uploaded_file:
-            with st.spinner("Analyzing cry..."):
-                tmp_path = None
-                try:
-                    analyzer, load_err = load_cry_analyzer()
-                    if analyzer is None:
-                        st.error(f"Could not load model: {load_err}")
-                        return
-
-                    tmp_path = _save_upload_to_temp(uploaded_file, ".wav")
-
-                    result = analyzer.analyze(tmp_path)
-
-                    # Display results
-                    risk_class = f"risk-{result['risk_level']}"
-                    st.markdown(f'<div class="{risk_class}">', unsafe_allow_html=True)
-
-                    if result["is_abnormal"]:
-                        st.error("⚠️ ABNORMAL CRY PATTERN DETECTED")
-                    else:
-                        st.success("✅ Normal Cry Pattern")
-
-                    st.markdown("</div>", unsafe_allow_html=True)
-
-                    # Metrics
-                    col_a, col_b, col_c = st.columns(3)
-                    with col_a:
-                        st.metric("Asphyxia Risk", f"{result['asphyxia_risk']:.1%}")
-                    with col_b:
-                        st.metric("Cry Type", result['cry_type'].title())
-                    with col_c:
-                        st.metric("F0 (Pitch)", f"{result['features']['f0_mean']:.0f} Hz")
-
-                    # Recommendation
-                    st.markdown("### Recommendation")
-                    st.info(result["recommendation"])
-
-                    # Acoustic features
-                    with st.expander("Acoustic Features"):
-                        st.json(result["features"])
-
-                except Exception as e:
-                    st.error(f"Error analyzing audio: {e}")
-                finally:
-                    _cleanup_temp(tmp_path)
-        else:
-            st.info("👆 Upload an audio file to begin analysis")
-
-
-def render_combined_assessment():
-    """Render combined assessment interface using Clinical Synthesizer."""
-    st.header("Combined Clinical Assessment")
-    st.markdown(
-        f"Upload multiple inputs for a comprehensive assessment using **MedGemma Clinical Synthesizer**. "
-        f"This combines findings from all HAI-DEF models to provide integrated clinical recommendations. "
-        f'{_model_badge("MedSigLIP", "#388e3c")} '
-        f'{_model_badge("HeAR", "#f57c00")} '
-        f'{_model_badge("MedGemma", "#1976d2")}',
-        unsafe_allow_html=True,
-    )
-
-    # Reset findings each time this tab is rendered to prevent
-    # stale data from previous patients contaminating results
-    st.session_state.findings = {
-        "anemia": None,
-        "jaundice": None,
-        "cry": None
-    }
-
-    col1, col2, col3 = st.columns(3)
-
-    with col1:
-        st.subheader("🩸 Anemia Screening")
-        anemia_file = st.file_uploader(
-            "Conjunctiva image",
-            type=["jpg", "jpeg", "png"],
-            key="combined_anemia"
-        )
-        if anemia_file:
-            st.image(anemia_file, use_container_width=True)
-            with st.spinner("Analyzing..."):
-                try:
-                    detector, load_err = load_anemia_detector()
-                    if detector is None:
-                        st.error(f"Model error: {load_err}")
-                    else:
-                        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
-                            tmp.write(anemia_file.getvalue())
-                            result = detector.detect(tmp.name)
-                        st.session_state.findings["anemia"] = result
-                        if result["is_anemic"]:
-                            st.error(f"Anemia: {result['risk_level'].upper()}")
-                        else:
-                            st.success("No Anemia")
-                except Exception as e:
-                    st.error(f"Error: {e}")
-
-    with col2:
-        st.subheader("👶 Jaundice Detection")
-        jaundice_file = st.file_uploader(
-            "Neonatal skin image",
-            type=["jpg", "jpeg", "png"],
-            key="combined_jaundice"
-        )
-        if jaundice_file:
-            st.image(jaundice_file, use_container_width=True)
-            with st.spinner("Analyzing..."):
-                try:
-                    detector, load_err = load_jaundice_detector()
-                    if detector is None:
-                        st.error(f"Model error: {load_err}")
-                    else:
-                        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
-                            tmp.write(jaundice_file.getvalue())
-                            result = detector.detect(tmp.name)
-                            st.session_state.findings["jaundice"] = result
-                            if result["has_jaundice"]:
-                                st.warning(f"Jaundice: {result['severity'].upper()}")
-                            else:
-                                st.success("No Jaundice")
-                except Exception as e:
-                    st.error(f"Error: {e}")
-
-    with col3:
-        st.subheader("🔊 Cry Analysis")
-        cry_file = st.file_uploader(
-            "Cry audio",
-            type=["wav", "mp3", "ogg"],
-            key="combined_cry"
-        )
-        if cry_file:
-            st.audio(cry_file)
-            with st.spinner("Analyzing..."):
-                try:
-                    analyzer, load_err = load_cry_analyzer()
-                    if analyzer is None:
-                        st.error(f"Model error: {load_err}")
-                        raise RuntimeError(load_err)
-                    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
-                        tmp.write(cry_file.getvalue())
-                        result = analyzer.analyze(tmp.name)
-                        st.session_state.findings["cry"] = result
-                        if result["is_abnormal"]:
-                            st.error(f"Abnormal Cry: {result['risk_level'].upper()}")
-                        else:
-                            st.success("Normal Cry")
-                except Exception as e:
-                    st.error(f"Error: {e}")
-
-    # Clinical Synthesis Section
-    st.markdown("---")
-    st.subheader("🏥 Clinical Synthesis (MedGemma)")
-
-    # Check if any findings are available
-    has_findings = any(v is not None for v in st.session_state.findings.values())
-
-    if has_findings:
-        if st.button("Generate Clinical Synthesis", type="primary"):
-            with st.spinner("Synthesizing findings with MedGemma..."):
-                try:
-                    synthesizer, load_err = load_clinical_synthesizer()
-                    if synthesizer is None:
-                        st.error(f"Could not load synthesizer: {load_err}")
-                        return
-
-                    # Prepare findings dict
-                    findings = {}
-                    if st.session_state.findings["anemia"]:
-                        findings["anemia"] = st.session_state.findings["anemia"]
-                    if st.session_state.findings["jaundice"]:
-                        findings["jaundice"] = st.session_state.findings["jaundice"]
-                    if st.session_state.findings["cry"]:
-                        findings["cry"] = st.session_state.findings["cry"]
-
-                    synthesis = synthesizer.synthesize(findings)
-
-                    # Display synthesis results
-                    severity_level = synthesis.get("severity_level", "GREEN")
-                    severity_colors = {
-                        "GREEN": ("🟢", "#d4edda", "#155724"),
-                        "YELLOW": ("🟡", "#fff3cd", "#856404"),
-                        "RED": ("🔴", "#f8d7da", "#721c24")
-                    }
-                    emoji, bg_color, text_color = severity_colors.get(severity_level, ("⚪", "#f8f9fa", "#000"))
-
-                    st.markdown(f"""
-                    <div style="background-color: {bg_color}; padding: 1.5rem; border-radius: 10px; margin: 1rem 0;">
-                        <h3 style="color: {text_color}; margin: 0;">{emoji} Severity: {severity_level}</h3>
-                        <p style="color: {text_color}; font-size: 1.1rem; margin-top: 0.5rem;">{synthesis.get('severity_description', '')}</p>
-                    </div>
-                    """, unsafe_allow_html=True)
-
-                    # Summary
-                    st.markdown("### Summary")
-                    st.info(synthesis.get("summary", "No summary available"))
-
-                    # Actions
-                    if synthesis.get("immediate_actions"):
-                        st.markdown("### Immediate Actions")
-                        for action in synthesis["immediate_actions"]:
-                            st.markdown(f"- {action}")
-
-                    # Referral
-                    col_a, col_b = st.columns(2)
-                    with col_a:
-                        st.markdown("### Referral Status")
-                        if synthesis.get("referral_needed"):
-                            st.error(f"⚠️ REFERRAL NEEDED: {synthesis.get('referral_urgency', 'standard').upper()}")
-                        else:
-                            st.success("✅ No referral needed")
-
-                    with col_b:
-                        st.markdown("### Follow-up")
-                        st.info(synthesis.get("follow_up", "Schedule routine follow-up"))
-
-                    # Technical details
-                    with st.expander("Technical Details"):
-                        model_name = synthesis.get("model", "unknown")
-                        st.json({
-                            "model": model_name,
-                            "model_id": synthesis.get("model_id", ""),
-                            "generated_at": synthesis.get("generated_at"),
-                            "urgent_conditions": synthesis.get("urgent_conditions", []),
-                        })
-                        if model_name and "Fallback" not in str(model_name):
-                            st.success(f"Synthesis powered by {model_name}")
-                        elif "Fallback" in str(model_name):
-                            st.warning("Using rule-based fallback (MedGemma unavailable)")
-
-                except Exception as e:
-                    st.error(f"Error generating synthesis: {e}")
-    else:
-        st.info("👆 Upload at least one input (image or audio) to generate clinical synthesis")
-
-
-def render_hai_def_info():
-    """Render HAI-DEF models information."""
-    st.header("Google HAI-DEF Models")
-    st.markdown("""
-    NEXUS is built using **Google Health AI Developer Foundations (HAI-DEF)** models,
-    designed specifically for healthcare applications in resource-limited settings.
-    """)
-
-    hai_def = get_hai_def_info()
-
-    # MedSigLIP
-    st.markdown("---")
-    col1, col2 = st.columns([1, 2])
-    with col1:
-        st.markdown("### 🖼️ MedSigLIP")
-        st.info("google/medsiglip-448\n\nHAI-DEF Vision Model")
-    with col2:
-        info = hai_def["MedSigLIP"]
-        st.markdown(f"**Model**: {info['name']}")
-        st.markdown(f"**Use Case**: {info['use']}")
-        st.markdown(f"**Method**: {info['method']}")
-        st.markdown(f"**Validated Performance**: {info['accuracy']}")
-        st.markdown("""
-        MedSigLIP enables zero-shot medical image classification using
-        text prompts. NEXUS extends this with trained SVM/LR classifiers
-        on MedSigLIP embeddings (with data augmentation) for improved
-        accuracy, plus a novel 3-layer MLP regression head for continuous
-        bilirubin prediction from frozen embeddings.
-        """)
-
-    # HeAR
-    st.markdown("---")
-    col1, col2 = st.columns([1, 2])
-    with col1:
-        st.markdown("### 🔊 HeAR")
-        st.info("google/hear-pytorch\n\nHAI-DEF Audio Model")
-    with col2:
-        info = hai_def["HeAR"]
-        st.markdown(f"**Model**: {info['name']}")
-        st.markdown(f"**Use Case**: {info['use']}")
-        st.markdown(f"**Method**: {info['method']}")
-        st.markdown(f"**Validated Performance**: {info['accuracy']}")
-        st.markdown("""
-        HeAR (Health Acoustic Representations) produces 512-dim embeddings
-        from 2-second audio clips at 16kHz. NEXUS trains a linear classifier
-        on HeAR embeddings for 5-class cry type classification (hungry,
-        belly_pain, burping, discomfort, tired) and derives asphyxia risk
-        from distress patterns.
-        """)
-
-    # MedGemma
-    st.markdown("---")
-    col1, col2 = st.columns([1, 2])
-    with col1:
-        st.markdown("### 🧠 MedGemma")
-        st.info("google/medgemma-1.5-4b-it\n\nHAI-DEF Language Model")
-    with col2:
-        info = hai_def["MedGemma"]
-        st.markdown(f"**Model**: {info['name']}")
-        st.markdown(f"**Use Case**: {info['use']}")
-        st.markdown(f"**Method**: {info['method']}")
-        st.markdown(f"**Validated Performance**: {info['accuracy']}")
-        st.markdown("""
-        MedGemma 1.5 provides clinical reasoning capabilities via 4-bit NF4
-        quantized inference (~2 GB VRAM). It synthesizes multi-modal findings
-        into actionable recommendations following WHO IMNCI protocols,
-        producing structured reasoning chains within the 6-agent pipeline.
-        """)
-
-    # Competition Info
-    st.markdown("---")
-    st.subheader("🏆 MedGemma Impact Challenge 2026")
-    st.markdown("""
-    NEXUS is being developed for the [MedGemma Impact Challenge](https://www.kaggle.com/competitions/medgemma-impact-challenge-2026)
-    on Kaggle.
-
-    **Competition Focus**: Solutions for resource-limited healthcare settings using HAI-DEF models.
-
-    **NEXUS Impact**:
-    - 📍 Target: Sub-Saharan Africa and South Asia
-    - 👩‍⚕️ Users: Community Health Workers
-    - 🎯 Goals: Reduce maternal/neonatal mortality
-    - 📱 Deployment: Offline-capable mobile app
-    """)
-
-
-def render_agentic_workflow():
-    """Render the agentic workflow interface with reasoning traces."""
-    st.header("Agentic Clinical Workflow")
-    st.markdown(
-        f"**6-Agent Pipeline** with step-by-step reasoning traces. "
-        f"Each agent explains its clinical decision process, providing a full audit trail. "
-        f'{_model_badge("MedSigLIP", "#388e3c")} '
-        f'{_model_badge("HeAR", "#f57c00")} '
-        f'{_model_badge("MedGemma", "#1976d2")}',
-        unsafe_allow_html=True,
-    )
-
-    # Pipeline diagram
-    st.markdown("""
-    <div style="display: flex; align-items: center; justify-content: center; gap: 0.5rem; flex-wrap: wrap; margin: 1rem 0;">
-        <div style="background: #e3f2fd; padding: 0.5rem 1rem; border-radius: 8px; font-weight: bold; border: 2px solid #1976d2;">Triage</div>
-        <span style="font-size: 1.5rem;">&#8594;</span>
-        <div style="background: #e8f5e9; padding: 0.5rem 1rem; border-radius: 8px; font-weight: bold; border: 2px solid #388e3c;">Image (MedSigLIP)</div>
-        <span style="font-size: 1.5rem;">&#8594;</span>
-        <div style="background: #fff3e0; padding: 0.5rem 1rem; border-radius: 8px; font-weight: bold; border: 2px solid #f57c00;">Audio (HeAR)</div>
-        <span style="font-size: 1.5rem;">&#8594;</span>
-        <div style="background: #f3e5f5; padding: 0.5rem 1rem; border-radius: 8px; font-weight: bold; border: 2px solid #7b1fa2;">Protocol (WHO)</div>
-        <span style="font-size: 1.5rem;">&#8594;</span>
-        <div style="background: #fce4ec; padding: 0.5rem 1rem; border-radius: 8px; font-weight: bold; border: 2px solid #c62828;">Referral</div>
-        <span style="font-size: 1.5rem;">&#8594;</span>
-        <div style="background: #e0f7fa; padding: 0.5rem 1rem; border-radius: 8px; font-weight: bold; border: 2px solid #00838f;">Synthesis (MedGemma)</div>
-    </div>
-    """, unsafe_allow_html=True)
-
-    st.markdown("---")
-
-    # Input section
-    col_left, col_right = st.columns([1, 1])
-
-    with col_left:
-        st.subheader("Patient & Inputs")
-        patient_type = st.selectbox("Patient Type", ["newborn", "pregnant"], key="agentic_patient")
-
-        # Danger signs
-        st.markdown("**Danger Signs**")
-        danger_signs = []
-        if patient_type == "pregnant":
-            sign_options = [
-                ("Severe headache", "high"),
-                ("Blurred vision", "high"),
-                ("Convulsions", "critical"),
-                ("Severe abdominal pain", "high"),
-                ("Vaginal bleeding", "critical"),
-                ("High fever", "high"),
-                ("Severe pallor", "medium"),
-            ]
-        else:
-            sign_options = [
-                ("Not breathing at birth", "critical"),
-                ("Convulsions", "critical"),
-                ("Severe chest indrawing", "high"),
-                ("Not feeding", "high"),
-                ("High fever (>38C)", "high"),
-                ("Hypothermia (<35.5C)", "high"),
-                ("Lethargy / unconscious", "critical"),
-                ("Umbilical redness", "medium"),
-            ]
-
-        selected_signs = st.multiselect(
-            "Select present danger signs",
-            [s[0] for s in sign_options],
-            key="agentic_signs"
-        )
-        for label, severity in sign_options:
-            if label in selected_signs:
-                danger_signs.append({
-                    "id": label.lower().replace(" ", "_"),
-                    "label": label,
-                    "severity": severity,
-                    "present": True,
-                })
-
-        # Image uploads
-        st.markdown("**Clinical Images**")
-        conjunctiva_file = st.file_uploader(
-            "Conjunctiva image (anemia)", type=["jpg", "jpeg", "png"],
-            key="agentic_conjunctiva"
-        )
-        skin_file = st.file_uploader(
-            "Skin image (jaundice)", type=["jpg", "jpeg", "png"],
-            key="agentic_skin"
-        )
-        cry_file = st.file_uploader(
-            "Cry audio", type=["wav", "mp3", "ogg"],
-            key="agentic_cry"
-        )
-
-    with col_right:
-        st.subheader("Workflow Execution")
-
-        if st.button("Run Agentic Assessment", type="primary", key="run_agentic"):
-            with st.spinner("Running 6-agent workflow..."):
-                try:
-                    from nexus.agentic_workflow import (
-                        AgenticWorkflowEngine,
-                        AgentPatientInfo,
-                        DangerSign,
-                        WorkflowInput,
-                    )
-
-                    # Save uploaded files (track for cleanup)
-                    _temp_paths = []
-                    conjunctiva_path = None
-                    skin_path = None
-                    cry_path = None
-
-                    if conjunctiva_file:
-                        conjunctiva_path = _save_upload_to_temp(conjunctiva_file, ".jpg")
-                        _temp_paths.append(conjunctiva_path)
-
-                    if skin_file:
-                        skin_path = _save_upload_to_temp(skin_file, ".jpg")
-                        _temp_paths.append(skin_path)
-
-                    if cry_file:
-                        cry_path = _save_upload_to_temp(cry_file, ".wav")
-                        _temp_paths.append(cry_path)
-
-                    # Build workflow input
-                    signs = [
-                        DangerSign(
-                            id=s["id"], label=s["label"],
-                            severity=s["severity"], present=True,
-                        )
-                        for s in danger_signs
-                    ]
-
-                    info = AgentPatientInfo(patient_type=patient_type)
-                    workflow_input = WorkflowInput(
-                        patient_type=patient_type,
-                        patient_info=info,
-                        danger_signs=signs,
-                        conjunctiva_image=conjunctiva_path,
-                        skin_image=skin_path,
-                        cry_audio=cry_path,
-                    )
-
-                    # Run workflow — reuse cached model instances when available
-                    anemia_det, _ = load_anemia_detector()
-                    jaundice_det, _ = load_jaundice_detector()
-                    cry_ana, _ = load_cry_analyzer()
-                    synth, _ = load_clinical_synthesizer()
-
-                    engine = AgenticWorkflowEngine(
-                        anemia_detector=anemia_det,
-                        jaundice_detector=jaundice_det,
-                        cry_analyzer=cry_ana,
-                        synthesizer=synth,
-                    )
-                    result = engine.execute(workflow_input)
-
-                    st.session_state["agentic_result"] = result
-                    st.success("Workflow complete!")
-
-                except Exception as e:
-                    st.error(f"Workflow error: {e}")
-                finally:
-                    for p in _temp_paths:
-                        _cleanup_temp(p)
-
-    # Results display
-    if "agentic_result" in st.session_state:
-        result = st.session_state["agentic_result"]
-
-        st.markdown("---")
-
-        # Overall classification
-        severity_colors = {
-            "GREEN": ("#d4edda", "#155724", "Routine care"),
-            "YELLOW": ("#fff3cd", "#856404", "Close monitoring"),
-            "RED": ("#f8d7da", "#721c24", "Urgent referral"),
-        }
-        bg, fg, desc = severity_colors.get(result.who_classification, ("#f8f9fa", "#000", "Unknown"))
-
-        st.markdown(f"""
-        <div style="background: {bg}; color: {fg}; padding: 1.5rem; border-radius: 10px; text-align: center; margin: 1rem 0;">
-            <h2 style="margin: 0;">WHO Classification: {result.who_classification}</h2>
-            <p style="margin: 0.5rem 0 0 0; font-size: 1.1rem;">{desc}</p>
-        </div>
-        """, unsafe_allow_html=True)
-
-        # Key metrics
-        m1, m2, m3, m4 = st.columns(4)
-        with m1:
-            st.metric("Agents Run", len(result.agent_traces))
-        with m2:
-            st.metric("Total Time", f"{result.processing_time_ms:.0f} ms")
-        with m3:
-            referral_text = "Yes" if (result.referral_result and result.referral_result.referral_needed) else "No"
-            st.metric("Referral Needed", referral_text)
-        with m4:
-            triage_score = result.triage_result.score if result.triage_result else 0
-            st.metric("Triage Score", triage_score)
-
-        # Clinical synthesis
-        st.subheader("Clinical Synthesis")
-        st.info(result.clinical_synthesis)
-
-        if result.immediate_actions:
-            st.subheader("Immediate Actions")
-            for action in result.immediate_actions:
-                st.markdown(f"- {action}")
-
-        # Visual pipeline flow with status indicators
-        st.markdown("---")
-        st.subheader("Agent Pipeline Execution")
-
-        agent_meta = {
-            "TriageAgent": {"color": "#1976d2", "bg": "#e3f2fd", "icon": "1", "label": "Triage"},
-            "ImageAnalysisAgent": {"color": "#388e3c", "bg": "#e8f5e9", "icon": "2", "label": "Image (MedSigLIP)"},
-            "AudioAnalysisAgent": {"color": "#f57c00", "bg": "#fff3e0", "icon": "3", "label": "Audio (HeAR)"},
-            "ProtocolAgent": {"color": "#7b1fa2", "bg": "#f3e5f5", "icon": "4", "label": "WHO Protocol"},
-            "ReferralAgent": {"color": "#c62828", "bg": "#fce4ec", "icon": "5", "label": "Referral"},
-            "SynthesisAgent": {"color": "#00838f", "bg": "#e0f7fa", "icon": "6", "label": "Synthesis (MedGemma)"},
-        }
-        status_symbols = {"success": "OK", "skipped": "SKIP", "error": "ERR"}
-
-        # Build trace lookup
-        trace_lookup = {t.agent_name: t for t in result.agent_traces}
-
-        # Pipeline status bar
-        pipeline_html_parts = []
-        for agent_name, meta in agent_meta.items():
-            trace = trace_lookup.get(agent_name)
-            if trace:
-                status_sym = status_symbols.get(trace.status, "?")
-                opacity = "1.0" if trace.status == "success" else "0.5"
-                border_style = f"3px solid {meta['color']}" if trace.status == "success" else "2px dashed #999"
-                time_label = f"{trace.processing_time_ms:.0f}ms"
-            else:
-                status_sym = "---"
-                opacity = "0.3"
-                border_style = "2px dashed #ccc"
-                time_label = ""
-
-            pipeline_html_parts.append(f"""
-            <div style="background: {meta['bg']}; padding: 0.4rem 0.7rem; border-radius: 8px;
-                        border: {border_style}; opacity: {opacity}; text-align: center; min-width: 90px;">
-                <div style="font-weight: bold; font-size: 0.8rem; color: {meta['color']};">{meta['label']}</div>
-                <div style="font-size: 0.7rem; color: #666;">{status_sym} {time_label}</div>
-            </div>
-            """)
-
-        pipeline_html = '<div style="display: flex; align-items: center; justify-content: center; gap: 0.3rem; flex-wrap: wrap; margin: 0.5rem 0;">'
-        for i, part in enumerate(pipeline_html_parts):
-            pipeline_html += part
-            if i < len(pipeline_html_parts) - 1:
-                pipeline_html += '<span style="font-size: 1.2rem; color: #999;">&#8594;</span>'
-        pipeline_html += "</div>"
-        st.markdown(pipeline_html, unsafe_allow_html=True)
-
-        # Agent reasoning traces (key feature for Agentic Workflow prize)
-        st.markdown("---")
-        st.subheader("Agent Reasoning Traces")
-
-        for trace in result.agent_traces:
-            meta = agent_meta.get(trace.agent_name, {"color": "#666", "bg": "#f5f5f5", "label": trace.agent_name})
-            status_emoji = {"success": "OK", "skipped": "SKIP", "error": "ERR"}.get(trace.status, "?")
-
-            header_label = f"{meta['label']} [{status_emoji}] - {trace.confidence:.0%} confidence - {trace.processing_time_ms:.0f}ms"
-            with st.expander(header_label, expanded=(trace.status == "success")):
-                # Status bar
-                st.markdown(f"""
-                <div style="background: {meta['bg']}; padding: 0.8rem 1rem; border-radius: 8px;
-                            border-left: 4px solid {meta['color']}; margin-bottom: 0.5rem;">
-                    <strong style="color: {meta['color']};">{trace.agent_name}</strong> &nbsp;|&nbsp;
-                    Status: <strong>{trace.status}</strong> &nbsp;|&nbsp;
-                    Confidence: <strong>{trace.confidence:.1%}</strong> &nbsp;|&nbsp;
-                    Time: <strong>{trace.processing_time_ms:.1f}ms</strong>
-                </div>
-                """, unsafe_allow_html=True)
-
-                # Reasoning steps with numbered styling
-                if trace.reasoning:
-                    st.markdown("**Reasoning Chain:**")
-                    for i, step in enumerate(trace.reasoning, 1):
-                        st.markdown(f"**Step {i}.** {step}")
-
-                # Key findings
-                if trace.findings:
-                    st.markdown("**Key Findings:**")
-                    st.json(trace.findings)
-
-        # Processing time breakdown
-        st.markdown("---")
-        col_chart, col_summary = st.columns([2, 1])
-
-        with col_chart:
-            st.subheader("Processing Time by Agent")
-            import pandas as pd
-            chart_data = pd.DataFrame({
-                "Agent": [agent_meta.get(t.agent_name, {}).get("label", t.agent_name) for t in result.agent_traces],
-                "Time (ms)": [t.processing_time_ms for t in result.agent_traces],
-            })
-            st.bar_chart(chart_data.set_index("Agent"))
-
-        with col_summary:
-            st.subheader("Workflow Summary")
-            total_time = result.processing_time_ms
-            successful = sum(1 for t in result.agent_traces if t.status == "success")
-            skipped = sum(1 for t in result.agent_traces if t.status == "skipped")
-            errors = sum(1 for t in result.agent_traces if t.status == "error")
-            st.markdown(f"""
-            | Metric | Value |
-            |--------|-------|
-            | Total agents | {len(result.agent_traces)} |
-            | Successful | {successful} |
-            | Skipped | {skipped} |
-            | Errors | {errors} |
-            | Total time | {total_time:.0f} ms |
-            | Avg per agent | {total_time / max(len(result.agent_traces), 1):.0f} ms |
-            """)
-
-        # Referral details
-        if result.referral_result and result.referral_result.referral_needed:
-            st.markdown("---")
-            st.subheader("Referral Details")
-            ref = result.referral_result
-            r1, r2, r3 = st.columns(3)
-            with r1:
-                st.metric("Urgency", ref.urgency.upper())
-            with r2:
-                st.metric("Facility", ref.facility_level.title())
-            with r3:
-                st.metric("Timeframe", ref.timeframe)
-            st.warning(f"Reason: {ref.reason}")
-
-
-# Footer
-def render_footer():
-    """Render footer."""
-    st.markdown("---")
-    st.markdown("""
-    <div style="text-align: center; color: #666; font-size: 0.9rem;">
-        <p>NEXUS - Built with Google HAI-DEF for MedGemma Impact Challenge 2026</p>
-        <p>⚠️ This is a screening tool only. Always confirm with laboratory tests.</p>
-    </div>
-    """, unsafe_allow_html=True)
-
-
-if __name__ == "__main__":
-    main()
-    render_footer()
+"""
+NEXUS Streamlit Demo Application
+
+Interactive demo for the NEXUS Maternal-Neonatal Care Platform.
+Built with Google HAI-DEF models for the MedGemma Impact Challenge.
+
+HAI-DEF Models Used:
+- MedSigLIP: Medical image analysis (anemia, jaundice detection)
+- HeAR: Health acoustic representations (cry analysis)
+- MedGemma: Clinical reasoning and synthesis
+"""
+
+import streamlit as st
+from pathlib import Path
+import sys
+import os
+import tempfile
+import json
+
+# Add parent directory to path for imports
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+# Page configuration
+st.set_page_config(
+    page_title="NEXUS - Maternal-Neonatal Care",
+    page_icon="👶",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+# Custom CSS
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 2.5rem;
+        font-weight: bold;
+        color: #1f77b4;
+        text-align: center;
+        margin-bottom: 1rem;
+    }
+    .sub-header {
+        font-size: 1.2rem;
+        color: #666;
+        text-align: center;
+        margin-bottom: 2rem;
+    }
+    .risk-high {
+        background-color: #ffcccc;
+        border: 2px solid #ff0000;
+        padding: 1rem;
+        border-radius: 10px;
+    }
+    .risk-medium {
+        background-color: #fff3cd;
+        border: 2px solid #ffc107;
+        padding: 1rem;
+        border-radius: 10px;
+    }
+    .risk-low {
+        background-color: #d4edda;
+        border: 2px solid #28a745;
+        padding: 1rem;
+        border-radius: 10px;
+    }
+    .metric-card {
+        background-color: #f8f9fa;
+        padding: 1rem;
+        border-radius: 10px;
+        text-align: center;
+    }
+    .model-badge {
+        display: inline-block;
+        padding: 2px 10px;
+        border-radius: 12px;
+        font-size: 0.78rem;
+        font-weight: 600;
+        color: white;
+        letter-spacing: 0.3px;
+    }
+    .stMetric > div {
+        background-color: #f8f9fa;
+        padding: 0.5rem;
+        border-radius: 8px;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+
+@st.cache_resource
+def load_anemia_detector():
+    """Load anemia detector model with error handling."""
+    try:
+        from nexus.anemia_detector import AnemiaDetector
+        detector = AnemiaDetector()
+        return detector, None
+    except Exception as e:
+        return None, str(e)
+
+
+@st.cache_resource
+def load_jaundice_detector():
+    """Load jaundice detector model with error handling."""
+    try:
+        from nexus.jaundice_detector import JaundiceDetector
+        detector = JaundiceDetector()
+        return detector, None
+    except Exception as e:
+        return None, str(e)
+
+
+@st.cache_resource
+def load_cry_analyzer():
+    """Load cry analyzer with error handling."""
+    try:
+        from nexus.cry_analyzer import CryAnalyzer
+        analyzer = CryAnalyzer()
+        return analyzer, None
+    except Exception as e:
+        return None, str(e)
+
+
+@st.cache_resource
+def load_clinical_synthesizer():
+    """Load clinical synthesizer (MedGemma) with error handling."""
+    try:
+        import os
+        from nexus.clinical_synthesizer import ClinicalSynthesizer
+        use_medgemma = os.environ.get("NEXUS_USE_MEDGEMMA", "true").lower() != "false"
+        synthesizer = ClinicalSynthesizer(use_medgemma=use_medgemma)
+        return synthesizer, None
+    except Exception as e:
+        return None, str(e)
+
+
+def get_hai_def_info():
+    """Get HAI-DEF models information with validated accuracy numbers."""
+    return {
+        "MedSigLIP": {
+            "name": "MedSigLIP (google/medsiglip-448)",
+            "use": "Image analysis for anemia and jaundice detection + bilirubin regression",
+            "method": "Zero-shot classification (max-similarity, 8 prompts/class) + trained SVM/LR classifiers on embeddings",
+            "accuracy": "Anemia: trained classifier on augmented data, Jaundice: trained classifier on 2,235 images, Bilirubin: MAE 2.67 mg/dL (r=0.77)",
+            "badge": "Vision",
+            "badge_color": "#388e3c",
+        },
+        "HeAR": {
+            "name": "HeAR (google/hear-pytorch)",
+            "use": "Infant cry analysis for asphyxia and cry type classification",
+            "method": "512-dim health acoustic embeddings + trained linear classifier on donate-a-cry dataset (5-class: hungry, belly_pain, burping, discomfort, tired)",
+            "accuracy": "Trained cry type classifier with asphyxia risk derivation from distress patterns",
+            "badge": "Audio",
+            "badge_color": "#f57c00",
+        },
+        "MedGemma": {
+            "name": "MedGemma 1.5 4B (google/medgemma-1.5-4b-it)",
+            "use": "Clinical reasoning and recommendation synthesis",
+            "method": "4-bit NF4 quantized inference with WHO IMNCI protocol-aligned synthesis and 6-agent reasoning traces",
+            "accuracy": "Protocol-aligned clinical recommendations with structured reasoning chains",
+            "badge": "Language",
+            "badge_color": "#1976d2",
+        },
+    }
+
+
+def main():
+    """Main application."""
+
+    # Header
+    st.markdown('<div class="main-header">NEXUS</div>', unsafe_allow_html=True)
+    st.markdown(
+        '<div class="sub-header">AI-Powered Maternal-Neonatal Care Platform</div>',
+        unsafe_allow_html=True
+    )
+
+    # Sidebar
+    with st.sidebar:
+        st.markdown("## 🏥 NEXUS")
+        st.markdown("---")
+
+        assessment_type = st.radio(
+            "Select Assessment Type",
+            [
+                "Maternal Anemia Screening",
+                "Neonatal Jaundice Detection",
+                "Cry Analysis",
+                "Combined Assessment",
+                "Agentic Workflow",
+                "HAI-DEF Models Info"
+            ],
+            index=0,
+        )
+
+        st.markdown("---")
+        st.markdown("### About NEXUS")
+        st.markdown("""
+        NEXUS uses AI to provide non-invasive screening for:
+        - **Maternal Anemia** via conjunctiva imaging
+        - **Neonatal Jaundice** via skin color analysis
+        - **Birth Asphyxia** via cry pattern analysis
+
+        Built with **Google HAI-DEF models** for the MedGemma Impact Challenge 2026.
+        """)
+
+        st.markdown("---")
+        st.markdown("### Edge AI Mode")
+        edge_mode = st.toggle("Enable Edge AI Mode", value=False, key="edge_mode")
+        if edge_mode:
+            st.success("Edge AI: INT8 quantized models + offline inference")
+        else:
+            st.info("Cloud mode: Full-precision HAI-DEF models")
+
+        st.markdown("---")
+        st.markdown("### HAI-DEF Models")
+        st.markdown("""
+        - **MedSigLIP**: Vision (trained classifiers)
+        - **HeAR**: Audio (trained cry classifier)
+        - **MedGemma 1.5**: Clinical AI (4-bit NF4)
+        """)
+
+    # Show Edge AI banner when enabled
+    if edge_mode:
+        render_edge_ai_banner()
+
+    # Main content based on selection
+    if assessment_type == "Maternal Anemia Screening":
+        render_anemia_screening()
+    elif assessment_type == "Neonatal Jaundice Detection":
+        render_jaundice_detection()
+    elif assessment_type == "Cry Analysis":
+        render_cry_analysis()
+    elif assessment_type == "Combined Assessment":
+        render_combined_assessment()
+    elif assessment_type == "Agentic Workflow":
+        render_agentic_workflow()
+    else:
+        render_hai_def_info()
+
+
+def render_edge_ai_banner():
+    """Show Edge AI mode status and model metrics."""
+    st.markdown("""
+    <div style="background: linear-gradient(135deg, #1a237e 0%, #0d47a1 100%);
+                color: white; padding: 1rem 1.5rem; border-radius: 10px; margin-bottom: 1rem;">
+        <h4 style="margin:0; color: white;">Edge AI Mode Active</h4>
+        <p style="margin: 0.3rem 0 0 0; opacity: 0.9; font-size: 0.9rem;">
+            Running INT8 quantized models for offline-capable inference on low-resource devices.
+        </p>
+    </div>
+    """, unsafe_allow_html=True)
+
+    col1, col2, col3, col4 = st.columns(4)
+    with col1:
+        st.metric("MedSigLIP INT8", "111.2 MB", "-86% memory")
+    with col2:
+        st.metric("Acoustic Model", "0.6 MB", "INT8 quantized")
+    with col3:
+        st.metric("Text Embeddings", "12 KB", "Pre-computed")
+    with col4:
+        st.metric("Total Edge Size", "~289 MB", "Offline-ready")
+
+    with st.expander("Edge AI Details"):
+        st.markdown("""
+        **Quantization**: Dynamic INT8 (PyTorch `quantize_dynamic`, qnnpack backend)
+
+        | Component | Cloud (FP32) | Edge (INT8) | Compression |
+        |-----------|-------------|-------------|-------------|
+        | MedSigLIP Vision | 812.6 MB | 111.2 MB | **7.31x** |
+        | Acoustic Model | 0.665 MB | 0.599 MB | 1.11x |
+        | CPU Latency | 97.7 ms | ~65 ms (ARM est.) | ~1.5x faster |
+
+        **Target Devices**: Android 8.0+, ARM Cortex-A53, 2GB RAM
+
+        **Offline Capabilities**:
+        - Image analysis via INT8 MedSigLIP + pre-computed binary text embeddings
+        - Audio analysis via INT8 acoustic feature extractor
+        - Clinical reasoning via rule-based WHO IMNCI protocols (no MedGemma required)
+        """)
+
+
+def _cleanup_temp(path: str) -> None:
+    """Safely remove a temporary file."""
+    try:
+        if path and os.path.exists(path):
+            os.unlink(path)
+    except OSError:
+        pass
+
+
+def _save_upload_to_temp(uploaded_file, suffix: str) -> str:
+    """Save an uploaded file to a temporary path and return the path."""
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=suffix)
+    try:
+        tmp.write(uploaded_file.getvalue())
+        tmp.close()
+        return tmp.name
+    except Exception:
+        tmp.close()
+        _cleanup_temp(tmp.name)
+        raise
+
+
+def _model_badge(name: str, color: str) -> str:
+    """Return an HTML badge for displaying which HAI-DEF model is active."""
+    return (
+        f'<span style="background:{color}; color:white; padding:2px 10px; '
+        f'border-radius:12px; font-size:0.78rem; font-weight:600; '
+        f'letter-spacing:0.3px;">{name}</span>'
+    )
+
+
+def render_anemia_screening():
+    """Render anemia screening interface."""
+    st.header("Maternal Anemia Screening")
+    st.markdown(
+        f"Upload a clear image of the inner eyelid (conjunctiva) for anemia screening. "
+        f'{_model_badge("MedSigLIP", "#388e3c")}',
+        unsafe_allow_html=True,
+    )
+
+    col1, col2 = st.columns([1, 1])
+
+    with col1:
+        st.subheader("Upload Image")
+        uploaded_file = st.file_uploader(
+            "Choose a conjunctiva image",
+            type=["jpg", "jpeg", "png"],
+            key="anemia_upload"
+        )
+
+        if uploaded_file:
+            image_bytes = uploaded_file.getvalue()
+            st.image(image_bytes, caption="Uploaded Image", use_container_width=True)
+
+    with col2:
+        st.subheader("Analysis Results")
+
+        if uploaded_file:
+            with st.spinner("Analyzing image..."):
+                tmp_path = None
+                try:
+                    detector, load_err = load_anemia_detector()
+                    if detector is None:
+                        st.error(f"Could not load model: {load_err}")
+                        return
+
+                    tmp_path = _save_upload_to_temp(uploaded_file, ".jpg")
+
+                    result = detector.detect(tmp_path)
+                    color_info = detector.analyze_color_features(tmp_path)
+
+                    # Display results
+                    risk_class = f"risk-{result['risk_level']}"
+                    st.markdown(f'<div class="{risk_class}">', unsafe_allow_html=True)
+
+                    if result["is_anemic"]:
+                        st.error("⚠️ ANEMIA DETECTED")
+                    else:
+                        st.success("✅ No Anemia Detected")
+
+                    st.markdown("</div>", unsafe_allow_html=True)
+
+                    # Metrics
+                    col_a, col_b, col_c = st.columns(3)
+                    with col_a:
+                        st.metric("Confidence", f"{result['confidence']:.1%}")
+                    with col_b:
+                        st.metric("Risk Level", result['risk_level'].upper())
+                    with col_c:
+                        st.metric("Est. Hemoglobin", f"{color_info['estimated_hemoglobin']} g/dL")
+
+                    # Recommendation
+                    st.markdown("### Recommendation")
+                    st.info(result["recommendation"])
+
+                    # Color analysis
+                    with st.expander("Technical Details"):
+                        st.json({
+                            "anemia_score": round(result["anemia_score"], 3),
+                            "healthy_score": round(result["healthy_score"], 3),
+                            "red_ratio": round(color_info["red_ratio"], 3),
+                            "pallor_index": round(color_info["pallor_index"], 3),
+                        })
+
+                except Exception as e:
+                    st.error(f"Error analyzing image: {e}")
+                finally:
+                    _cleanup_temp(tmp_path)
+        else:
+            st.info("👆 Upload an image to begin analysis")
+
+
+def render_jaundice_detection():
+    """Render jaundice detection interface."""
+    st.header("Neonatal Jaundice Detection")
+    st.markdown(
+        f"Upload an image of the newborn's skin or sclera for jaundice assessment. "
+        f'{_model_badge("MedSigLIP", "#388e3c")}',
+        unsafe_allow_html=True,
+    )
+
+    col1, col2 = st.columns([1, 1])
+
+    with col1:
+        st.subheader("Upload Image")
+        uploaded_file = st.file_uploader(
+            "Choose a neonatal image",
+            type=["jpg", "jpeg", "png"],
+            key="jaundice_upload"
+        )
+
+        if uploaded_file:
+            image_bytes = uploaded_file.getvalue()
+            st.image(image_bytes, caption="Uploaded Image", use_container_width=True)
+
+        # Patient info
+        st.subheader("Patient Information (Optional)")
+        age_days = st.number_input("Age (days)", min_value=0, max_value=28, value=3)
+        birth_weight = st.number_input("Birth weight (grams)", min_value=500, max_value=5000, value=3000)
+
+    with col2:
+        st.subheader("Analysis Results")
+
+        if uploaded_file:
+            with st.spinner("Analyzing image..."):
+                tmp_path = None
+                try:
+                    detector, load_err = load_jaundice_detector()
+                    if detector is None:
+                        st.error(f"Could not load model: {load_err}")
+                        return
+
+                    tmp_path = _save_upload_to_temp(uploaded_file, ".jpg")
+
+                    result = detector.detect(tmp_path)
+                    zone_info = detector.analyze_kramer_zones(tmp_path)
+
+                    # Display results
+                    risk_class = "risk-high" if result["needs_phototherapy"] else (
+                        "risk-medium" if result["severity"] in ["moderate", "mild"] else "risk-low"
+                    )
+                    st.markdown(f'<div class="{risk_class}">', unsafe_allow_html=True)
+
+                    if result["has_jaundice"]:
+                        st.warning(f"⚠️ JAUNDICE DETECTED - {result['severity'].upper()}")
+                    else:
+                        st.success("✅ No Significant Jaundice")
+
+                    st.markdown("</div>", unsafe_allow_html=True)
+
+                    # Metrics - show ML bilirubin if available
+                    col_a, col_b, col_c = st.columns(3)
+                    with col_a:
+                        bili_value = result.get('estimated_bilirubin_ml', result.get('estimated_bilirubin', 0))
+                        bili_method = result.get('bilirubin_method', 'Color Analysis')
+                        st.metric("Est. Bilirubin", f"{bili_value} mg/dL")
+                        st.caption(f"Method: {bili_method}")
+                    with col_b:
+                        st.metric("Severity", result['severity'].upper())
+                    with col_c:
+                        st.metric("Kramer Zone", zone_info['kramer_zone'])
+
+                    # Phototherapy indicator
+                    if result["needs_phototherapy"]:
+                        st.error("🔆 PHOTOTHERAPY RECOMMENDED")
+
+                    # Recommendation
+                    st.markdown("### Recommendation")
+                    st.info(result["recommendation"])
+
+                    # Zone analysis
+                    with st.expander("Kramer Zone Analysis"):
+                        st.write(f"**Zone**: {zone_info['kramer_zone']} - {zone_info['zone_description']}")
+                        st.write(f"**Yellow Index**: {zone_info['yellow_index']}")
+                        st.progress(min(zone_info['yellow_index'] * 2, 1.0))
+
+                    # Technical details
+                    with st.expander("Technical Details"):
+                        details = {
+                            "jaundice_score": round(result["jaundice_score"], 3),
+                            "confidence": round(result["confidence"], 3),
+                            "model": result.get("model", "unknown"),
+                            "model_type": result.get("model_type", "unknown"),
+                            "bilirubin_method": result.get("bilirubin_method", "Color Analysis"),
+                        }
+                        if result.get("estimated_bilirubin_ml") is not None:
+                            details["bilirubin_ml"] = result["estimated_bilirubin_ml"]
+                            details["bilirubin_color"] = result["estimated_bilirubin"]
+                        st.json(details)
+
+                except Exception as e:
+                    st.error(f"Error analyzing image: {e}")
+                finally:
+                    _cleanup_temp(tmp_path)
+        else:
+            st.info("👆 Upload an image to begin analysis")
+
+
+def render_cry_analysis():
+    """Render cry analysis interface."""
+    st.header("Infant Cry Analysis")
+    st.markdown(
+        f"Upload an audio recording of the infant's cry for analysis. "
+        f'{_model_badge("HeAR", "#f57c00")}',
+        unsafe_allow_html=True,
+    )
+
+    col1, col2 = st.columns([1, 1])
+
+    with col1:
+        st.subheader("Upload Audio")
+        uploaded_file = st.file_uploader(
+            "Choose a cry audio file",
+            type=["wav", "mp3", "ogg"],
+            key="cry_upload"
+        )
+
+        if uploaded_file:
+            st.audio(uploaded_file)
+
+    with col2:
+        st.subheader("Analysis Results")
+
+        if uploaded_file:
+            with st.spinner("Analyzing cry..."):
+                tmp_path = None
+                try:
+                    analyzer, load_err = load_cry_analyzer()
+                    if analyzer is None:
+                        st.error(f"Could not load model: {load_err}")
+                        return
+
+                    tmp_path = _save_upload_to_temp(uploaded_file, ".wav")
+
+                    result = analyzer.analyze(tmp_path)
+
+                    # Display results
+                    risk_class = f"risk-{result['risk_level']}"
+                    st.markdown(f'<div class="{risk_class}">', unsafe_allow_html=True)
+
+                    if result["is_abnormal"]:
+                        st.error("⚠️ ABNORMAL CRY PATTERN DETECTED")
+                    else:
+                        st.success("✅ Normal Cry Pattern")
+
+                    st.markdown("</div>", unsafe_allow_html=True)
+
+                    # Metrics
+                    col_a, col_b, col_c = st.columns(3)
+                    with col_a:
+                        st.metric("Asphyxia Risk", f"{result['asphyxia_risk']:.1%}")
+                    with col_b:
+                        st.metric("Cry Type", result['cry_type'].title())
+                    with col_c:
+                        st.metric("F0 (Pitch)", f"{result['features']['f0_mean']:.0f} Hz")
+
+                    # Recommendation
+                    st.markdown("### Recommendation")
+                    st.info(result["recommendation"])
+
+                    # Acoustic features
+                    with st.expander("Acoustic Features"):
+                        st.json(result["features"])
+
+                except Exception as e:
+                    st.error(f"Error analyzing audio: {e}")
+                finally:
+                    _cleanup_temp(tmp_path)
+        else:
+            st.info("👆 Upload an audio file to begin analysis")
+
+
+def render_combined_assessment():
+    """Render combined assessment interface using Clinical Synthesizer."""
+    st.header("Combined Clinical Assessment")
+    st.markdown(
+        f"Upload multiple inputs for a comprehensive assessment using **MedGemma Clinical Synthesizer**. "
+        f"This combines findings from all HAI-DEF models to provide integrated clinical recommendations. "
+        f'{_model_badge("MedSigLIP", "#388e3c")} '
+        f'{_model_badge("HeAR", "#f57c00")} '
+        f'{_model_badge("MedGemma", "#1976d2")}',
+        unsafe_allow_html=True,
+    )
+
+    # Reset findings each time this tab is rendered to prevent
+    # stale data from previous patients contaminating results
+    st.session_state.findings = {
+        "anemia": None,
+        "jaundice": None,
+        "cry": None
+    }
+
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        st.subheader("🩸 Anemia Screening")
+        anemia_file = st.file_uploader(
+            "Conjunctiva image",
+            type=["jpg", "jpeg", "png"],
+            key="combined_anemia"
+        )
+        if anemia_file:
+            st.image(anemia_file.getvalue(), use_container_width=True)
+            with st.spinner("Analyzing..."):
+                try:
+                    detector, load_err = load_anemia_detector()
+                    if detector is None:
+                        st.error(f"Model error: {load_err}")
+                    else:
+                        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
+                            tmp.write(anemia_file.getvalue())
+                            result = detector.detect(tmp.name)
+                        st.session_state.findings["anemia"] = result
+                        if result["is_anemic"]:
+                            st.error(f"Anemia: {result['risk_level'].upper()}")
+                        else:
+                            st.success("No Anemia")
+                except Exception as e:
+                    st.error(f"Error: {e}")
+
+    with col2:
+        st.subheader("👶 Jaundice Detection")
+        jaundice_file = st.file_uploader(
+            "Neonatal skin image",
+            type=["jpg", "jpeg", "png"],
+            key="combined_jaundice"
+        )
+        if jaundice_file:
+            st.image(jaundice_file.getvalue(), use_container_width=True)
+            with st.spinner("Analyzing..."):
+                try:
+                    detector, load_err = load_jaundice_detector()
+                    if detector is None:
+                        st.error(f"Model error: {load_err}")
+                    else:
+                        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
+                            tmp.write(jaundice_file.getvalue())
+                            result = detector.detect(tmp.name)
+                            st.session_state.findings["jaundice"] = result
+                            if result["has_jaundice"]:
+                                st.warning(f"Jaundice: {result['severity'].upper()}")
+                            else:
+                                st.success("No Jaundice")
+                except Exception as e:
+                    st.error(f"Error: {e}")
+
+    with col3:
+        st.subheader("🔊 Cry Analysis")
+        cry_file = st.file_uploader(
+            "Cry audio",
+            type=["wav", "mp3", "ogg"],
+            key="combined_cry"
+        )
+        if cry_file:
+            st.audio(cry_file)
+            with st.spinner("Analyzing..."):
+                try:
+                    analyzer, load_err = load_cry_analyzer()
+                    if analyzer is None:
+                        st.error(f"Model error: {load_err}")
+                        raise RuntimeError(load_err)
+                    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
+                        tmp.write(cry_file.getvalue())
+                        result = analyzer.analyze(tmp.name)
+                        st.session_state.findings["cry"] = result
+                        if result["is_abnormal"]:
+                            st.error(f"Abnormal Cry: {result['risk_level'].upper()}")
+                        else:
+                            st.success("Normal Cry")
+                except Exception as e:
+                    st.error(f"Error: {e}")
+
+    # Clinical Synthesis Section
+    st.markdown("---")
+    st.subheader("🏥 Clinical Synthesis (MedGemma)")
+
+    # Check if any findings are available
+    has_findings = any(v is not None for v in st.session_state.findings.values())
+
+    if has_findings:
+        if st.button("Generate Clinical Synthesis", type="primary"):
+            with st.spinner("Synthesizing findings with MedGemma..."):
+                try:
+                    synthesizer, load_err = load_clinical_synthesizer()
+                    if synthesizer is None:
+                        st.error(f"Could not load synthesizer: {load_err}")
+                        return
+
+                    # Prepare findings dict
+                    findings = {}
+                    if st.session_state.findings["anemia"]:
+                        findings["anemia"] = st.session_state.findings["anemia"]
+                    if st.session_state.findings["jaundice"]:
+                        findings["jaundice"] = st.session_state.findings["jaundice"]
+                    if st.session_state.findings["cry"]:
+                        findings["cry"] = st.session_state.findings["cry"]
+
+                    synthesis = synthesizer.synthesize(findings)
+
+                    # Display synthesis results
+                    severity_level = synthesis.get("severity_level", "GREEN")
+                    severity_colors = {
+                        "GREEN": ("🟢", "#d4edda", "#155724"),
+                        "YELLOW": ("🟡", "#fff3cd", "#856404"),
+                        "RED": ("🔴", "#f8d7da", "#721c24")
+                    }
+                    emoji, bg_color, text_color = severity_colors.get(severity_level, ("⚪", "#f8f9fa", "#000"))
+
+                    st.markdown(f"""
+                    <div style="background-color: {bg_color}; padding: 1.5rem; border-radius: 10px; margin: 1rem 0;">
+                        <h3 style="color: {text_color}; margin: 0;">{emoji} Severity: {severity_level}</h3>
+                        <p style="color: {text_color}; font-size: 1.1rem; margin-top: 0.5rem;">{synthesis.get('severity_description', '')}</p>
+                    </div>
+                    """, unsafe_allow_html=True)
+
+                    # Summary
+                    st.markdown("### Summary")
+                    st.info(synthesis.get("summary", "No summary available"))
+
+                    # Actions
+                    if synthesis.get("immediate_actions"):
+                        st.markdown("### Immediate Actions")
+                        for action in synthesis["immediate_actions"]:
+                            st.markdown(f"- {action}")
+
+                    # Referral
+                    col_a, col_b = st.columns(2)
+                    with col_a:
+                        st.markdown("### Referral Status")
+                        if synthesis.get("referral_needed"):
+                            st.error(f"⚠️ REFERRAL NEEDED: {synthesis.get('referral_urgency', 'standard').upper()}")
+                        else:
+                            st.success("✅ No referral needed")
+
+                    with col_b:
+                        st.markdown("### Follow-up")
+                        st.info(synthesis.get("follow_up", "Schedule routine follow-up"))
+
+                    # Technical details
+                    with st.expander("Technical Details"):
+                        model_name = synthesis.get("model", "unknown")
+                        st.json({
+                            "model": model_name,
+                            "model_id": synthesis.get("model_id", ""),
+                            "generated_at": synthesis.get("generated_at"),
+                            "urgent_conditions": synthesis.get("urgent_conditions", []),
+                        })
+                        if model_name and "Fallback" not in str(model_name):
+                            st.success(f"Synthesis powered by {model_name}")
+                        elif "Fallback" in str(model_name):
+                            st.warning("Using rule-based fallback (MedGemma unavailable)")
+
+                except Exception as e:
+                    st.error(f"Error generating synthesis: {e}")
+    else:
+        st.info("👆 Upload at least one input (image or audio) to generate clinical synthesis")
+
+
+def render_hai_def_info():
+    """Render HAI-DEF models information."""
+    st.header("Google HAI-DEF Models")
+    st.markdown("""
+    NEXUS is built using **Google Health AI Developer Foundations (HAI-DEF)** models,
+    designed specifically for healthcare applications in resource-limited settings.
+    """)
+
+    hai_def = get_hai_def_info()
+
+    # MedSigLIP
+    st.markdown("---")
+    col1, col2 = st.columns([1, 2])
+    with col1:
+        st.markdown("### 🖼️ MedSigLIP")
+        st.info("google/medsiglip-448\n\nHAI-DEF Vision Model")
+    with col2:
+        info = hai_def["MedSigLIP"]
+        st.markdown(f"**Model**: {info['name']}")
+        st.markdown(f"**Use Case**: {info['use']}")
+        st.markdown(f"**Method**: {info['method']}")
+        st.markdown(f"**Validated Performance**: {info['accuracy']}")
+        st.markdown("""
+        MedSigLIP enables zero-shot medical image classification using
+        text prompts. NEXUS extends this with trained SVM/LR classifiers
+        on MedSigLIP embeddings (with data augmentation) for improved
+        accuracy, plus a novel 3-layer MLP regression head for continuous
+        bilirubin prediction from frozen embeddings.
+        """)
+
+    # HeAR
+    st.markdown("---")
+    col1, col2 = st.columns([1, 2])
+    with col1:
+        st.markdown("### 🔊 HeAR")
+        st.info("google/hear-pytorch\n\nHAI-DEF Audio Model")
+    with col2:
+        info = hai_def["HeAR"]
+        st.markdown(f"**Model**: {info['name']}")
+        st.markdown(f"**Use Case**: {info['use']}")
+        st.markdown(f"**Method**: {info['method']}")
+        st.markdown(f"**Validated Performance**: {info['accuracy']}")
+        st.markdown("""
+        HeAR (Health Acoustic Representations) produces 512-dim embeddings
+        from 2-second audio clips at 16kHz. NEXUS trains a linear classifier
+        on HeAR embeddings for 5-class cry type classification (hungry,
+        belly_pain, burping, discomfort, tired) and derives asphyxia risk
+        from distress patterns.
+        """)
+
+    # MedGemma
+    st.markdown("---")
+    col1, col2 = st.columns([1, 2])
+    with col1:
+        st.markdown("### 🧠 MedGemma")
+        st.info("google/medgemma-1.5-4b-it\n\nHAI-DEF Language Model")
+    with col2:
+        info = hai_def["MedGemma"]
+        st.markdown(f"**Model**: {info['name']}")
+        st.markdown(f"**Use Case**: {info['use']}")
+        st.markdown(f"**Method**: {info['method']}")
+        st.markdown(f"**Validated Performance**: {info['accuracy']}")
+        st.markdown("""
+        MedGemma 1.5 provides clinical reasoning capabilities via 4-bit NF4
+        quantized inference (~2 GB VRAM). It synthesizes multi-modal findings
+        into actionable recommendations following WHO IMNCI protocols,
+        producing structured reasoning chains within the 6-agent pipeline.
+        """)
+
+    # Competition Info
+    st.markdown("---")
+    st.subheader("🏆 MedGemma Impact Challenge 2026")
+    st.markdown("""
+    NEXUS is being developed for the [MedGemma Impact Challenge](https://www.kaggle.com/competitions/medgemma-impact-challenge-2026)
+    on Kaggle.
+
+    **Competition Focus**: Solutions for resource-limited healthcare settings using HAI-DEF models.
+
+    **NEXUS Impact**:
+    - 📍 Target: Sub-Saharan Africa and South Asia
+    - 👩‍⚕️ Users: Community Health Workers
+    - 🎯 Goals: Reduce maternal/neonatal mortality
+    - 📱 Deployment: Offline-capable mobile app
+    """)
+
+
+def render_agentic_workflow():
+    """Render the agentic workflow interface with reasoning traces."""
+    st.header("Agentic Clinical Workflow")
+    st.markdown(
+        f"**6-Agent Pipeline** with step-by-step reasoning traces. "
+        f"Each agent explains its clinical decision process, providing a full audit trail. "
+        f'{_model_badge("MedSigLIP", "#388e3c")} '
+        f'{_model_badge("HeAR", "#f57c00")} '
+        f'{_model_badge("MedGemma", "#1976d2")}',
+        unsafe_allow_html=True,
+    )
+
+    # Pipeline diagram
+    st.markdown("""
+    <div style="display: flex; align-items: center; justify-content: center; gap: 0.5rem; flex-wrap: wrap; margin: 1rem 0;">
+        <div style="background: #e3f2fd; padding: 0.5rem 1rem; border-radius: 8px; font-weight: bold; border: 2px solid #1976d2;">Triage</div>
+        <span style="font-size: 1.5rem;">&#8594;</span>
+        <div style="background: #e8f5e9; padding: 0.5rem 1rem; border-radius: 8px; font-weight: bold; border: 2px solid #388e3c;">Image (MedSigLIP)</div>
+        <span style="font-size: 1.5rem;">&#8594;</span>
+        <div style="background: #fff3e0; padding: 0.5rem 1rem; border-radius: 8px; font-weight: bold; border: 2px solid #f57c00;">Audio (HeAR)</div>
+        <span style="font-size: 1.5rem;">&#8594;</span>
+        <div style="background: #f3e5f5; padding: 0.5rem 1rem; border-radius: 8px; font-weight: bold; border: 2px solid #7b1fa2;">Protocol (WHO)</div>
+        <span style="font-size: 1.5rem;">&#8594;</span>
+        <div style="background: #fce4ec; padding: 0.5rem 1rem; border-radius: 8px; font-weight: bold; border: 2px solid #c62828;">Referral</div>
+        <span style="font-size: 1.5rem;">&#8594;</span>
+        <div style="background: #e0f7fa; padding: 0.5rem 1rem; border-radius: 8px; font-weight: bold; border: 2px solid #00838f;">Synthesis (MedGemma)</div>
+    </div>
+    """, unsafe_allow_html=True)
+
+    st.markdown("---")
+
+    # Input section
+    col_left, col_right = st.columns([1, 1])
+
+    with col_left:
+        st.subheader("Patient & Inputs")
+        patient_type = st.selectbox("Patient Type", ["newborn", "pregnant"], key="agentic_patient")
+
+        # Danger signs
+        st.markdown("**Danger Signs**")
+        danger_signs = []
+        if patient_type == "pregnant":
+            sign_options = [
+                ("Severe headache", "high"),
+                ("Blurred vision", "high"),
+                ("Convulsions", "critical"),
+                ("Severe abdominal pain", "high"),
+                ("Vaginal bleeding", "critical"),
+                ("High fever", "high"),
+                ("Severe pallor", "medium"),
+            ]
+        else:
+            sign_options = [
+                ("Not breathing at birth", "critical"),
+                ("Convulsions", "critical"),
+                ("Severe chest indrawing", "high"),
+                ("Not feeding", "high"),
+                ("High fever (>38C)", "high"),
+                ("Hypothermia (<35.5C)", "high"),
+                ("Lethargy / unconscious", "critical"),
+                ("Umbilical redness", "medium"),
+            ]
+
+        selected_signs = st.multiselect(
+            "Select present danger signs",
+            [s[0] for s in sign_options],
+            key="agentic_signs"
+        )
+        for label, severity in sign_options:
+            if label in selected_signs:
+                danger_signs.append({
+                    "id": label.lower().replace(" ", "_"),
+                    "label": label,
+                    "severity": severity,
+                    "present": True,
+                })
+
+        # Image uploads
+        st.markdown("**Clinical Images**")
+        conjunctiva_file = st.file_uploader(
+            "Conjunctiva image (anemia)", type=["jpg", "jpeg", "png"],
+            key="agentic_conjunctiva"
+        )
+        skin_file = st.file_uploader(
+            "Skin image (jaundice)", type=["jpg", "jpeg", "png"],
+            key="agentic_skin"
+        )
+        cry_file = st.file_uploader(
+            "Cry audio", type=["wav", "mp3", "ogg"],
+            key="agentic_cry"
+        )
+
+    with col_right:
+        st.subheader("Workflow Execution")
+
+        if st.button("Run Agentic Assessment", type="primary", key="run_agentic"):
+            with st.spinner("Running 6-agent workflow..."):
+                try:
+                    from nexus.agentic_workflow import (
+                        AgenticWorkflowEngine,
+                        AgentPatientInfo,
+                        DangerSign,
+                        WorkflowInput,
+                    )
+
+                    # Save uploaded files (track for cleanup)
+                    _temp_paths = []
+                    conjunctiva_path = None
+                    skin_path = None
+                    cry_path = None
+
+                    if conjunctiva_file:
+                        conjunctiva_path = _save_upload_to_temp(conjunctiva_file, ".jpg")
+                        _temp_paths.append(conjunctiva_path)
+
+                    if skin_file:
+                        skin_path = _save_upload_to_temp(skin_file, ".jpg")
+                        _temp_paths.append(skin_path)
+
+                    if cry_file:
+                        cry_path = _save_upload_to_temp(cry_file, ".wav")
+                        _temp_paths.append(cry_path)
+
+                    # Build workflow input
+                    signs = [
+                        DangerSign(
+                            id=s["id"], label=s["label"],
+                            severity=s["severity"], present=True,
+                        )
+                        for s in danger_signs
+                    ]
+
+                    info = AgentPatientInfo(patient_type=patient_type)
+                    workflow_input = WorkflowInput(
+                        patient_type=patient_type,
+                        patient_info=info,
+                        danger_signs=signs,
+                        conjunctiva_image=conjunctiva_path,
+                        skin_image=skin_path,
+                        cry_audio=cry_path,
+                    )
+
+                    # Run workflow — reuse cached model instances when available
+                    anemia_det, _ = load_anemia_detector()
+                    jaundice_det, _ = load_jaundice_detector()
+                    cry_ana, _ = load_cry_analyzer()
+                    synth, _ = load_clinical_synthesizer()
+
+                    engine = AgenticWorkflowEngine(
+                        anemia_detector=anemia_det,
+                        jaundice_detector=jaundice_det,
+                        cry_analyzer=cry_ana,
+                        synthesizer=synth,
+                    )
+                    result = engine.execute(workflow_input)
+
+                    st.session_state["agentic_result"] = result
+                    st.success("Workflow complete!")
+
+                except Exception as e:
+                    st.error(f"Workflow error: {e}")
+                finally:
+                    for p in _temp_paths:
+                        _cleanup_temp(p)
+
+    # Results display
+    if "agentic_result" in st.session_state:
+        result = st.session_state["agentic_result"]
+
+        st.markdown("---")
+
+        # Overall classification
+        severity_colors = {
+            "GREEN": ("#d4edda", "#155724", "Routine care"),
+            "YELLOW": ("#fff3cd", "#856404", "Close monitoring"),
+            "RED": ("#f8d7da", "#721c24", "Urgent referral"),
+        }
+        bg, fg, desc = severity_colors.get(result.who_classification, ("#f8f9fa", "#000", "Unknown"))
+
+        st.markdown(f"""
+        <div style="background: {bg}; color: {fg}; padding: 1.5rem; border-radius: 10px; text-align: center; margin: 1rem 0;">
+            <h2 style="margin: 0;">WHO Classification: {result.who_classification}</h2>
+            <p style="margin: 0.5rem 0 0 0; font-size: 1.1rem;">{desc}</p>
+        </div>
+        """, unsafe_allow_html=True)
+
+        # Key metrics
+        m1, m2, m3, m4 = st.columns(4)
+        with m1:
+            st.metric("Agents Run", len(result.agent_traces))
+        with m2:
+            st.metric("Total Time", f"{result.processing_time_ms:.0f} ms")
+        with m3:
+            referral_text = "Yes" if (result.referral_result and result.referral_result.referral_needed) else "No"
+            st.metric("Referral Needed", referral_text)
+        with m4:
+            triage_score = result.triage_result.score if result.triage_result else 0
+            st.metric("Triage Score", triage_score)
+
+        # Clinical synthesis
+        st.subheader("Clinical Synthesis")
+        st.info(result.clinical_synthesis)
+
+        if result.immediate_actions:
+            st.subheader("Immediate Actions")
+            for action in result.immediate_actions:
+                st.markdown(f"- {action}")
+
+        # Visual pipeline flow with status indicators
+        st.markdown("---")
+        st.subheader("Agent Pipeline Execution")
+
+        agent_meta = {
+            "TriageAgent": {"color": "#1976d2", "bg": "#e3f2fd", "icon": "1", "label": "Triage"},
+            "ImageAnalysisAgent": {"color": "#388e3c", "bg": "#e8f5e9", "icon": "2", "label": "Image (MedSigLIP)"},
+            "AudioAnalysisAgent": {"color": "#f57c00", "bg": "#fff3e0", "icon": "3", "label": "Audio (HeAR)"},
+            "ProtocolAgent": {"color": "#7b1fa2", "bg": "#f3e5f5", "icon": "4", "label": "WHO Protocol"},
+            "ReferralAgent": {"color": "#c62828", "bg": "#fce4ec", "icon": "5", "label": "Referral"},
+            "SynthesisAgent": {"color": "#00838f", "bg": "#e0f7fa", "icon": "6", "label": "Synthesis (MedGemma)"},
+        }
+        status_symbols = {"success": "OK", "skipped": "SKIP", "error": "ERR"}
+
+        # Build trace lookup
+        trace_lookup = {t.agent_name: t for t in result.agent_traces}
+
+        # Pipeline status bar
+        pipeline_html_parts = []
+        for agent_name, meta in agent_meta.items():
+            trace = trace_lookup.get(agent_name)
+            if trace:
+                status_sym = status_symbols.get(trace.status, "?")
+                opacity = "1.0" if trace.status == "success" else "0.5"
+                border_style = f"3px solid {meta['color']}" if trace.status == "success" else "2px dashed #999"
+                time_label = f"{trace.processing_time_ms:.0f}ms"
+            else:
+                status_sym = "---"
+                opacity = "0.3"
+                border_style = "2px dashed #ccc"
+                time_label = ""
+
+            pipeline_html_parts.append(f"""
+            <div style="background: {meta['bg']}; padding: 0.4rem 0.7rem; border-radius: 8px;
+                        border: {border_style}; opacity: {opacity}; text-align: center; min-width: 90px;">
+                <div style="font-weight: bold; font-size: 0.8rem; color: {meta['color']};">{meta['label']}</div>
+                <div style="font-size: 0.7rem; color: #666;">{status_sym} {time_label}</div>
+            </div>
+            """)
+
+        pipeline_html = '<div style="display: flex; align-items: center; justify-content: center; gap: 0.3rem; flex-wrap: wrap; margin: 0.5rem 0;">'
+        for i, part in enumerate(pipeline_html_parts):
+            pipeline_html += part
+            if i < len(pipeline_html_parts) - 1:
+                pipeline_html += '<span style="font-size: 1.2rem; color: #999;">&#8594;</span>'
+        pipeline_html += "</div>"
+        st.markdown(pipeline_html, unsafe_allow_html=True)
+
+        # Agent reasoning traces (key feature for Agentic Workflow prize)
+        st.markdown("---")
+        st.subheader("Agent Reasoning Traces")
+
+        for trace in result.agent_traces:
+            meta = agent_meta.get(trace.agent_name, {"color": "#666", "bg": "#f5f5f5", "label": trace.agent_name})
+            status_emoji = {"success": "OK", "skipped": "SKIP", "error": "ERR"}.get(trace.status, "?")
+
+            header_label = f"{meta['label']} [{status_emoji}] - {trace.confidence:.0%} confidence - {trace.processing_time_ms:.0f}ms"
+            with st.expander(header_label, expanded=(trace.status == "success")):
+                # Status bar
+                st.markdown(f"""
+                <div style="background: {meta['bg']}; padding: 0.8rem 1rem; border-radius: 8px;
+                            border-left: 4px solid {meta['color']}; margin-bottom: 0.5rem;">
+                    <strong style="color: {meta['color']};">{trace.agent_name}</strong> &nbsp;|&nbsp;
+                    Status: <strong>{trace.status}</strong> &nbsp;|&nbsp;
+                    Confidence: <strong>{trace.confidence:.1%}</strong> &nbsp;|&nbsp;
+                    Time: <strong>{trace.processing_time_ms:.1f}ms</strong>
+                </div>
+                """, unsafe_allow_html=True)
+
+                # Reasoning steps with numbered styling
+                if trace.reasoning:
+                    st.markdown("**Reasoning Chain:**")
+                    for i, step in enumerate(trace.reasoning, 1):
+                        st.markdown(f"**Step {i}.** {step}")
+
+                # Key findings
+                if trace.findings:
+                    st.markdown("**Key Findings:**")
+                    st.json(trace.findings)
+
+        # Processing time breakdown
+        st.markdown("---")
+        col_chart, col_summary = st.columns([2, 1])
+
+        with col_chart:
+            st.subheader("Processing Time by Agent")
+            import pandas as pd
+            chart_data = pd.DataFrame({
+                "Agent": [agent_meta.get(t.agent_name, {}).get("label", t.agent_name) for t in result.agent_traces],
+                "Time (ms)": [t.processing_time_ms for t in result.agent_traces],
+            })
+            st.bar_chart(chart_data.set_index("Agent"))
+
+        with col_summary:
+            st.subheader("Workflow Summary")
+            total_time = result.processing_time_ms
+            successful = sum(1 for t in result.agent_traces if t.status == "success")
+            skipped = sum(1 for t in result.agent_traces if t.status == "skipped")
+            errors = sum(1 for t in result.agent_traces if t.status == "error")
+            st.markdown(f"""
+            | Metric | Value |
+            |--------|-------|
+            | Total agents | {len(result.agent_traces)} |
+            | Successful | {successful} |
+            | Skipped | {skipped} |
+            | Errors | {errors} |
+            | Total time | {total_time:.0f} ms |
+            | Avg per agent | {total_time / max(len(result.agent_traces), 1):.0f} ms |
+            """)
+
+        # Referral details
+        if result.referral_result and result.referral_result.referral_needed:
+            st.markdown("---")
+            st.subheader("Referral Details")
+            ref = result.referral_result
+            r1, r2, r3 = st.columns(3)
+            with r1:
+                st.metric("Urgency", ref.urgency.upper())
+            with r2:
+                st.metric("Facility", ref.facility_level.title())
+            with r3:
+                st.metric("Timeframe", ref.timeframe)
+            st.warning(f"Reason: {ref.reason}")
+
+
+# Footer
+def render_footer():
+    """Render footer."""
+    st.markdown("---")
+    st.markdown("""
+    <div style="text-align: center; color: #666; font-size: 0.9rem;">
+        <p>NEXUS - Built with Google HAI-DEF for MedGemma Impact Challenge 2026</p>
+        <p>⚠️ This is a screening tool only. Always confirm with laboratory tests.</p>
+    </div>
+    """, unsafe_allow_html=True)
+
+
+if __name__ == "__main__":
+    main()
+    render_footer()