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app.py

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+"""
+RadioFlow: AI-Powered Radiology Workflow Agent
+Main Gradio Application for MedGemma Impact Challenge
+
+This application demonstrates a multi-agent system for chest X-ray analysis
+using Google's Health AI Developer Foundations (HAI-DEF) models.
+"""
+
+import gradio as gr
+from PIL import Image
+import time
+from typing import Optional, Tuple, List, Dict
+import json
+
+# Import our modules
+from orchestrator import RadioFlowOrchestrator, WorkflowResult, create_orchestrator
+from utils.visualization import (
+    create_workflow_diagram,
+    create_radar_chart,
+    create_priority_gauge,
+    create_timeline_chart
+)
+
+# Global orchestrator instance
+orchestrator: Optional[RadioFlowOrchestrator] = None
+
+
+def initialize_system():
+    """Initialize the RadioFlow system."""
+    global orchestrator
+    if orchestrator is None:
+        orchestrator = create_orchestrator(demo_mode=True)
+    return "✅ RadioFlow System Initialized"
+
+
+def process_xray(
+    image: Optional[Image.Image],
+    clinical_history: str,
+    patient_age: str,
+    symptoms: str,
+    progress=gr.Progress()
+) -> Tuple[str, str, str, str, str, dict, dict, dict]:
+    """
+    Process a chest X-ray through the RadioFlow pipeline.
+    
+    Returns:
+        Tuple of (report, priority_html, findings_json, metrics, status, 
+                  workflow_fig, radar_fig, priority_fig)
+    """
+    global orchestrator
+    
+    if image is None:
+        return (
+            "⚠️ Please upload a chest X-ray image.",
+            "",
+            "{}",
+            "",
+            "No image uploaded",
+            None, None, None
+        )
+    
+    # Initialize if needed
+    if orchestrator is None:
+        initialize_system()
+    
+    # Prepare clinical context
+    context = {
+        "clinical_history": clinical_history or "Not provided",
+        "age": patient_age or "Not provided",
+        "symptoms": symptoms or "Not provided"
+    }
+    
+    # Progress updates
+    progress(0.1, desc="🔬 Analyzing chest X-ray...")
+    time.sleep(0.2)
+    
+    progress(0.3, desc="📋 Interpreting findings...")
+    
+    # Run the workflow
+    result = orchestrator.process(image, context)
+    
+    progress(0.6, desc="📝 Generating report...")
+    time.sleep(0.1)
+    
+    progress(0.8, desc="🚦 Assessing priority...")
+    time.sleep(0.1)
+    
+    progress(1.0, desc="✅ Complete!")
+    
+    # Format outputs
+    report = result.final_report if result.final_report else "Report generation failed."
+    
+    # Priority HTML
+    priority_html = format_priority_display(result)
+    
+    # Findings JSON
+    findings = []
+    if result.cxr_analysis and result.cxr_analysis.data:
+        findings = result.cxr_analysis.data.get("findings", [])
+    findings_json = json.dumps(findings, indent=2)
+    
+    # Metrics
+    metrics = format_metrics(result)
+    
+    # Status
+    status = f"✅ Workflow {result.status.upper()} | {result.total_duration_ms:.0f}ms"
+    
+    # Create visualizations
+    agent_results = []
+    for agent_result in [result.cxr_analysis, result.finding_interpretation, 
+                         result.report, result.priority_routing]:
+        if agent_result:
+            agent_results.append({
+                "name": agent_result.agent_name,
+                "status": agent_result.status,
+                "processing_time_ms": agent_result.processing_time_ms
+            })
+    
+    workflow_fig = create_workflow_diagram(agent_results)
+    
+    # Radar chart for analysis scores
+    if result.cxr_analysis and result.cxr_analysis.data:
+        region_analysis = result.cxr_analysis.data.get("region_analysis", {})
+        scores = {}
+        for region, data in list(region_analysis.items())[:5]:
+            clean_name = region.replace("_", " ").title()
+            scores[clean_name] = data.get("confidence", 0.5)
+        if scores:
+            radar_fig = create_radar_chart(scores, "Regional Confidence Scores")
+        else:
+            radar_fig = None
+    else:
+        radar_fig = None
+    
+    # Priority gauge
+    priority_fig = create_priority_gauge(result.priority_score, result.priority_level)
+    
+    return (
+        report,
+        priority_html,
+        findings_json,
+        metrics,
+        status,
+        workflow_fig,
+        radar_fig,
+        priority_fig
+    )
+
+
+def format_priority_display(result: WorkflowResult) -> str:
+    """Format priority information as HTML."""
+    level = result.priority_level
+    score = result.priority_score
+    
+    colors = {
+        "STAT": "#ef4444",
+        "URGENT": "#f59e0b",
+        "ROUTINE": "#22c55e"
+    }
+    color = colors.get(level, "#6b7280")
+    
+    critical_html = ""
+    if result.critical_findings:
+        critical_html = f"""
+        <div style="margin-top: 10px; padding: 10px; background: #fef2f2; border-radius: 5px; border-left: 4px solid #ef4444;">
+            <strong>⚠️ Critical Findings:</strong>
+            <ul style="margin: 5px 0 0 20px;">
+                {"".join(f"<li>{f}</li>" for f in result.critical_findings)}
+            </ul>
+        </div>
+        """
+    
+    routing_html = ""
+    if result.priority_routing and result.priority_routing.data:
+        routing = result.priority_routing.data.get("routing_recommendation", {})
+        if routing:
+            routing_html = f"""
+            <div style="margin-top: 10px; padding: 10px; background: #f0f9ff; border-radius: 5px;">
+                <strong>📍 Routing:</strong> {routing.get("destination", "Standard Queue")}
+            </div>
+            """
+    
+    return f"""
+    <div style="padding: 15px; border-radius: 10px; background: linear-gradient(135deg, {color}22, {color}11);">
+        <div style="display: flex; align-items: center; gap: 15px;">
+            <div style="
+                width: 60px; 
+                height: 60px; 
+                background: {color}; 
+                border-radius: 50%; 
+                display: flex; 
+                align-items: center; 
+                justify-content: center;
+                color: white;
+                font-weight: bold;
+                font-size: 14px;
+            ">
+                {level}
+            </div>
+            <div>
+                <div style="font-size: 24px; font-weight: bold; color: {color};">
+                    Priority Score: {score:.0%}
+                </div>
+                <div style="color: #666; font-size: 14px;">
+                    {result.findings_count} finding(s) detected
+                </div>
+            </div>
+        </div>
+        {critical_html}
+        {routing_html}
+    </div>
+    """
+
+
+def format_metrics(result: WorkflowResult) -> str:
+    """Format workflow metrics."""
+    lines = [
+        "## 📊 Workflow Metrics",
+        "",
+        f"**Total Duration:** {result.total_duration_ms:.0f}ms",
+        f"**Status:** {result.status.upper()}",
+        f"**Findings Detected:** {result.findings_count}",
+        "",
+        "### Agent Performance",
+        ""
+    ]
+    
+    agents = [
+        ("CXR Analyzer", result.cxr_analysis),
+        ("Finding Interpreter", result.finding_interpretation),
+        ("Report Generator", result.report),
+        ("Priority Router", result.priority_routing)
+    ]
+    
+    for name, agent_result in agents:
+        if agent_result:
+            status_icon = "✅" if agent_result.status == "success" else "❌"
+            lines.append(f"- {status_icon} **{name}:** {agent_result.processing_time_ms:.0f}ms")
+    
+    return "\n".join(lines)
+
+
+def get_sample_image():
+    """Return a sample X-ray image for demo purposes."""
+    # Create a simple placeholder image
+    img = Image.new('RGB', (512, 512), color=(20, 20, 30))
+    from PIL import ImageDraw
+    draw = ImageDraw.Draw(img)
+    
+    # Draw a simple chest outline
+    draw.ellipse([100, 150, 412, 450], outline=(60, 60, 70), width=3)
+    draw.ellipse([150, 180, 280, 350], outline=(50, 50, 60), width=2)
+    draw.ellipse([232, 180, 362, 350], outline=(50, 50, 60), width=2)
+    
+    # Add text
+    draw.text((150, 50), "Sample CXR", fill=(80, 80, 90))
+    draw.text((120, 470), "Upload real X-ray for analysis", fill=(80, 80, 90))
+    
+    return img
+
+
+# ============================================
+# GRADIO INTERFACE
+# ============================================
+
+# Custom CSS for professional styling
+custom_css = """
+/* Main container */
+.gradio-container {
+    max-width: 1400px !important;
+    margin: auto;
+}
+
+/* Header styling */
+.header-container {
+    background: linear-gradient(135deg, #1e3a5f 0%, #2d5a87 100%);
+    padding: 20px;
+    border-radius: 10px;
+    margin-bottom: 20px;
+    color: white;
+}
+
+/* Agent cards */
+.agent-card {
+    background: white;
+    border-radius: 8px;
+    padding: 15px;
+    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    margin: 5px;
+}
+
+/* Priority badges */
+.priority-stat { background: #ef4444; }
+.priority-urgent { background: #f59e0b; }
+.priority-routine { background: #22c55e; }
+
+/* Code blocks */
+pre {
+    background: #1e293b !important;
+    border-radius: 8px;
+}
+
+/* Tabs */
+.tab-nav button {
+    font-weight: 600 !important;
+}
+"""
+
+# Create the Gradio interface
+with gr.Blocks(
+    title="RadioFlow - AI Radiology Workflow"
+) as demo:
+    
+    # Header
+    gr.HTML("""
+    <div style="
+        background: linear-gradient(135deg, #1e3a5f 0%, #2d5a87 100%);
+        padding: 25px 30px;
+        border-radius: 12px;
+        margin-bottom: 20px;
+        color: white;
+    ">
+        <div style="display: flex; align-items: center; gap: 15px;">
+            <div style="font-size: 40px;">🩻</div>
+            <div>
+                <h1 style="margin: 0; font-size: 28px; font-weight: 700;">RadioFlow</h1>
+                <p style="margin: 5px 0 0 0; opacity: 0.9; font-size: 16px;">
+                    AI-Powered Radiology Workflow Agent | MedGemma Impact Challenge
+                </p>
+            </div>
+        </div>
+        <div style="
+            display: flex; 
+            gap: 20px; 
+            margin-top: 15px; 
+            padding-top: 15px; 
+            border-top: 1px solid rgba(255,255,255,0.2);
+        ">
+            <div>
+                <span style="opacity: 0.7;">Powered by</span>
+                <strong>MedGemma + CXR Foundation</strong>
+            </div>
+            <div>
+                <span style="opacity: 0.7;">Architecture</span>
+                <strong>4-Agent Pipeline</strong>
+            </div>
+            <div>
+                <span style="opacity: 0.7;">Prize Track</span>
+                <strong>Main + Agentic Workflow</strong>
+            </div>
+        </div>
+    </div>
+    """)
+    
+    # Agent Pipeline Visualization
+    gr.HTML("""
+    <div style="
+        background: #f8fafc; 
+        padding: 15px 20px; 
+        border-radius: 10px; 
+        margin-bottom: 20px;
+        border: 1px solid #e2e8f0;
+    ">
+        <div style="text-align: center; margin-bottom: 10px; font-weight: 600; color: #475569;">
+            Multi-Agent Pipeline
+        </div>
+        <div style="display: flex; justify-content: center; align-items: center; gap: 10px; flex-wrap: wrap;">
+            <div style="background: #3b82f6; color: white; padding: 10px 15px; border-radius: 8px; font-weight: 500;">
+                1️⃣ CXR Analyzer
+            </div>
+            <div style="color: #94a3b8;">→</div>
+            <div style="background: #8b5cf6; color: white; padding: 10px 15px; border-radius: 8px; font-weight: 500;">
+                2️⃣ Finding Interpreter
+            </div>
+            <div style="color: #94a3b8;">→</div>
+            <div style="background: #ec4899; color: white; padding: 10px 15px; border-radius: 8px; font-weight: 500;">
+                3️⃣ Report Generator
+            </div>
+            <div style="color: #94a3b8;">→</div>
+            <div style="background: #f59e0b; color: white; padding: 10px 15px; border-radius: 8px; font-weight: 500;">
+                4️⃣ Priority Router
+            </div>
+        </div>
+    </div>
+    """)
+    
+    with gr.Row():
+        # Left Column - Input
+        with gr.Column(scale=1):
+            gr.Markdown("### 📤 Input")
+            
+            image_input = gr.Image(
+                label="Chest X-Ray Image",
+                type="pil",
+                height=300
+            )
+            
+            with gr.Accordion("Clinical Context (Optional)", open=False):
+                clinical_history = gr.Textbox(
+                    label="Clinical History",
+                    placeholder="e.g., 65-year-old male with cough and fever for 3 days",
+                    lines=2
+                )
+                patient_age = gr.Textbox(
+                    label="Patient Age",
+                    placeholder="e.g., 65"
+                )
+                symptoms = gr.Textbox(
+                    label="Presenting Symptoms",
+                    placeholder="e.g., Cough, fever, shortness of breath",
+                    lines=2
+                )
+            
+            with gr.Row():
+                analyze_btn = gr.Button(
+                    "🔬 Analyze X-Ray",
+                    variant="primary",
+                    size="lg"
+                )
+                clear_btn = gr.Button("🗑️ Clear", size="lg")
+            
+            status_display = gr.Textbox(
+                label="Status",
+                interactive=False,
+                value="Ready to analyze"
+            )
+        
+        # Right Column - Output
+        with gr.Column(scale=2):
+            gr.Markdown("### 📊 Analysis Results")
+            
+            with gr.Tabs():
+                with gr.Tab("📋 Report"):
+                    priority_display = gr.HTML(label="Priority Assessment")
+                    report_output = gr.Textbox(
+                        label="Radiology Report",
+                        lines=20,
+                        max_lines=30,
+                        interactive=False
+                    )
+                
+                with gr.Tab("📈 Visualizations"):
+                    with gr.Row():
+                        workflow_plot = gr.Plot(label="Agent Pipeline Status")
+                        priority_plot = gr.Plot(label="Priority Gauge")
+                    radar_plot = gr.Plot(label="Analysis Confidence")
+                
+                with gr.Tab("🔍 Findings"):
+                    findings_output = gr.Code(
+                        label="Detected Findings (JSON)",
+                        language="json",
+                        lines=15
+                    )
+                
+                with gr.Tab("⚡ Metrics"):
+                    metrics_output = gr.Markdown()
+    
+    # Footer
+    gr.HTML("""
+    <div style="
+        margin-top: 30px;
+        padding: 20px;
+        background: #f1f5f9;
+        border-radius: 10px;
+        text-align: center;
+    ">
+        <div style="font-weight: 600; margin-bottom: 10px;">
+            🏆 MedGemma Impact Challenge Submission
+        </div>
+        <div style="color: #64748b; font-size: 14px;">
+            Built with Google HAI-DEF: MedGemma + CXR Foundation | 
+            Targeting: Main Track + Agentic Workflow Prize
+        </div>
+        <div style="margin-top: 10px; font-size: 12px; color: #94a3b8;">
+            ⚠️ For demonstration purposes only. Not for clinical use.
+            This AI system requires radiologist verification.
+        </div>
+    </div>
+    """)
+    
+    # Event handlers
+    analyze_btn.click(
+        fn=process_xray,
+        inputs=[image_input, clinical_history, patient_age, symptoms],
+        outputs=[
+            report_output,
+            priority_display,
+            findings_output,
+            metrics_output,
+            status_display,
+            workflow_plot,
+            radar_plot,
+            priority_plot
+        ]
+    )
+    
+    clear_btn.click(
+        fn=lambda: (None, "", "", "", "Ready to analyze", None, None, None, None),
+        outputs=[
+            image_input,
+            report_output,
+            priority_display,
+            findings_output,
+            status_display,
+            workflow_plot,
+            radar_plot,
+            priority_plot
+        ]
+    )
+    
+    # Initialize on load
+    demo.load(fn=initialize_system, outputs=[])
+
+
+# ============================================
+# MAIN ENTRY POINT
+# ============================================
+
+if __name__ == "__main__":
+    # Initialize the system
+    print("🚀 Starting RadioFlow...")
+    initialize_system()
+    
+    # Launch the demo
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True,
+        css=custom_css
+    )