Create app.py

app.py

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+import gradio as gr
+import json
+from datetime import datetime
+
+# Simulated knowledge graph data
+SAMPLE_NODES = {
+    "SARS-CoV-2": {
+        "type": "Virus",
+        "genome_size": 29.9,
+        "family": "Coronaviridae",
+        "metadata": {"discovered": "2019", "origin": "Wuhan, China"}
+    },
+    "Spike Protein": {
+        "type": "Protein",
+        "function": "Viral entry",
+        "receptor": "ACE2",
+        "metadata": {"key_mutations": ["D614G", "N501Y", "E484K"]}
+    },
+    "ACE2": {
+        "type": "Receptor",
+        "location": "Cell membrane",
+        "function": "Viral entry receptor",
+        "metadata": {"tissue_expression": ["Lung", "Heart", "Kidney"]}
+    },
+    "Omicron": {
+        "type": "Variant",
+        "lineage": "BA.1",
+        "mutations": ["30+ spike mutations"],
+        "metadata": {"transmissibility": "High", "severity": "Lower"}
+    },
+    "mRNA Vaccine": {
+        "type": "Therapy",
+        "mechanism": "Induced immunity",
+        "efficacy": "~95% (original strain)",
+        "metadata": {"examples": ["Pfizer-BioNTech", "Moderna"]}
+    }
+}
+
+SAMPLE_EDGES = [
+    {"from": "SARS-CoV-2", "to": "Spike Protein", "relationship": "encodes", "confidence": 1.0},
+    {"from": "Spike Protein", "to": "ACE2", "relationship": "binds_to", "confidence": 0.95},
+    {"from": "SARS-CoV-2", "to": "Omicron", "relationship": "evolves_to", "confidence": 0.90},
+    {"from": "mRNA Vaccine", "to": "Spike Protein", "relationship": "targets", "confidence": 0.98},
+]
+
+INTENT_TYPES = ["Factual", "Causal", "Comparative", "Predictive", "Exploratory"]
+
+# Query processing simulation
+def process_query(query_text, intent_type, use_quantum):
+    """Process a query and return results"""
+    results = {
+        "query": query_text,
+        "intent": intent_type,
+        "quantum_optimized": use_quantum,
+        "timestamp": datetime.now().isoformat(),
+        "nodes_searched": 0,
+        "edges_traversed": 0,
+        "results": []
+    }
+    
+    # Simple keyword matching simulation
+    query_lower = query_text.lower()
+    for node_name, node_data in SAMPLE_NODES.items():
+        if any(keyword in query_lower for keyword in node_name.lower().split()):
+            results["results"].append({
+                "node": node_name,
+                "data": node_data,
+                "relevance": 0.85 + (0.1 if use_quantum else 0)
+            })
+            results["nodes_searched"] += 1
+    
+    # Add related edges
+    for edge in SAMPLE_EDGES:
+        if any(node in [r["node"] for r in results["results"]] 
+               for node in [edge["from"], edge["to"]]):
+            results["edges_traversed"] += 1
+    
+    # Quantum optimization bonus
+    if use_quantum and results["results"]:
+        results["optimization"] = {
+            "rate": 0.8,
+            "distortion": 0.2,
+            "method": "Quantum-inspired sampling"
+        }
+    
+    return results
+
+def query_interface(query_text, intent_type, use_quantum):
+    """Main query interface"""
+    if not query_text.strip():
+        return "Please enter a query.", ""
+    
+    results = process_query(query_text, intent_type, use_quantum)
+    
+    # Format output
+    output = f"## Query Results\n\n"
+    output += f"**Query:** {results['query']}\n\n"
+    output += f"**Intent Type:** {results['intent']}\n\n"
+    output += f"**Quantum Optimization:** {'Enabled ⚡' if use_quantum else 'Disabled'}\n\n"
+    output += f"**Nodes Searched:** {results['nodes_searched']}\n\n"
+    output += f"**Edges Traversed:** {results['edges_traversed']}\n\n"
+    
+    if results["results"]:
+        output += "### Found Nodes:\n\n"
+        for r in results["results"]:
+            output += f"**{r['node']}** (Relevance: {r['relevance']:.2f})\n"
+            output += f"- Type: {r['data']['type']}\n"
+            for key, value in r['data'].items():
+                if key not in ['type', 'metadata']:
+                    output += f"- {key.replace('_', ' ').title()}: {value}\n"
+            output += "\n"
+    else:
+        output += "No nodes found matching your query.\n\n"
+    
+    if use_quantum and "optimization" in results:
+        output += "### Quantum Optimization\n\n"
+        output += f"- Rate: {results['optimization']['rate']}\n"
+        output += f"- Distortion: {results['optimization']['distortion']}\n"
+        output += f"- Method: {results['optimization']['method']}\n\n"
+    
+    # JSON output
+    json_output = json.dumps(results, indent=2)
+    
+    return output, json_output
+
+def browse_graph():
+    """Browse all nodes in the graph"""
+    output = "# Knowledge Graph Nodes\n\n"
+    for node_name, node_data in SAMPLE_NODES.items():
+        output += f"## {node_name}\n\n"
+        output += f"**Type:** {node_data['type']}\n\n"
+        for key, value in node_data.items():
+            if key not in ['type', 'metadata']:
+                output += f"- **{key.replace('_', ' ').title()}:** {value}\n"
+        if 'metadata' in node_data:
+            output += f"\n**Metadata:**\n"
+            for k, v in node_data['metadata'].items():
+                output += f"- {k.replace('_', ' ').title()}: {v}\n"
+        output += "\n---\n\n"
+    
+    output += "# Knowledge Graph Edges\n\n"
+    for edge in SAMPLE_EDGES:
+        output += f"- **{edge['from']}** → {edge['relationship']} → **{edge['to']}** "
+        output += f"(Confidence: {edge['confidence']})\n"
+    
+    return output
+
+def show_architecture():
+    """Show system architecture"""
+    arch = """# SARS-CoV-2 Knowledge Graph Architecture
+
+## System Components
+
+### Stage 1: Biomedical Graph (limit-bio-sars)
+- **Node Types:** Virus, Protein, Receptor, Variant, Therapy
+- **Features:** Metadata tracking, Provenance, Confidence scoring
+- **Operations:** O(1) node addition, O(E) edge queries
+
+### Stage 2: Multi-Intent Harness (limit-benchmark)
+- **Intent Types:** Factual, Causal, Comparative, Predictive, Exploratory
+- **Performance:** ~1000 queries/second
+- **Metrics:** Graph and query performance tracking
+
+### Stage 3: Quantum-Inspired Retrieval (limit-quantum)
+- **Algorithms:** Rate-Distortion optimization, Quantum sampling
+- **Features:** Quantum annealing, Quantum walk simulation
+- **Benefits:** Optimal retrieval strategies
+
+### Stage 4: Open-Source Hub (limit-hub)
+- **API:** REST endpoints with Axum framework
+- **Governance:** Validation rules, Quality control
+- **Latency:** <50ms per request
+
+### Stage 5: Testing
+- **Coverage:** Unit, Integration, Performance tests
+- **Validation:** Automated quality checks
+
+## Technical Stack
+- **Language:** Rust
+- **Dependencies:** serde, axum, tokio, uuid, rand
+- **Performance:** ~100MB memory for 10K nodes
+- **License:** MIT
+
+## Data Flow
+
+## Source Code
+Full implementation available at:
+https://github.com/NurcholishAdam/SARS-CoV-2-KG
+"""
+    return arch
+
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Soft(), title="SARS-CoV-2 Knowledge Graph") as demo:
+    gr.Markdown("""
+    # 🦠 SARS-CoV-2 Extended Knowledge Graph
+    
+    An interactive biomedical knowledge graph with quantum-inspired retrieval capabilities.
+    Explore viral entities, proteins, variants, and therapies with multi-intent querying.
+    
+    **Version:** 2.4.1 | **Source:** [GitHub](https://github.com/NurcholishAdam/SARS-CoV-2-KG)
+    """)
+    
+    with gr.Tabs():
+        # Query Tab
+        with gr.Tab("🔍 Query"):
+            gr.Markdown("### Search the Knowledge Graph")
+            with gr.Row():
+                with gr.Column(scale=2):
+                    query_input = gr.Textbox(
+                        label="Enter your query",
+                        placeholder="e.g., What is the spike protein? How does Omicron differ?",
+                        lines=2
+                    )
+                    with gr.Row():
+                        intent_dropdown = gr.Dropdown(
+                            choices=INTENT_TYPES,
+                            value="Factual",
+                            label="Query Intent Type"
+                        )
+                        quantum_checkbox = gr.Checkbox(
+                            label="Enable Quantum Optimization ⚡",
+                            value=False
+                        )
+                    submit_btn = gr.Button("Search", variant="primary")
+                
+                with gr.Column(scale=2):
+                    output_md = gr.Markdown(label="Results")
+            
+            with gr.Accordion("View JSON Response", open=False):
+                json_output = gr.Code(language="json", label="JSON Output")
+            
+            submit_btn.click(
+                fn=query_interface,
+                inputs=[query_input, intent_dropdown, quantum_checkbox],
+                outputs=[output_md, json_output]
+            )
+            
+            gr.Examples(
+                examples=[
+                    ["What is the spike protein?", "Factual", False],
+                    ["How does the spike protein bind to ACE2?", "Causal", True],
+                    ["Compare Omicron to the original strain", "Comparative", True],
+                    ["What therapies target the spike protein?", "Exploratory", False],
+                ],
+                inputs=[query_input, intent_dropdown, quantum_checkbox]
+            )
+        
+        # Browse Tab
+        with gr.Tab("📊 Browse Graph"):
+            gr.Markdown("### Explore All Nodes and Edges")
+            browse_btn = gr.Button("Load Knowledge Graph", variant="primary")
+            browse_output = gr.Markdown()
+            browse_btn.click(fn=browse_graph, outputs=browse_output)
+        
+        # Architecture Tab
+        with gr.Tab("🏗️ Architecture"):
+            gr.Markdown("### System Architecture Overview")
+            arch_btn = gr.Button("Show Architecture", variant="primary")
+            arch_output = gr.Markdown()
+            arch_btn.click(fn=show_architecture, outputs=arch_output)
+        
+        # About Tab
+        with gr.Tab("ℹ️ About"):
+            gr.Markdown("""
+            ## About This Project
+            
+            This is a demonstration interface for the SARS-CoV-2 Extended Knowledge Graph,
+            a comprehensive biomedical information system with quantum-inspired retrieval.
+            
+            ### Key Features
+            
+            - **Enriched Biomedical Graph:** Comprehensive node types with metadata
+            - **Multi-Intent Queries:** Support for 5 query types
+            - **Quantum-Inspired Retrieval:** Advanced optimization algorithms
+            - **Open-Source:** MIT licensed, community contributions welcome
+            
+            ### Intent Types
+            
+            - **Factual:** Direct information retrieval
+            - **Causal:** Understanding relationships and mechanisms
+            - **Comparative:** Comparing entities or concepts
+            - **Predictive:** Forward-looking analysis
+            - **Exploratory:** Open-ended discovery
+            
+            ### Performance
+            
+            - Query Throughput: ~1000 queries/second
+            - API Latency: <50ms
+            - Memory: ~100MB for 10K nodes
+            
+            ### Links
+            
+            - **GitHub:** [NurcholishAdam/SARS-CoV-2-KG](https://github.com/NurcholishAdam/SARS-CoV-2-KG)
+            - **License:** MIT
+            - **Version:** 2.4.1
+            
+            ### Citation
+            
+            If you use this work, please cite:
+            @software{sarscov2_kg_2024,
+          title={SARS-CoV-2 Extended Knowledge Graph},
+          author={NurcholishAdam},
+          year={2024},
+          url={https://github.com/NurcholishAdam/SARS-CoV-2-KG}
+        }
+        """)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()