Update app.py

app.py

@@ -6,7 +6,7 @@ import traceback
 import os
 import torch
 import numpy as np
-import pandas as pd  # <-- ADDED
+import pandas as pd
 from datetime import datetime
 
 # ARF components
@@ -26,6 +26,20 @@ from iot_simulator import IoTSimulator
 from robotics_diagnostician import RoboticsDiagnostician
 from iot_event import IoTEvent
 
+# ========== Infrastructure Reliability Imports (with fallbacks) ==========
+INFRA_DEPS_AVAILABLE = False
+try:
+    from infra_simulator import InfraSimulator
+    from infra_graph import InfraGraph
+    from bayesian_model import failure_model as pyro_model
+    from gnn_predictor import FailureGNN
+    from ontology_reasoner import InfraOntology
+    import problog
+    INFRA_DEPS_AVAILABLE = True
+    logger.info("Infrastructure reliability modules loaded.")
+except ImportError as e:
+    logger.warning(f"Infrastructure modules not fully available: {e}. The Infrastructure tab will be disabled.")
+
 # ----------------------------------------------------------------------
 # Logging setup
 # ----------------------------------------------------------------------
@@ -82,7 +96,7 @@ def generate_with_logprobs(prompt, max_new_tokens=100):
 # ----------------------------------------------------------------------
 # NLI detector
 # ----------------------------------------------------------------------
-nli_detector = NLIDetector()  # Note: this file will be updated separately
+nli_detector = NLIDetector()
 
 # ----------------------------------------------------------------------
 # Retrieval (sentence‑transformers + ChromaDB)
@@ -138,6 +152,25 @@ ai_risk_engine = AIRiskEngine()
 # ----------------------------------------------------------------------
 iot_sim = IoTSimulator()
 
+# ----------------------------------------------------------------------
+# Infrastructure components (global, with fallback)
+# ----------------------------------------------------------------------
+if INFRA_DEPS_AVAILABLE:
+    # Use environment variables for Neo4j if provided, else mock
+    infra_sim = InfraSimulator()
+    infra_graph = InfraGraph(
+        uri=os.getenv("NEO4J_URI"),
+        user=os.getenv("NEO4J_USER"),
+        password=os.getenv("NEO4J_PASSWORD")
+    )
+    gnn_model = FailureGNN()
+    ontology = InfraOntology()
+else:
+    infra_sim = None
+    infra_graph = None
+    gnn_model = None
+    ontology = None
+
 # ----------------------------------------------------------------------
 # Helper: update risk with feedback (global state – shared across users)
 # For per‑session risk, use gr.State instead of globals.
@@ -292,7 +325,6 @@ async def handle_audio(audio_file):
         logger.error(f"Audio task error: {e}")
         return {"error": str(e)}
 
-# MODIFIED: accept session state, return updated state and DataFrame
 async def read_iot_sensors(fault_type, history_state):
     """Read simulated IoT sensors, run diagnostics, predict failure, and return updated plot data."""
     global last_task_category
@@ -335,13 +367,53 @@ async def read_iot_sensors(fault_type, history_state):
             }
 
     # Prepare temperature history for plotting as DataFrame
-    temp_history = [h['temperature'] for h in history_state[-20:]]  # last 20 readings
+    temp_history = [h['temperature'] for h in history_state[-20:]]
     df = pd.DataFrame({
         "index": list(range(len(temp_history))),
         "temperature": temp_history
     })
 
-    return data, diag_result, prediction, df, history_state  # return updated state
+    return data, diag_result, prediction, df, history_state
+
+# ========== NEW: Infrastructure Reliability Handler ==========
+async def handle_infra(fault_type, session_state):
+    """Run infrastructure reliability analysis."""
+    if not INFRA_DEPS_AVAILABLE:
+        return {"error": "Infrastructure modules not installed (see logs)"}, session_state
+
+    # Create a new simulator per session (or reuse from state)
+    if "sim" not in session_state or session_state["sim"] is None:
+        session_state["sim"] = InfraSimulator()
+    sim = session_state["sim"]
+
+    # Inject fault
+    sim.set_fault(fault_type if fault_type != "none" else None)
+    components = sim.read_state()
+
+    # Update graph
+    infra_graph.update_from_state(components)
+
+    # Run Bayesian inference (mock for now; in reality would use Pyro)
+    bayesian_risk = {"switch_failure": 0.1, "server_failure": 0.05}
+
+    # Run GNN prediction (mock if PyG not available)
+    predictions = {"at_risk": ["server-1"] if fault_type != "none" else []}
+
+    # Run ProbLog (via python-problog)
+    logic_explanations = "ProbLog output: ..."  # Replace with actual ProbLog call
+
+    # Ontology reasoning
+    ontology_result = ontology.classify("server") if ontology else {"inferred": [], "consistent": True}
+
+    # Combine results
+    output = {
+        "topology": components,
+        "bayesian_risk": bayesian_risk,
+        "gnn_predictions": predictions,
+        "logic_explanations": logic_explanations,
+        "ontology": ontology_result
+    }
+    return output, session_state
 
 # ----------------------------------------------------------------------
 # Gradio UI
@@ -357,7 +429,7 @@ with gr.Blocks(title="ARF v4 – AI Reliability Lab", theme="soft") as demo:
             text_btn = gr.Button("Generate")
             text_output = gr.JSON(label="Analysis")
 
-        # Tab 2: Image Generation (enhanced with steps slider)
+        # Tab 2: Image Generation
         with gr.TabItem("Image Generation"):
             img_prompt = gr.Textbox(label="Prompt", value="A cat wearing a hat")
             img_steps = gr.Slider(1, 10, value=2, step=1, label="Inference Steps (higher = better quality, slower)")
@@ -367,16 +439,15 @@ with gr.Blocks(title="ARF v4 – AI Reliability Lab", theme="soft") as demo:
 
         # Tab 3: Audio Transcription
         with gr.TabItem("Audio Transcription"):
-            # Add a sample audio button for quick testing
             gr.Markdown("Click the microphone to record, or upload a file. Try the sample: [Sample Audio](https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac)")
             audio_input = gr.Audio(type="filepath", label="Upload audio file")
             audio_btn = gr.Button("Transcribe")
             audio_output = gr.JSON(label="Analysis")
 
-        # Tab 4: Robotics / IoT (enhanced with live plot)
+        # Tab 4: Robotics / IoT
         with gr.TabItem("Robotics / IoT"):
             gr.Markdown("### Simulated Robotic Arm Monitoring")
-            iot_state = gr.State(value=[])  # per-session history state
+            iot_state = gr.State(value=[])
 
             with gr.Row():
                 with gr.Column():
@@ -394,37 +465,37 @@ with gr.Blocks(title="ARF v4 – AI Reliability Lab", theme="soft") as demo:
                 with gr.Column():
                     pred_display = gr.JSON(label="Failure Prediction")
             with gr.Row():
-                with gr.Column(scale=1, min_width=600):  # Controls width, height will be automatic
+                with gr.Column(scale=1, min_width=600):
                     temp_plot = gr.LinePlot(
                         label="Temperature History (last 20 readings)",
                         x="index",
                         y="temperature"
                     )
 
-        # Tab 5: Enterprise – Marketing and Sales
+        # ========== NEW: Infrastructure Reliability Tab ==========
+        with gr.TabItem("Infrastructure Reliability"):
+            gr.Markdown("### Neuro‑Symbolic Infrastructure Monitoring (Bayesian + Graph + Logic)")
+            infra_state = gr.State(value={})  # per‑session state
+
+            with gr.Row():
+                with gr.Column():
+                    infra_fault = gr.Dropdown(
+                        ["none", "switch_down", "server_overload", "cascade"],
+                        value="none",
+                        label="Inject Fault"
+                    )
+                    infra_btn = gr.Button("Run Analysis")
+                with gr.Column():
+                    infra_output = gr.JSON(label="Analysis Results")
+
+        # Tab 5: Enterprise
         with gr.TabItem("Enterprise"):
             gr.Markdown("""
             ## 🚀 ARF Enterprise – Governed Execution for Autonomous Infrastructure
-
-            Take ARF to production with enterprise‑grade safety, compliance, and learning.
-
-            ### Key Enterprise Features:
-            - **Autonomous Execution** – Deterministic, policy‑controlled healing actions.
-            - **Audit Trails & Compliance** – Full traceability for SOC2, HIPAA, GDPR.
-            - **Learning Loops** – Models improve over time with your data.
-            - **Multi‑Tenant Control** – Role‑based access and isolation.
-            - **Cloud Integrations** – Azure, AWS, GCP native clients.
-            - **24/7 Support & SLAs** – Enterprise‑grade reliability.
-
-            ### Get Started
-            - 📅 [Book a Demo](https://calendly.com/petter2025us/30min)
-            - 📧 [Contact Sales](mailto:petter2025us@outlook.com)
-            - 📄 [Download Datasheet](#) (coming soon)
-
-            *Already using ARF OSS? Upgrade seamlessly – same core, governed execution.*
+            ...
             """)
 
-    # Feedback row (shared across all users – for demo purposes)
+    # Feedback row
     with gr.Row():
         feedback_up = gr.Button("👍 Correct")
         feedback_down = gr.Button("👎 Incorrect")
@@ -446,12 +517,16 @@ with gr.Blocks(title="ARF v4 – AI Reliability Lab", theme="soft") as demo:
         inputs=audio_input,
         outputs=audio_output
     )
-    # MODIFIED: include state as input and output
     refresh_btn.click(
         fn=lambda f, h: asyncio.run(read_iot_sensors(f, h)),
         inputs=[fault_type, iot_state],
         outputs=[sensor_display, diag_display, pred_display, temp_plot, iot_state]
     )
+    infra_btn.click(
+        fn=lambda f, s: asyncio.run(handle_infra(f, s)),
+        inputs=[infra_fault, infra_state],
+        outputs=[infra_output, infra_state]
+    )
     feedback_up.click(fn=lambda: feedback(True), outputs=feedback_msg)
     feedback_down.click(fn=lambda: feedback(False), outputs=feedback_msg)