Update app.py

app.py

@@ -199,8 +199,8 @@ async def handle_text(task_type, prompt):
         logger.error(f"Text task error: {e}")
         return {"error": str(e)}
 
-async def handle_image(prompt):
-    """Handle image generation and quality analysis. Returns (image, json_data)."""
+async def handle_image(prompt, steps):
+    """Handle image generation with configurable steps. Returns (image, json_data)."""
     global last_task_category
     last_task_category = "image"
     if image_pipe is None:
@@ -208,7 +208,7 @@ async def handle_image(prompt):
     try:
         import time
         start = time.time()
-        image = image_pipe(prompt, num_inference_steps=2).images[0]  # minimal steps
+        image = image_pipe(prompt, num_inference_steps=steps).images[0]
         gen_time = time.time() - start
         retrieval_score = retriever.get_similarity(prompt)
         event = AIEvent(
@@ -293,7 +293,7 @@ async def handle_audio(audio_file):
         return {"error": str(e)}
 
 async def read_iot_sensors(fault_type):
-    """Read simulated IoT sensors, run diagnostics, and predict failure."""
+    """Read simulated IoT sensors, run diagnostics, predict failure, and return updated plot data."""
     global last_task_category, iot_history
     last_task_category = "iot"
     iot_sim.set_fault(fault_type if fault_type != "none" else None)
@@ -302,10 +302,10 @@ async def read_iot_sensors(fault_type):
     if len(iot_history) > 100:
         iot_history.pop(0)
 
-    # Create IoTEvent with valid component name (hyphens allowed, underscores not allowed)
+    # Create IoTEvent with valid component name
     event = IoTEvent(
         timestamp=datetime.utcnow(),
-        component="robotic-arm",  # changed from 'robotic_arm'
+        component="robotic-arm",
         service_mesh="factory",
         latency_p99=0,
         error_rate=0.0,
@@ -317,10 +317,9 @@ async def read_iot_sensors(fault_type):
         motor_current=data['motor_current'],
         position_error=data['position_error']
     )
-    # Run diagnostician
     diag_result = await robotics_diagnostician.analyze(event)
 
-    # Simple failure prediction (linear extrapolation)
+    # Simple failure prediction
     prediction = None
     if len(iot_history) >= 5:
         temps = [h['temperature'] for h in iot_history[-5:]]
@@ -334,7 +333,9 @@ async def read_iot_sensors(fault_type):
                 "time_to_overheat_min": time_to_threshold
             }
 
-    return data, diag_result, prediction
+    # Prepare temperature history for plotting
+    temp_history = [h['temperature'] for h in iot_history[-20:]]  # last 20 readings
+    return data, diag_result, prediction, temp_history
 
 # ----------------------------------------------------------------------
 # Gradio UI
@@ -350,31 +351,71 @@ 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
+        # Tab 2: Image Generation (enhanced with steps slider)
         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)")
             img_btn = gr.Button("Generate")
             img_output = gr.Image(label="Generated Image")
             img_json = gr.JSON(label="Analysis")
 
         # 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
+        # Tab 4: Robotics / IoT (enhanced with live plot)
         with gr.TabItem("Robotics / IoT"):
             gr.Markdown("### Simulated Robotic Arm Monitoring")
-            fault_type = gr.Dropdown(
-                ["none", "overheat", "vibration", "stall", "drift"],
-                value="none",
-                label="Inject Fault"
-            )
-            refresh_btn = gr.Button("Read Sensors")
-            sensor_display = gr.JSON(label="Sensor Readings")
-            diag_display = gr.JSON(label="Diagnosis")
-            pred_display = gr.JSON(label="Failure Prediction")
+            with gr.Row():
+                with gr.Column():
+                    fault_type = gr.Dropdown(
+                        ["none", "overheat", "vibration", "stall", "drift"],
+                        value="none",
+                        label="Inject Fault"
+                    )
+                    refresh_btn = gr.Button("Read Sensors")
+                with gr.Column():
+                    sensor_display = gr.JSON(label="Sensor Readings")
+            with gr.Row():
+                with gr.Column():
+                    diag_display = gr.JSON(label="Diagnosis")
+                with gr.Column():
+                    pred_display = gr.JSON(label="Failure Prediction")
+            with gr.Row():
+                temp_plot = gr.LinePlot(
+                    label="Temperature History (last 20 readings)",
+                    x="index",
+                    y="temperature",
+                    width=600,
+                    height=300
+                )
+
+        # Tab 5: Enterprise – Marketing and Sales
+        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)
     with gr.Row():
@@ -389,8 +430,8 @@ with gr.Blocks(title="ARF v4 – AI Reliability Lab", theme="soft") as demo:
         outputs=text_output
     )
     img_btn.click(
-        fn=lambda p: asyncio.run(handle_image(p)),
-        inputs=img_prompt,
+        fn=lambda p, s: asyncio.run(handle_image(p, s)),
+        inputs=[img_prompt, img_steps],
         outputs=[img_output, img_json]
     )
     audio_btn.click(
@@ -401,7 +442,7 @@ with gr.Blocks(title="ARF v4 – AI Reliability Lab", theme="soft") as demo:
     refresh_btn.click(
         fn=lambda f: asyncio.run(read_iot_sensors(f)),
         inputs=fault_type,
-        outputs=[sensor_display, diag_display, pred_display]
+        outputs=[sensor_display, diag_display, pred_display, temp_plot]
     )
     feedback_up.click(fn=lambda: feedback(True), outputs=feedback_msg)
     feedback_down.click(fn=lambda: feedback(False), outputs=feedback_msg)