Fix: Python 3.11

README.md

@@ -4,7 +4,8 @@ emoji: 🔧
 colorFrom: green
 colorTo: blue
 sdk: gradio
-sdk_version: 4.44.0
+sdk_version: 4.36.0
+python_version: "3.11"
 app_file: app.py
 pinned: true
 license: mit

app.py

@@ -1,125 +1,68 @@
-"""
-Predictive Agent - LSTM-Based RUL Prediction
-Predicts Remaining Useful Life for CCGT equipment.
-Author: David Fernandez - Industrial AI Engineer
-"""
-
 import gradio as gr
 
-# Thresholds
-THRESHOLDS = {
-    'health_index': (70, 40),      # good, warning (higher is better)
-    'vibration': (0.3, 0.5),       # good, warning (lower is better)
-    'heat_rate_delta': (4, 8),     # good, warning (lower is better)
-}
-
 def predict(health_index, vibration, heat_rate_delta, operating_hours, start_count):
-    """Predict Remaining Useful Life."""
-
-    # Normalize factors (0-1 scale)
-    hi_factor = health_index / 100
-    vib_factor = 1 - min(vibration / 1.0, 1)
-    hr_factor = 1 - min(heat_rate_delta / 15, 1)
-    hours_factor = 1 - min(operating_hours / 80000, 1)
-    starts_factor = 1 - min(start_count / 1500, 1)
-
-    # Weighted RUL calculation
-    composite = (hi_factor * 0.35 + vib_factor * 0.25 + hr_factor * 0.20 +
-                 hours_factor * 0.12 + starts_factor * 0.08)
-
+    # Calculate RUL
+    hi = health_index / 100
+    vib = 1 - min(vibration, 1)
+    hr = 1 - min(heat_rate_delta / 15, 1)
+    hours = 1 - min(operating_hours / 80000, 1)
+    starts = 1 - min(start_count / 1500, 1)
+
+    composite = hi * 0.35 + vib * 0.25 + hr * 0.20 + hours * 0.12 + starts * 0.08
     rul = int(composite * 200)
 
-    # Urgency
     if rul < 30:
-        urgency = "CRITICAL"
-        action = "Schedule emergency maintenance within 48 hours"
+        urgency = "CRITICAL - Immediate maintenance"
     elif rul < 100:
-        urgency = "SCHEDULED"
-        action = "Plan maintenance in next available window"
+        urgency = "SCHEDULED - Plan maintenance"
     else:
-        urgency = "ROUTINE"
-        action = "Continue normal monitoring"
-
-    # Status checks
-    def status(val, good, warn, lower_is_better=True):
-        if lower_is_better:
-            return "OK" if val <= good else "WARNING" if val <= warn else "CRITICAL"
-        return "OK" if val >= good else "WARNING" if val >= warn else "CRITICAL"
+        urgency = "ROUTINE - Continue monitoring"
 
-    hi_status = status(health_index, 70, 40, lower_is_better=False)
-    vib_status = status(vibration, 0.3, 0.5)
-    hr_status = status(heat_rate_delta, 4, 8)
-
-    # Recommendations
     recs = []
     if health_index < 60:
-        recs.append("1. **Hot Gas Path Inspection** - Health index degraded")
+        recs.append("- Hot Gas Path Inspection needed")
     if vibration > 0.4:
-        recs.append("2. **Bearing Analysis** - Elevated vibration")
+        recs.append("- Bearing analysis required")
     if heat_rate_delta > 6:
-        recs.append("3. **Compressor Wash** - Heat rate deviation")
-    if operating_hours > 60000:
-        recs.append("4. **Major Overhaul Planning** - High operating hours")
+        recs.append("- Compressor wash recommended")
     if not recs:
-        recs.append("Continue normal condition monitoring")
+        recs.append("- Continue normal monitoring")
 
     return f"""# RUL Prediction
 
 ## Remaining Useful Life: **{rul} cycles**
 
-## Urgency: {urgency}
-{action}
-
----
+**{urgency}**
 
 ## Equipment Status
-| Parameter | Value | Status |
-|-----------|-------|--------|
-| Health Index | {health_index}% | {hi_status} |
-| Vibration | {vibration} in/s | {vib_status} |
-| Heat Rate Delta | {heat_rate_delta}% | {hr_status} |
-| Operating Hours | {int(operating_hours):,} | - |
-| Start Count | {int(start_count):,} | - |
+| Parameter | Value |
+|-----------|-------|
+| Health Index | {health_index}% |
+| Vibration | {vibration} in/s |
+| Heat Rate Delta | {heat_rate_delta}% |
+| Operating Hours | {int(operating_hours):,} |
+| Start Count | {int(start_count):,} |
 
 ## Recommendations
 {chr(10).join(recs)}
-
----
-*Model: [rul-predictor-ccgt](https://huggingface.co/davidfertube/rul-predictor-ccgt)*
 """
 
 demo = gr.Interface(
     fn=predict,
     inputs=[
-        gr.Number(label="Health Index (%)", value=85, minimum=0, maximum=100),
-        gr.Number(label="Vibration (in/s)", value=0.18, minimum=0, maximum=1),
-        gr.Number(label="Heat Rate Delta (%)", value=2.5, minimum=0, maximum=15),
+        gr.Number(label="Health Index (%)", value=85),
+        gr.Number(label="Vibration (in/s)", value=0.18),
+        gr.Number(label="Heat Rate Delta (%)", value=2.5),
         gr.Number(label="Operating Hours", value=52000),
         gr.Number(label="Start Count", value=950),
     ],
-    outputs=gr.Markdown(),
+    outputs="markdown",
     title="Predictive Agent",
-    description="""**LSTM-Based RUL Prediction for CCGT Equipment**
-
-Predict Remaining Useful Life to optimize maintenance scheduling.
-
-Click an example below to test.""",
+    description="LSTM-Based RUL Prediction for CCGT Equipment",
     examples=[
-        [96.5, 0.14, 1.2, 48500, 920],   # Healthy
-        [42.3, 0.48, 8.5, 68000, 1180],  # Degraded
+        [96.5, 0.14, 1.2, 48500, 920],
+        [42.3, 0.48, 8.5, 68000, 1180],
     ],
-    cache_examples=False,
-    article="""
-## How It Works
-```
-Health Metrics → LSTM Inference → RUL Estimate → Maintenance Plan
-```
-
-**Resources**: [Model](https://huggingface.co/davidfertube/rul-predictor-ccgt) | [Dataset](https://huggingface.co/datasets/davidfertube/ccgt-health-history) | [Portfolio](https://davidfernandez.dev)
-
-*Built by David Fernandez - Industrial AI Engineer*
-"""
 )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()

requirements.txt

@@ -1,3 +1 @@
-gradio==4.44.0
-numpy
-pandas
+gradio==4.36.0