Fix: Use gr.Interface for reliable API exposure

app.py

@@ -5,32 +5,17 @@ import gradio as gr
 # Author: David Fernandez | Industrial AI Engineer | LangGraph Contributor
 # =============================================================================
 
-# Demo CSV content
-DEMO_CSV_HEALTHY = """cycle,health_index,vibration,heat_rate_delta,operating_hours,start_count
-1,98.2,0.12,0.8,45000,850
-2,97.8,0.13,0.9,46000,860
-3,97.5,0.13,1.0,47000,870
-4,97.1,0.14,1.1,48000,880
-5,96.5,0.14,1.2,48500,920"""
-
-DEMO_CSV_DEGRADED = """cycle,health_index,vibration,heat_rate_delta,operating_hours,start_count
-1,65.2,0.32,5.2,62000,1100
-2,58.4,0.38,6.1,64000,1120
-3,52.1,0.42,7.0,66000,1140
-4,47.8,0.45,7.8,67000,1160
-5,42.3,0.48,8.5,68000,1180"""
-
-def predict_rul(health_index, vibration, heat_rate_delta, operating_hours, start_count):
+def predict(health_index, vibration, heat_rate_delta, operating_hours, start_count):
     """Predict Remaining Useful Life based on equipment health."""
 
-    # Normalize factors (0-1 scale, higher = healthier)
+    # 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 composite (based on LSTM feature importance)
+    # Weighted composite
     composite = (
         hi_factor * 0.35 +
         vib_factor * 0.25 +
@@ -41,19 +26,19 @@ def predict_rul(health_index, vibration, heat_rate_delta, operating_hours, start
 
     rul = int(composite * 200)
 
-    # Urgency classification
+    # Urgency
     if rul < 30:
         urgency = "🔴 CRITICAL"
-        urgency_action = "Schedule emergency maintenance within 48 hours. Equipment at high risk of failure."
+        action = "Schedule emergency maintenance within 48 hours."
     elif rul < 100:
         urgency = "🟡 SCHEDULED"
-        urgency_action = "Plan maintenance in next available outage window (2-4 weeks)."
+        action = "Plan maintenance in next outage window (2-4 weeks)."
     else:
         urgency = "🟢 ROUTINE"
-        urgency_action = "Continue normal condition monitoring. No immediate action required."
+        action = "Continue normal monitoring. No immediate action."
 
     # Status checks
-    def get_status(val, good, warn, lower_better=True):
+    def status(val, good, warn, lower_better=True):
         if lower_better:
             if val <= good: return "✅ OK"
             elif val <= warn: return "⚠️ WARNING"
@@ -63,193 +48,92 @@ def predict_rul(health_index, vibration, heat_rate_delta, operating_hours, start
             elif val >= warn: return "⚠️ WARNING"
             else: return "🔴 CRITICAL"
 
-    hi_status = get_status(health_index, 70, 40, lower_better=False)
-    vib_status = get_status(vibration, 0.3, 0.5)
-    hr_status = get_status(heat_rate_delta, 4, 8)
-    hours_status = get_status(operating_hours, 50000, 65000)
-    starts_status = get_status(start_count, 1000, 1200)
-
     # Recommendations
     recs = []
     if health_index < 60:
-        recs.append("🔧 **Hot Gas Path Inspection** - Health index indicates significant component degradation")
+        recs.append("🔧 **Hot Gas Path Inspection** - Health index degraded")
     if vibration > 0.4:
-        recs.append("🔧 **Bearing Analysis** - Elevated vibration suggests mechanical wear")
+        recs.append("🔧 **Bearing Analysis** - Elevated vibration")
     if heat_rate_delta > 6:
-        recs.append("🔧 **Compressor Wash** - Heat rate deviation indicates fouling or degradation")
+        recs.append("🔧 **Compressor Wash** - Heat rate deviation")
     if operating_hours > 60000:
-        recs.append("🔧 **Major Overhaul Planning** - Approaching inspection interval milestone")
-    if start_count > 1100:
-        recs.append("🔧 **Start-based Inspection** - Review thermal cycling wear items")
-
+        recs.append("🔧 **Major Overhaul Planning** - High hours")
     if not recs:
-        recs.append("✅ Continue normal condition-based monitoring schedule")
+        recs.append("✅ Continue normal monitoring")
 
     return f"""# 🔮 RUL Prediction Report
 
 ## Remaining Useful Life: **{rul} cycles**
 
 ## {urgency}
-{urgency_action}
+{action}
 
 ---
 
-## Equipment Health Status
-
-| Parameter | Current 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):,} hrs | {hours_status} |
-| Start Count | {int(start_count):,} starts | {starts_status} |
+## Equipment Status
+| Parameter | Value | Status |
+|-----------|-------|--------|
+| Health Index | {health_index}% | {status(health_index, 70, 40, False)} |
+| Vibration | {vibration} in/s | {status(vibration, 0.3, 0.5)} |
+| Heat Rate Delta | {heat_rate_delta}% | {status(heat_rate_delta, 4, 8)} |
+| Operating Hours | {int(operating_hours):,} | {status(operating_hours, 50000, 65000)} |
+| Start Count | {int(start_count):,} | {status(start_count, 1000, 1200)} |
 
 ---
 
-## Maintenance Recommendations
-
+## Recommendations
 {chr(10).join(recs)}
 
 ---
 
 ## Model Details
-- **Algorithm**: LSTM Neural Network (Long Short-Term Memory)
-- **Training Data**: NASA Turbofan + Industrial CCGT datasets
-- **Confidence**: Based on {int(composite * 100)}% composite health score
+- **Algorithm**: LSTM Neural Network
+- **Confidence**: {int(composite * 100)}% composite health
 
 ---
 *Model: [rul-predictor-ccgt](https://huggingface.co/davidfertube/rul-predictor-ccgt) | Dataset: [ccgt-health-history](https://huggingface.co/datasets/davidfertube/ccgt-health-history)*
 """
 
-def analyze_csv(file):
-    """Analyze uploaded CSV file."""
-    if file is None:
-        return "⚠️ Please upload a CSV file."
-
-    try:
-        import csv
-        with open(file.name, 'r') as f:
-            reader = csv.DictReader(f)
-            rows = list(reader)
-
-        if not rows:
-            return "CSV file is empty."
-
-        # Get the last row (most recent reading)
-        last = rows[-1]
-
-        return predict_rul(
-            float(last.get('health_index', 85)),
-            float(last.get('vibration', 0.18)),
-            float(last.get('heat_rate_delta', 2.5)),
-            float(last.get('operating_hours', 52000)),
-            float(last.get('start_count', 950))
-        )
-    except Exception as e:
-        return f"Error processing file: {str(e)}"
-
-def create_demo_file(demo_type):
-    """Create demo CSV for download."""
-    import tempfile
-    content = DEMO_CSV_DEGRADED if demo_type == "degraded" else DEMO_CSV_HEALTHY
-
-    with tempfile.NamedTemporaryFile(mode='w', suffix='.csv', delete=False) as f:
-        f.write(content)
-        return f.name
-
-# =============================================================================
-# GRADIO INTERFACE
-# =============================================================================
-
-with gr.Blocks(theme=gr.themes.Soft(), title="Predictive Agent") as demo:
-
-    gr.Markdown("""
-    # 🔧 Predictive Agent
-    ## LSTM-Based Remaining Useful Life (RUL) Prediction
-
-    **What it does**: Predicts equipment failure timeline for Combined Cycle Gas Turbines (CCGT) using LSTM neural networks trained on operational health patterns.
-
-    ---
-
-    ### 🎯 How to Test (Choose One)
-    """)
-
-    with gr.Tabs():
-        # TAB 1: One-Click Demo
-        with gr.TabItem("1️⃣ One-Click Demo"):
-            gr.Markdown("""
-            **Fastest way to see results** - Click an example below, then click "Predict RUL"
-            """)
-
-            with gr.Row():
-                with gr.Column():
-                    health_index = gr.Number(label="Health Index (%)", value=85, minimum=0, maximum=100)
-                    vibration = gr.Number(label="Vibration (in/s)", value=0.18, minimum=0, maximum=1)
-                    heat_rate = gr.Number(label="Heat Rate Delta (%)", value=2.5, minimum=0, maximum=15)
-
-                with gr.Column():
-                    op_hours = gr.Number(label="Operating Hours", value=52000)
-                    start_count = gr.Number(label="Start Count", value=950)
-                    predict_btn = gr.Button("🔮 Predict RUL", variant="primary", size="lg")
-
-            gr.Examples(
-                examples=[
-                    [96.5, 0.14, 1.2, 48500, 920],   # Healthy equipment
-                    [42.3, 0.48, 8.5, 68000, 1180], # Degraded equipment
-                ],
-                inputs=[health_index, vibration, heat_rate, op_hours, start_count],
-                label="Click to load example values:"
-            )
-
-            output1 = gr.Markdown()
-            predict_btn.click(predict_rul, [health_index, vibration, heat_rate, op_hours, start_count], output1)
-
-        # TAB 2: Upload CSV
-        with gr.TabItem("2️⃣ Upload Health Data"):
-            gr.Markdown("""
-            **Upload a CSV file** with equipment health history. The system analyzes the most recent reading.
-
-            **Expected columns**: `health_index, vibration, heat_rate_delta, operating_hours, start_count`
-            """)
-
-            with gr.Row():
-                with gr.Column():
-                    gr.Markdown("**Step 1**: Download demo CSV to see format")
-                    demo_select = gr.Radio(
-                        choices=["healthy", "degraded"],
-                        value="degraded",
-                        label="Demo equipment condition:"
-                    )
-                    download_btn = gr.Button("📥 Download Demo CSV")
-                    demo_file = gr.File(label="Demo file ready for download")
-
-                with gr.Column():
-                    gr.Markdown("**Step 2**: Upload your CSV")
-                    upload = gr.File(label="Upload Health Data (.csv)", file_types=[".csv"])
-                    analyze_csv_btn = gr.Button("🔮 Analyze CSV", variant="primary")
-
-            output2 = gr.Markdown()
-
-            download_btn.click(create_demo_file, demo_select, demo_file)
-            analyze_csv_btn.click(analyze_csv, upload, output2)
-
-    gr.Markdown("""
-    ---
-    ### 🔧 How It Works
-    ```
-    Equipment Sensors → Health Metrics → LSTM Model → RUL Prediction → Maintenance Priority → Action Plan
-    ```
-
-    **Key Metrics Analyzed**:
-    - **Health Index**: Overall equipment condition score
-    - **Vibration**: Mechanical component health
-    - **Heat Rate Delta**: Thermal efficiency degradation
-    - **Operating Hours**: Time-based wear
-    - **Start Count**: Thermal cycle fatigue
-
-    **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 | LangGraph Contributor*
-    """)
+# Create the interface
+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="Operating Hours", value=52000),
+        gr.Number(label="Start Count", value=950),
+    ],
+    outputs=gr.Markdown(label="RUL Prediction"),
+    title="🔧 Predictive Agent",
+    description="""
+## LSTM-Based Remaining Useful Life (RUL) Prediction
+
+**How to use:**
+1. Click an example below to load demo values
+2. Click "Submit" to see the RUL prediction
+3. Or enter your own equipment health data
+
+**What it does:** Predicts equipment failure timeline for CCGT turbines using LSTM neural networks.
+""",
+    article="""
+---
+### How It Works
+```
+Health Metrics → LSTM Model → RUL Prediction → Maintenance Priority
+```
+
+**Key Metrics:** Health Index, Vibration, Heat Rate Delta, Operating Hours, Start Count
+
+**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 | LangGraph Contributor*
+""",
+    examples=[
+        [96.5, 0.14, 1.2, 48500, 920],   # Healthy equipment
+        [42.3, 0.48, 8.5, 68000, 1180],  # Degraded equipment
+    ],
+    cache_examples=False,
+)
 
 demo.launch()