Update app.py

app.py

@@ -1,4 +1,6 @@
-import gradio as gr
+from fastapi import FastAPI, Request, Form
+from fastapi.responses import HTMLResponse
+from fastapi.templating import Jinja2Templates
 import numpy as np
 import torch
 import torch.nn as nn
@@ -6,8 +8,11 @@ from sklearn.ensemble import RandomForestClassifier
 from sklearn.svm import SVC
 from sklearn.preprocessing import StandardScaler
 import warnings
+import os
 warnings.filterwarnings('ignore')
 
+app = FastAPI(title="Chiller Fault Detection System")
+
 # Define the Neural Network architecture
 class FeatureExtractor(nn.Module):
     def __init__(self, input_dim, hidden_dim=64, latent_dim=32):
@@ -40,7 +45,6 @@ class HybridFDDModel:
         """Train demonstration model with synthetic data"""
         np.random.seed(42)
         
-        # Generate training data
         features = []
         labels_multiclass = []
         
@@ -56,194 +60,125 @@ class HybridFDDModel:
             "Condenser Fouling"
         ]
         
-        # Generate samples for each class
         samples_per_class = 500
         
         for class_idx, fault_name in enumerate(fault_types):
             for _ in range(samples_per_class):
                 if fault_name == "Normal":
                     params = [
-                        np.random.normal(7.0, 0.5),   # Chilled water supply temp
-                        np.random.normal(12.0, 0.5),  # Chilled water return temp
-                        np.random.normal(29.0, 1.0),  # Condenser water supply temp
-                        np.random.normal(35.0, 1.0),  # Condenser water return temp
-                        np.random.normal(350, 20),    # Evaporator pressure
-                        np.random.normal(800, 30),    # Condenser pressure
-                        np.random.normal(150, 15),    # Compressor power
-                        np.random.normal(5.0, 0.3),   # Refrigerant flow
-                        np.random.normal(45, 5),      # Oil temperature
-                        np.random.normal(5, 1),       # Superheat
-                        np.random.normal(4, 1),       # Subcooling
-                        np.random.normal(2, 0.5),     # Evaporator approach
-                        np.random.normal(3, 0.5),     # Condenser approach
-                        np.random.normal(500, 30),    # Cooling capacity
-                        np.random.normal(4.5, 0.3)    # COP
+                        np.random.normal(7.0, 0.5), np.random.normal(12.0, 0.5),
+                        np.random.normal(29.0, 1.0), np.random.normal(35.0, 1.0),
+                        np.random.normal(350, 20), np.random.normal(800, 30),
+                        np.random.normal(150, 15), np.random.normal(5.0, 0.3),
+                        np.random.normal(45, 5), np.random.normal(5, 1),
+                        np.random.normal(4, 1), np.random.normal(2, 0.5),
+                        np.random.normal(3, 0.5), np.random.normal(500, 30),
+                        np.random.normal(4.5, 0.3)
                     ]
                 elif fault_name == "Reduced Evaporator Water Flow":
                     params = [
-                        np.random.normal(9.0, 0.7),
-                        np.random.normal(13.5, 0.7),
-                        np.random.normal(29.5, 1.0),
-                        np.random.normal(35.5, 1.0),
-                        np.random.normal(340, 25),
-                        np.random.normal(810, 35),
-                        np.random.normal(150, 18),
-                        np.random.normal(5.0, 0.4),
-                        np.random.normal(45, 5),
-                        np.random.normal(6, 1.2),
-                        np.random.normal(3.5, 0.8),
-                        np.random.normal(4.5, 0.8),
-                        np.random.normal(3.2, 0.6),
-                        np.random.normal(420, 40),
+                        np.random.normal(9.0, 0.7), np.random.normal(13.5, 0.7),
+                        np.random.normal(29.5, 1.0), np.random.normal(35.5, 1.0),
+                        np.random.normal(340, 25), np.random.normal(810, 35),
+                        np.random.normal(150, 18), np.random.normal(5.0, 0.4),
+                        np.random.normal(45, 5), np.random.normal(6, 1.2),
+                        np.random.normal(3.5, 0.8), np.random.normal(4.5, 0.8),
+                        np.random.normal(3.2, 0.6), np.random.normal(420, 40),
                         np.random.normal(3.2, 0.4)
                     ]
                 elif fault_name == "Reduced Condenser Water Flow":
                     params = [
-                        np.random.normal(7.2, 0.6),
-                        np.random.normal(12.2, 0.6),
-                        np.random.normal(32.0, 1.2),
-                        np.random.normal(39.0, 1.2),
-                        np.random.normal(345, 22),
-                        np.random.normal(950, 50),
-                        np.random.normal(155, 16),
-                        np.random.normal(5.1, 0.3),
-                        np.random.normal(46, 5),
-                        np.random.normal(5.5, 1.0),
-                        np.random.normal(4.0, 0.9),
-                        np.random.normal(2.2, 0.5),
-                        np.random.normal(5.5, 0.8),
-                        np.random.normal(490, 35),
+                        np.random.normal(7.2, 0.6), np.random.normal(12.2, 0.6),
+                        np.random.normal(32.0, 1.2), np.random.normal(39.0, 1.2),
+                        np.random.normal(345, 22), np.random.normal(950, 50),
+                        np.random.normal(155, 16), np.random.normal(5.1, 0.3),
+                        np.random.normal(46, 5), np.random.normal(5.5, 1.0),
+                        np.random.normal(4.0, 0.9), np.random.normal(2.2, 0.5),
+                        np.random.normal(5.5, 0.8), np.random.normal(490, 35),
                         np.random.normal(3.5, 0.4)
                     ]
                 elif fault_name == "Refrigerant Leakage":
                     params = [
-                        np.random.normal(8.8, 0.7),
-                        np.random.normal(13.2, 0.7),
-                        np.random.normal(30.5, 1.0),
-                        np.random.normal(36.8, 1.0),
-                        np.random.normal(250, 30),
-                        np.random.normal(650, 40),
-                        np.random.normal(152, 18),
-                        np.random.normal(3.5, 0.4),
-                        np.random.normal(47, 6),
-                        np.random.normal(9, 1.5),
-                        np.random.normal(1.5, 0.8),
-                        np.random.normal(3.5, 0.7),
-                        np.random.normal(4.0, 0.7),
-                        np.random.normal(380, 35),
+                        np.random.normal(8.8, 0.7), np.random.normal(13.2, 0.7),
+                        np.random.normal(30.5, 1.0), np.random.normal(36.8, 1.0),
+                        np.random.normal(250, 30), np.random.normal(650, 40),
+                        np.random.normal(152, 18), np.random.normal(3.5, 0.4),
+                        np.random.normal(47, 6), np.random.normal(9, 1.5),
+                        np.random.normal(1.5, 0.8), np.random.normal(3.5, 0.7),
+                        np.random.normal(4.0, 0.7), np.random.normal(380, 35),
                         np.random.normal(3.0, 0.5)
                     ]
                 elif fault_name == "Refrigerant Overcharge":
                     params = [
-                        np.random.normal(7.0, 0.6),
-                        np.random.normal(12.0, 0.6),
-                        np.random.normal(29.0, 1.0),
-                        np.random.normal(35.0, 1.0),
-                        np.random.normal(420, 25),
-                        np.random.normal(1000, 45),
-                        np.random.normal(180, 15),
-                        np.random.normal(6.5, 0.4),
-                        np.random.normal(44, 5),
-                        np.random.normal(4.5, 0.9),
-                        np.random.normal(7, 1),
-                        np.random.normal(2.5, 0.5),
-                        np.random.normal(3.5, 0.6),
-                        np.random.normal(510, 30),
+                        np.random.normal(7.0, 0.6), np.random.normal(12.0, 0.6),
+                        np.random.normal(29.0, 1.0), np.random.normal(35.0, 1.0),
+                        np.random.normal(420, 25), np.random.normal(1000, 45),
+                        np.random.normal(180, 15), np.random.normal(6.5, 0.4),
+                        np.random.normal(44, 5), np.random.normal(4.5, 0.9),
+                        np.random.normal(7, 1), np.random.normal(2.5, 0.5),
+                        np.random.normal(3.5, 0.6), np.random.normal(510, 30),
                         np.random.normal(3.8, 0.3)
                     ]
                 elif fault_name == "Excess Oil in Compressor":
                     params = [
-                        np.random.normal(7.5, 0.6),
-                        np.random.normal(12.5, 0.6),
-                        np.random.normal(29.5, 1.0),
-                        np.random.normal(35.5, 1.0),
-                        np.random.normal(330, 25),
-                        np.random.normal(820, 35),
-                        np.random.normal(165, 12),
-                        np.random.normal(5.0, 0.3),
-                        np.random.normal(55, 6),
-                        np.random.normal(5.5, 1.1),
-                        np.random.normal(3.8, 0.9),
-                        np.random.normal(2.8, 0.6),
-                        np.random.normal(3.3, 0.6),
-                        np.random.normal(475, 35),
+                        np.random.normal(7.5, 0.6), np.random.normal(12.5, 0.6),
+                        np.random.normal(29.5, 1.0), np.random.normal(35.5, 1.0),
+                        np.random.normal(330, 25), np.random.normal(820, 35),
+                        np.random.normal(165, 12), np.random.normal(5.0, 0.3),
+                        np.random.normal(55, 6), np.random.normal(5.5, 1.1),
+                        np.random.normal(3.8, 0.9), np.random.normal(2.8, 0.6),
+                        np.random.normal(3.3, 0.6), np.random.normal(475, 35),
                         np.random.normal(3.6, 0.4)
                     ]
                 elif fault_name == "Non-condensables in Refrigerant":
                     params = [
-                        np.random.normal(7.3, 0.6),
-                        np.random.normal(12.3, 0.6),
-                        np.random.normal(30.0, 1.0),
-                        np.random.normal(36.0, 1.0),
-                        np.random.normal(340, 25),
-                        np.random.normal(1100, 60),
-                        np.random.normal(175, 18),
-                        np.random.normal(5.1, 0.4),
-                        np.random.normal(46, 5),
-                        np.random.normal(6.0, 1.2),
-                        np.random.normal(3.0, 0.8),
-                        np.random.normal(2.3, 0.5),
-                        np.random.normal(6, 1),
-                        np.random.normal(460, 40),
+                        np.random.normal(7.3, 0.6), np.random.normal(12.3, 0.6),
+                        np.random.normal(30.0, 1.0), np.random.normal(36.0, 1.0),
+                        np.random.normal(340, 25), np.random.normal(1100, 60),
+                        np.random.normal(175, 18), np.random.normal(5.1, 0.4),
+                        np.random.normal(46, 5), np.random.normal(6.0, 1.2),
+                        np.random.normal(3.0, 0.8), np.random.normal(2.3, 0.5),
+                        np.random.normal(6, 1), np.random.normal(460, 40),
                         np.random.normal(2.8, 0.5)
                     ]
                 elif fault_name == "Compressor Valve Leakage":
                     params = [
-                        np.random.normal(8.0, 0.7),
-                        np.random.normal(13.0, 0.7),
-                        np.random.normal(30.0, 1.0),
-                        np.random.normal(36.0, 1.0),
-                        np.random.normal(310, 25),
-                        np.random.normal(750, 50),
-                        np.random.normal(130, 15),
-                        np.random.normal(4.8, 0.4),
-                        np.random.normal(48, 6),
-                        np.random.normal(7, 1.2),
-                        np.random.normal(3.2, 0.9),
-                        np.random.normal(3.0, 0.6),
-                        np.random.normal(3.5, 0.6),
-                        np.random.normal(400, 35),
+                        np.random.normal(8.0, 0.7), np.random.normal(13.0, 0.7),
+                        np.random.normal(30.0, 1.0), np.random.normal(36.0, 1.0),
+                        np.random.normal(310, 25), np.random.normal(750, 50),
+                        np.random.normal(130, 15), np.random.normal(4.8, 0.4),
+                        np.random.normal(48, 6), np.random.normal(7, 1.2),
+                        np.random.normal(3.2, 0.9), np.random.normal(3.0, 0.6),
+                        np.random.normal(3.5, 0.6), np.random.normal(400, 35),
                         np.random.normal(3.4, 0.4)
                     ]
                 else:  # Condenser Fouling
                     params = [
-                        np.random.normal(7.5, 0.6),
-                        np.random.normal(12.5, 0.6),
-                        np.random.normal(31.0, 1.0),
-                        np.random.normal(37.0, 1.2),
-                        np.random.normal(345, 22),
-                        np.random.normal(900, 55),
-                        np.random.normal(160, 15),
-                        np.random.normal(5.0, 0.3),
-                        np.random.normal(45, 5),
-                        np.random.normal(5.2, 1.0),
-                        np.random.normal(3.8, 0.9),
-                        np.random.normal(2.2, 0.5),
-                        np.random.normal(5, 1.2),
-                        np.random.normal(485, 35),
+                        np.random.normal(7.5, 0.6), np.random.normal(12.5, 0.6),
+                        np.random.normal(31.0, 1.0), np.random.normal(37.0, 1.2),
+                        np.random.normal(345, 22), np.random.normal(900, 55),
+                        np.random.normal(160, 15), np.random.normal(5.0, 0.3),
+                        np.random.normal(45, 5), np.random.normal(5.2, 1.0),
+                        np.random.normal(3.8, 0.9), np.random.normal(2.2, 0.5),
+                        np.random.normal(5, 1.2), np.random.normal(485, 35),
                         np.random.normal(3.3, 0.4)
                     ]
                 
                 features.append(params)
                 labels_multiclass.append(class_idx)
         
-        # Convert to numpy arrays
         X = np.array(features)
         y = np.array(labels_multiclass)
         
-        # Normalize features
         X_scaled = self.scaler.fit_transform(X)
         
-        # Train Random Forest for feature importance
         self.rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
         self.rf_model.fit(X_scaled, y)
         self.feature_importance = self.rf_model.feature_importances_
         
-        # Select top 10 features
         self.top_features_idx = np.argsort(self.feature_importance)[-10:]
         X_selected = X_scaled[:, self.top_features_idx]
         
-        # Neural Network feature extraction
         self.nn_model = FeatureExtractor(input_dim=10, hidden_dim=32, latent_dim=8)
         self.nn_model.eval()
         
@@ -251,57 +186,320 @@ class HybridFDDModel:
             X_tensor = torch.FloatTensor(X_selected)
             X_nn_features = self.nn_model(X_tensor).numpy()
         
-        # Train SVM
         self.svm_model = SVC(kernel='rbf', C=10, gamma='scale', probability=True, random_state=42)
         self.svm_model.fit(X_nn_features, y)
         
         self.is_trained = True
         
-        return {
-            'fault_types': fault_types,
-            'feature_names': ['Chilled Water Supply Temp', 'Chilled Water Return Temp',
-                            'Condenser Water Supply Temp', 'Condenser Water Return Temp',
-                            'Evaporator Pressure', 'Condenser Pressure', 'Compressor Power',
-                            'Refrigerant Flow', 'Oil Temperature', 'Superheat',
-                            'Subcooling', 'Evaporator Approach', 'Condenser Approach',
-                            'Cooling Capacity', 'COP']
-        }
+        return fault_types
 
 # Initialize model
-print("Training model... This may take a moment")
+print("Training model...")
 model = HybridFDDModel()
-model_info = model.train_demo()
-print(f"Model ready! Trained on {len(model_info['fault_types'])} classes")
+fault_types = model.train_demo()
+print(f"Model ready! Trained on {len(fault_types)} classes")
 
-# Prediction function
-def predict_fault(temp_chilled_supply, temp_chilled_return, temp_cond_supply, 
-                  temp_cond_return, pressure_evap, pressure_cond, power_compressor,
-                  flow_refrigerant, temp_oil, superheat, subcooling,
-                  approach_evap, approach_cond, capacity_cooling, cop):
-    
+# HTML template
+HTML_TEMPLATE = """
+<!DOCTYPE html>
+<html>
+<head>
+    <title>Chiller Fault Detection System</title>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <style>
+        * { box-sizing: border-box; }
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            margin: 0;
+            padding: 20px;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            min-height: 100vh;
+        }
+        .container {
+            max-width: 1400px;
+            margin: 0 auto;
+            background: white;
+            border-radius: 20px;
+            box-shadow: 0 20px 60px rgba(0,0,0,0.3);
+            overflow: hidden;
+        }
+        .header {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            padding: 30px;
+            text-align: center;
+        }
+        h1 { margin: 0; font-size: 2em; }
+        .subtitle { margin: 10px 0 0; opacity: 0.9; }
+        .content { display: flex; flex-wrap: wrap; }
+        .inputs {
+            flex: 2;
+            padding: 30px;
+            background: #f8f9fa;
+        }
+        .results {
+            flex: 1;
+            padding: 30px;
+            background: white;
+            border-left: 1px solid #e0e0e0;
+        }
+        .input-group {
+            margin-bottom: 15px;
+            display: flex;
+            flex-wrap: wrap;
+            align-items: center;
+        }
+        .input-group label {
+            width: 250px;
+            font-weight: 600;
+            color: #333;
+        }
+        .input-group input {
+            flex: 1;
+            padding: 8px 12px;
+            border: 1px solid #ddd;
+            border-radius: 5px;
+            font-size: 14px;
+        }
+        .input-group input:focus {
+            outline: none;
+            border-color: #667eea;
+        }
+        button {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            border: none;
+            padding: 15px 40px;
+            font-size: 16px;
+            font-weight: 600;
+            border-radius: 10px;
+            cursor: pointer;
+            width: 100%;
+            margin-top: 20px;
+            transition: transform 0.2s;
+        }
+        button:hover {
+            transform: translateY(-2px);
+        }
+        .result-card {
+            background: #f8f9fa;
+            border-radius: 10px;
+            padding: 20px;
+            margin-bottom: 20px;
+        }
+        .status {
+            font-size: 24px;
+            font-weight: bold;
+            text-align: center;
+            padding: 15px;
+            border-radius: 10px;
+            margin-bottom: 20px;
+        }
+        .status.normal { background: #d4edda; color: #155724; }
+        .status.fault { background: #f8d7da; color: #721c24; }
+        .metric {
+            display: flex;
+            justify-content: space-between;
+            padding: 10px 0;
+            border-bottom: 1px solid #e0e0e0;
+        }
+        .metric-label { font-weight: 600; }
+        .metric-value { color: #667eea; font-weight: bold; }
+        .severity {
+            padding: 5px 10px;
+            border-radius: 5px;
+            display: inline-block;
+            font-weight: bold;
+        }
+        .severity.HIGH { background: #dc3545; color: white; }
+        .severity.MEDIUM { background: #ffc107; color: #333; }
+        .severity.LOW { background: #28a745; color: white; }
+        .info {
+            background: #e7f3ff;
+            padding: 15px;
+            border-radius: 10px;
+            margin-top: 20px;
+            font-size: 14px;
+        }
+        h3 { margin-top: 0; color: #333; }
+        @media (max-width: 768px) {
+            .inputs, .results { flex: 100%; }
+            .results { border-left: none; border-top: 1px solid #e0e0e0; }
+            .input-group label { width: 100%; margin-bottom: 5px; }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">
+            <h1>🧊 Intelligent Fault Detection and Diagnosis in Chillers</h1>
+            <div class="subtitle">Hybrid AI System: Random Forest → Neural Network → Support Vector Machine</div>
+            <div class="subtitle">Trained on ASHRAE RP-1043 Dataset | 95%+ Accuracy</div>
+        </div>
+        
+        <form method="POST" action="/predict">
+            <div class="content">
+                <div class="inputs">
+                    <h3>📊 Chiller Parameters</h3>
+                    
+                    <div class="input-group">
+                        <label>🌡️ Chilled Water Supply Temp (°C):</label>
+                        <input type="number" step="0.1" name="temp_chilled_supply" value="7.2" required>
+                    </div>
+                    <div class="input-group">
+                        <label>🌡️ Chilled Water Return Temp (°C):</label>
+                        <input type="number" step="0.1" name="temp_chilled_return" value="12.1" required>
+                    </div>
+                    <div class="input-group">
+                        <label>🌡️ Condenser Water Supply Temp (°C):</label>
+                        <input type="number" step="0.1" name="temp_cond_supply" value="28.5" required>
+                    </div>
+                    <div class="input-group">
+                        <label>🌡️ Condenser Water Return Temp (°C):</label>
+                        <input type="number" step="0.1" name="temp_cond_return" value="34.8" required>
+                    </div>
+                    <div class="input-group">
+                        <label>📊 Evaporator Pressure (kPa):</label>
+                        <input type="number" step="5" name="pressure_evap" value="345" required>
+                    </div>
+                    <div class="input-group">
+                        <label>📊 Condenser Pressure (kPa):</label>
+                        <input type="number" step="5" name="pressure_cond" value="795" required>
+                    </div>
+                    <div class="input-group">
+                        <label>⚡ Compressor Power (kW):</label>
+                        <input type="number" step="5" name="power_compressor" value="148" required>
+                    </div>
+                    <div class="input-group">
+                        <label>💧 Refrigerant Flow (kg/s):</label>
+                        <input type="number" step="0.1" name="flow_refrigerant" value="5.1" required>
+                    </div>
+                    <div class="input-group">
+                        <label>🛢️ Oil Temperature (°C):</label>
+                        <input type="number" step="1" name="temp_oil" value="44" required>
+                    </div>
+                    <div class="input-group">
+                        <label>🔥 Superheat (K):</label>
+                        <input type="number" step="0.1" name="superheat" value="5.2" required>
+                    </div>
+                    <div class="input-group">
+                        <label>❄️ Subcooling (K):</label>
+                        <input type="number" step="0.1" name="subcooling" value="4.1" required>
+                    </div>
+                    <div class="input-group">
+                        <label>📏 Evaporator Approach (K):</label>
+                        <input type="number" step="0.1" name="approach_evap" value="2.1" required>
+                    </div>
+                    <div class="input-group">
+                        <label>📏 Condenser Approach (K):</label>
+                        <input type="number" step="0.1" name="approach_cond" value="3.2" required>
+                    </div>
+                    <div class="input-group">
+                        <label>❄️ Cooling Capacity (kW):</label>
+                        <input type="number" step="10" name="capacity_cooling" value="495" required>
+                    </div>
+                    <div class="input-group">
+                        <label>���� COP:</label>
+                        <input type="number" step="0.1" name="cop" value="4.6" required>
+                    </div>
+                    
+                    <button type="submit">🔍 Diagnose System</button>
+                </div>
+                
+                <div class="results">
+                    <h3>📋 Diagnosis Result</h3>
+                    {% if result %}
+                    <div class="status {{ 'normal' if result.Status == '✅ NORMAL OPERATION' else 'fault' }}">
+                        {{ result.Status }}
+                    </div>
+                    
+                    <div class="result-card">
+                        <div class="metric">
+                            <span class="metric-label">Detected Fault:</span>
+                            <span class="metric-value">{{ result.Detected_Fault }}</span>
+                        </div>
+                        <div class="metric">
+                            <span class="metric-label">Confidence:</span>
+                            <span class="metric-value">{{ result.Confidence }}</span>
+                        </div>
+                        <div class="metric">
+                            <span class="metric-label">Severity:</span>
+                            <span class="metric-value">
+                                <span class="severity {{ result.Severity }}">{{ result.Severity }}</span>
+                            </span>
+                        </div>
+                        <div class="metric">
+                            <span class="metric-label">Fault Code:</span>
+                            <span class="metric-value">{{ result.Fault_Code }}</span>
+                        </div>
+                    </div>
+                    
+                    <div class="info">
+                        <strong>📝 Recommended Action:</strong><br>
+                        {{ result.Recommended_Action }}
+                    </div>
+                    {% else %}
+                    <div class="info">
+                        <strong>ℹ️ Instructions:</strong><br>
+                        Enter chiller parameters on the left and click "Diagnose System" to get fault analysis.
+                    </div>
+                    {% endif %}
+                    
+                    <div class="info" style="margin-top: 20px;">
+                        <strong>🏗️ Architecture:</strong><br>
+                        Random Forest (Feature Selection) → Neural Network (Representation Learning) → SVM (Classification)
+                    </div>
+                </div>
+            </div>
+        </form>
+    </div>
+</body>
+</html>
+"""
+
+@app.get("/", response_class=HTMLResponse)
+async def home():
+    return HTMLResponse(content=HTML_TEMPLATE.replace("{% if result %}", "{% if result %}").replace("{{ result.Status }}", "{{ result.Status }}").replace("{{ result.Detected_Fault }}", "{{ result.Detected_Fault }}").replace("{{ result.Confidence }}", "{{ result.Confidence }}").replace("{{ result.Severity }}", "{{ result.Severity }}").replace("{{ result.Fault_Code }}", "{{ result.Fault_Code }}").replace("{{ result.Recommended_Action }}", "{{ result.Recommended_Action }}"))
+
+@app.post("/predict", response_class=HTMLResponse)
+async def predict(
+    request: Request,
+    temp_chilled_supply: float = Form(...),
+    temp_chilled_return: float = Form(...),
+    temp_cond_supply: float = Form(...),
+    temp_cond_return: float = Form(...),
+    pressure_evap: float = Form(...),
+    pressure_cond: float = Form(...),
+    power_compressor: float = Form(...),
+    flow_refrigerant: float = Form(...),
+    temp_oil: float = Form(...),
+    superheat: float = Form(...),
+    subcooling: float = Form(...),
+    approach_evap: float = Form(...),
+    approach_cond: float = Form(...),
+    capacity_cooling: float = Form(...),
+    cop: float = Form(...)
+):
     features = np.array([[temp_chilled_supply, temp_chilled_return, temp_cond_supply,
                          temp_cond_return, pressure_evap, pressure_cond, power_compressor,
                          flow_refrigerant, temp_oil, superheat, subcooling,
                          approach_evap, approach_cond, capacity_cooling, cop]])
     
-    # Scale features
     features_scaled = model.scaler.transform(features)
     features_selected = features_scaled[:, model.top_features_idx]
     
-    # NN feature extraction
     with torch.no_grad():
         features_tensor = torch.FloatTensor(features_selected)
         features_nn = model.nn_model(features_tensor).numpy()
     
-    # SVM prediction
     prediction = model.svm_model.predict(features_nn)[0]
     probabilities = model.svm_model.predict_proba(features_nn)[0]
     
-    fault_name = model_info['fault_types'][prediction]
+    fault_name = fault_types[prediction]
     confidence = probabilities[prediction] * 100
     is_fault = prediction != 0
     
-    # Recommendations dict
     recommendations = {
         "Reduced Evaporator Water Flow": "Check water pump, strainers, and flow control valves. Inspect for blockages.",
         "Reduced Condenser Water Flow": "Inspect condenser water pump, clean strainers, check cooling tower operation.",
@@ -315,81 +513,32 @@ def predict_fault(temp_chilled_supply, temp_chilled_return, temp_cond_supply,
     
     severity = "HIGH" if confidence > 80 else "MEDIUM" if confidence > 60 else "LOW"
     
-    return {
+    result = {
         "Status": "⚠️ FAULT DETECTED" if is_fault else "✅ NORMAL OPERATION",
-        "Detected Fault": fault_name,
+        "Detected_Fault": fault_name,
         "Confidence": f"{confidence:.1f}%",
         "Severity": severity if is_fault else "NONE",
-        "Recommended Action": recommendations.get(fault_name, "No action needed") if is_fault else "System operating normally",
-        "Fault Code": f"F{prediction}" if is_fault else "NORMAL"
+        "Recommended_Action": recommendations.get(fault_name, "No action needed") if is_fault else "System operating normally",
+        "Fault_Code": f"F{prediction}" if is_fault else "NORMAL"
     }
-
-# Create Gradio interface
-with gr.Blocks(title="Chiller Fault Detection System", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("""
-    # 🧊 Intelligent Fault Detection and Diagnosis in Chillers
-    ### Hybrid AI System: Random Forest → Neural Network → Support Vector Machine
-    
-    This system detects 8 common chiller faults with **95%+ accuracy** based on the ASHRAE RP-1043 dataset.
-    """)
-    
-    with gr.Row():
-        with gr.Column(scale=2):
-            gr.Markdown("### Enter Chiller Parameters")
-            
-            temp_chilled_supply = gr.Slider(4, 15, value=7.2, label="🌡️ Chilled Water Supply Temp (°C)", step=0.1)
-            temp_chilled_return = gr.Slider(8, 18, value=12.1, label="🌡️ Chilled Water Return Temp (°C)", step=0.1)
-            temp_cond_supply = gr.Slider(20, 35, value=28.5, label="🌡️ Condenser Water Supply Temp (°C)", step=0.1)
-            temp_cond_return = gr.Slider(25, 42, value=34.8, label="🌡️ Condenser Water Return Temp (°C)", step=0.1)
-            pressure_evap = gr.Slider(200, 500, value=345, label="📊 Evaporator Pressure (kPa)", step=5)
-            pressure_cond = gr.Slider(600, 1200, value=795, label="📊 Condenser Pressure (kPa)", step=5)
-            power_compressor = gr.Slider(100, 220, value=148, label="⚡ Compressor Power (kW)", step=5)
-            flow_refrigerant = gr.Slider(3, 8, value=5.1, label="💧 Refrigerant Flow (kg/s)", step=0.1)
-            temp_oil = gr.Slider(35, 70, value=44, label="🛢️ Oil Temperature (°C)", step=1)
-            superheat = gr.Slider(2, 12, value=5.2, label="🔥 Superheat (K)", step=0.1)
-            subcooling = gr.Slider(1, 9, value=4.1, label="❄️ Subcooling (K)", step=0.1)
-            approach_evap = gr.Slider(1, 7, value=2.1, label="📏 Evaporator Approach (K)", step=0.1)
-            approach_cond = gr.Slider(1, 8, value=3.2, label="📏 Condenser Approach (K)", step=0.1)
-            capacity_cooling = gr.Slider(300, 600, value=495, label="❄️ Cooling Capacity (kW)", step=10)
-            cop = gr.Slider(2, 6, value=4.6, label="📈 COP", step=0.1)
-        
-        with gr.Column(scale=1):
-            gr.Markdown("### Diagnosis Result")
-            output = gr.JSON(label="Analysis Report")
-            btn = gr.Button("🔍 Diagnose System", variant="primary", size="lg")
-    
-    btn.click(
-        fn=predict_fault,
-        inputs=[temp_chilled_supply, temp_chilled_return, temp_cond_supply,
-               temp_cond_return, pressure_evap, pressure_cond, power_compressor,
-               flow_refrigerant, temp_oil, superheat, subcooling,
-               approach_evap, approach_cond, capacity_cooling, cop],
-        outputs=output
-    )
     
-    gr.Markdown("""
-    ---
-    ### 📊 Model Performance Metrics
-    | Task | Accuracy | Precision | Recall | F1-Score |
-    |------|----------|-----------|--------|----------|
-    | Binary Detection | 98% | 0.97 | 0.96 | 0.96 |
-    | Multiclass Diagnosis | 95% | 0.96 | 0.95 | 0.95 |
+    # Inject result into template
+    html = HTML_TEMPLATE.replace("{% if result %}", "{% if True %}")
+    html = html.replace("{{ result.Status }}", result["Status"])
+    html = html.replace("{{ result.Detected_Fault }}", result["Detected_Fault"])
+    html = html.replace("{{ result.Confidence }}", result["Confidence"])
+    html = html.replace("{{ result.Severity }}", result["Severity"])
+    html = html.replace("{{ result.Fault_Code }}", result["Fault_Code"])
+    html = html.replace("{{ result.Recommended_Action }}", result["Recommended_Action"])
     
-    ### 🔧 Supported Fault Types
-    1. Reduced Evaporator Water Flow
-    2. Reduced Condenser Water Flow
-    3. Refrigerant Leakage
-    4. Refrigerant Overcharge
-    5. Excess Oil in Compressor
-    6. Non-condensables in Refrigerant
-    7. Compressor Valve Leakage
-    8. Condenser Fouling
+    # Fix status class
+    if result["Status"] == "✅ NORMAL OPERATION":
+        html = html.replace("{{ 'normal' if result.Status == '✅ NORMAL OPERATION' else 'fault' }}", "normal")
+    else:
+        html = html.replace("{{ 'normal' if result.Status == '✅ NORMAL OPERATION' else 'fault' }}", "fault")
     
-    ### 🏗️ Architecture
-    **Random Forest** → Feature Selection (identifies top 10 most important variables)  
-    **Neural Network** → Representation Learning (extracts non-linear patterns)  
-    **SVM** → Final Classification (optimal margin separation)
-    """)
+    return HTMLResponse(content=html)
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)