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README.md

@@ -1,5 +1,6 @@
-title: Zodiac 1.0 Engine Monitor
-emoji: 🛥️
+---
+title: Zodiac 1.0 Engine Diagnostics
+emoji: 🛠️
 colorFrom: blue
 colorTo: green
 sdk: gradio
@@ -8,12 +9,13 @@ app_file: app.py
 pinned: false
 ---
 
-# Zodiac 1.0: Predictive Engine Monitoring
+# Zodiac 1.0: Predictive Engine Diagnostics
 
-This is an MVP of a predictive maintenance system for marine and automotive engines. It uses an AI model to analyze vibration data and detect the early signs of mechanical failure.
+This is an MVP of a predictive maintenance system for engines. It uses a regression-based AI model (XGBoost) to predict the normal vibration of an engine based on its current operating conditions.
 
 ## How to Use This Demo
 
-1.  **Get Sample Data:** You can copy the sample data from the `data/raw_data.csv` file in the repository. You will need at least 51 rows.
-2.  **Paste Data:** Paste the copied CSV data into the input box.
-3.  **Get Prediction:** The model will analyze the last sequence of data and determine if it represents a "Normal" or "Anomalous" engine state.
+1.  **Set Operating Conditions:** Use the sliders and dropdowns for input the engine's current RPM, the ambient temperature, fuel level, and sea state.
+2.  **Enter Actual Vibration:** In the final box, enter the vibration reading you would get from a real-world sensor.
+3.  **Get Prediction:** The model will compare the predicted "normal" vibration to the actual value and determine if it's an anomaly.
+

data_logger.py

@@ -1,36 +1,40 @@
-# --- data_logger.py ---
-from flask import Flask, request
+# --- data_logger.py (Regression Version) ---
 import pandas as pd
 import os
 from datetime import datetime
+from flask import Flask, request
 
 app = Flask(__name__)
 
-# Define the data directory and the full path for the CSV file
 DATA_DIR = 'data'
 CSV_FILE = os.path.join(DATA_DIR, 'raw_data.csv')
 
 @app.route('/data', methods=['POST'])
 def receive_data():
     try:
-        # Ensure the data directory exists
         os.makedirs(DATA_DIR, exist_ok=True)
-        
         data = request.get_json()
+        
+        # Define the columns to match the new hardware output and training script
         log_data = {
-            'timestamp': [datetime.now().strftime('%Y-%m-%d %H:%M:%S')],
-            'ax': [data.get('ax')], 'ay': [data.get('ay')], 'az': [data.get('az')],
-            'gx': [data.get('gx')], 'gy': [data.get('gy')], 'gz': [data.get('gz')],
-            'temperature_c': [data.get('temperature_c')]
+            'rpm': [data.get('rpm')],
+            'ambient_temp_c': [data.get('ambient_temp_c')],
+            'fuel_level_percent': [data.get('fuel_level_percent')],
+            'sea_state': [data.get('sea_state')],
+            'az_vibration_actual': [data.get('az_vibration_actual')]
         }
+        
         df_new = pd.DataFrame(log_data)
         
         if not os.path.exists(CSV_FILE):
             df_new.to_csv(CSV_FILE, index=False, header=True)
         else:
             df_new.to_csv(CSV_FILE, mode='a', index=False, header=False)
+            
+        print(f"Logged: {log_data}")
         return "Data received successfully", 200
     except Exception as e:
+        print(f"Error: {e}")
         return "Error", 400
 
 if __name__ == '__main__':

hardware/esp32_sensor_code.ino

@@ -1,4 +1,4 @@
-// --- hardware/esp32_sensor_code.ino ---
+// --- hardware/esp32_sensor_code.ino (Regression Version) ---
 #include <WiFi.h>
 #include <HTTPClient.h>
 #include <Wire.h>
@@ -18,10 +18,39 @@ Adafruit_MPU6050 mpu;
 OneWire oneWire(ONEWIRE_BUS);
 DallasTemperature sensors(&oneWire);
 
-void connectToWiFi() { /* ... connection logic ... */ }
-void setup() { /* ... sensor and wifi init ... */ }
+// --- Variables for Simulating Conditions ---
+long lastStateChange = 0;
+int rpms[] = {800, 2500, 4000}; // Idle, Cruise, High
+int sea_states[] = {0, 1, 2}; // Calm, Choppy, Stormy
+int current_rpm = 800;
+int current_sea_state = 0;
+float current_fuel = 100.0;
+
+void setup() {
+  Serial.begin(115200);
+  // Initialize sensors and connect to WiFi (full code in previous tutorials)
+  WiFi.begin(ssid, password);
+  while (WiFi.status() != WL_CONNECTED) { delay(500); Serial.print("."); }
+  Serial.println("\nWiFi Connected");
+  mpu.begin();
+  sensors.begin();
+  
+  lastStateChange = millis();
+  randomSeed(analogRead(0)); // Seed for random numbers
+}
 
 void loop() {
+  // --- Simulate Changing Conditions every 15 seconds for varied data ---
+  if (millis() - lastStateChange > 15000) {
+    current_rpm = rpms[random(3)]; // Pick a random RPM state
+    current_sea_state = sea_states[random(3)]; // Pick a random sea state
+    current_fuel -= random(1, 5); // Simulate fuel consumption
+    if (current_fuel < 10) { current_fuel = 100; } // Refuel
+    
+    lastStateChange = millis();
+    Serial.println("--- SIMULATING NEW CONDITIONS ---");
+  }
+
   if(WiFi.status() == WL_CONNECTED) {
     HTTPClient http;
     sensors_event_t a, g, temp_event;
@@ -29,12 +58,23 @@ void loop() {
     sensors.requestTemperatures();
     float temperatureC = sensors.getTempCByIndex(0);
 
-    String jsonPayload = "{\"ax\":" + String(a.acceleration.x) + ",\"ay\":" + String(a.acceleration.y) + ",\"az\":" + String(a.acceleration.z) + ",\"gx\":" + String(g.gyro.x) + ",\"gy\":" + String(g.gyro.y) + ",\"gz\":" + String(g.gyro.z) + ",\"temperature_c\":" + String(temperatureC) + "}";
+    // --- Create JSON payload with REAL and SIMULATED data ---
+    String jsonPayload = "{";
+    jsonPayload += "\"rpm\":" + String(current_rpm) + ",";
+    // NOTE: We use the real sensor temp for 'ambient_temp_c' for this test
+    jsonPayload += "\"ambient_temp_c\":" + String(temperatureC) + ","; 
+    jsonPayload += "\"fuel_level_percent\":" + String(current_fuel) + ",";
+    jsonPayload += "\"sea_state\":" + String(current_sea_state) + ",";
+    // This is the actual, real vibration reading from the sensor
+    jsonPayload += "\"az_vibration_actual\":" + String(a.acceleration.z); 
+    jsonPayload += "}";
 
     http.begin(serverName);
     http.addHeader("Content-Type", "application/json");
     http.POST(jsonPayload);
     http.end();
+    
+    Serial.println(jsonPayload);
   }
   delay(2000);
-}
+}