Deploy from GitHub Actions

.devcontainer/devcontainer.json

@@ -0,0 +1,17 @@
+{
+	"name": "Predictive Maintenance MVP",
+	"image": "mcr.microsoft.com/devcontainers/python:3.10-bullseye",
+	"customizations": {
+		"vscode": {
+			"extensions": [
+				"ms-python.python",
+				"ms-python.vscode-pylance",
+				"ms-toolsai.jupyter",
+				"vsciot-vscode.vscode-arduino"
+			]
+		}
+	},
+	"forwardPorts": [5000, 7860],
+	"postCreateCommand": "pip install --user -r requirements.txt",
+	"remoteUser": "vscode"
+}

.gitattributes

@@ -1,35 +0,0 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,121 @@
+Creative Commons Legal Code
+
+CC0 1.0 Universal
+
+    CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE
+    LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN
+    ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS
+    INFORMATION ON AN "AS-IS" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES
+    REGARDING THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS
+    PROVIDED HEREUNDER, AND DISCLAIMS LIABILITY FOR DAMAGES RESULTING FROM
+    THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED
+    HEREUNDER.
+
+Statement of Purpose
+
+The laws of most jurisdictions throughout the world automatically confer
+exclusive Copyright and Related Rights (defined below) upon the creator
+and subsequent owner(s) (each and all, an "owner") of an original work of
+authorship and/or a database (each, a "Work").
+
+Certain owners wish to permanently relinquish those rights to a Work for
+the purpose of contributing to a commons of creative, cultural and
+scientific works ("Commons") that the public can reliably and without fear
+of later claims of infringement build upon, modify, incorporate in other
+works, reuse and redistribute as freely as possible in any form whatsoever
+and for any purposes, including without limitation commercial purposes.
+These owners may contribute to the Commons to promote the ideal of a free
+culture and the further production of creative, cultural and scientific
+works, or to gain reputation or greater distribution for their Work in
+part through the use and efforts of others.
+
+For these and/or other purposes and motivations, and without any
+expectation of additional consideration or compensation, the person
+associating CC0 with a Work (the "Affirmer"), to the extent that he or she
+is an owner of Copyright and Related Rights in the Work, voluntarily
+elects to apply CC0 to the Work and publicly distribute the Work under its
+terms, with knowledge of his or her Copyright and Related Rights in the
+Work and the meaning and intended legal effect of CC0 on those rights.
+
+1. Copyright and Related Rights. A Work made available under CC0 may be
+protected by copyright and related or neighboring rights ("Copyright and
+Related Rights"). Copyright and Related Rights include, but are not
+limited to, the following:
+
+  i. the right to reproduce, adapt, distribute, perform, display,
+     communicate, and translate a Work;
+ ii. moral rights retained by the original author(s) and/or performer(s);
+iii. publicity and privacy rights pertaining to a person's image or
+     likeness depicted in a Work;
+ iv. rights protecting against unfair competition in regards to a Work,
+     subject to the limitations in paragraph 4(a), below;
+  v. rights protecting the extraction, dissemination, use and reuse of data
+     in a Work;
+ vi. database rights (such as those arising under Directive 96/9/EC of the
+     European Parliament and of the Council of 11 March 1996 on the legal
+     protection of databases, and under any national implementation
+     thereof, including any amended or successor version of such
+     directive); and
+vii. other similar, equivalent or corresponding rights throughout the
+     world based on applicable law or treaty, and any national
+     implementations thereof.
+
+2. Waiver. To the greatest extent permitted by, but not in contravention
+of, applicable law, Affirmer hereby overtly, fully, permanently,
+irrevocably and unconditionally waives, abandons, and surrenders all of
+Affirmer's Copyright and Related Rights and associated claims and causes
+of action, whether now known or unknown (including existing as well as
+future claims and causes of action), in the Work (i) in all territories
+worldwide, (ii) for the maximum duration provided by applicable law or
+treaty (including future time extensions), (iii) in any current or future
+medium and for any number of copies, and (iv) for any purpose whatsoever,
+including without limitation commercial, advertising or promotional
+purposes (the "Waiver"). Affirmer makes the Waiver for the benefit of each
+member of the public at large and to the detriment of Affirmer's heirs and
+successors, fully intending that such Waiver shall not be subject to
+revocation, rescission, cancellation, termination, or any other legal or
+equitable action to disrupt the quiet enjoyment of the Work by the public
+as contemplated by Affirmer's express Statement of Purpose.
+
+3. Public License Fallback. Should any part of the Waiver for any reason
+be judged legally invalid or ineffective under applicable law, then the
+Waiver shall be preserved to the maximum extent permitted taking into
+account Affirmer's express Statement of Purpose. In addition, to the
+extent the Waiver is so judged Affirmer hereby grants to each affected
+person a royalty-free, non transferable, non sublicensable, non exclusive,
+irrevocable and unconditional license to exercise Affirmer's Copyright and
+Related Rights in the Work (i) in all territories worldwide, (ii) for the
+maximum duration provided by applicable law or treaty (including future
+time extensions), (iii) in any current or future medium and for any number
+of copies, and (iv) for any purpose whatsoever, including without
+limitation commercial, advertising or promotional purposes (the
+"License"). The License shall be deemed effective as of the date CC0 was
+applied by Affirmer to the Work. Should any part of the License for any
+reason be judged legally invalid or ineffective under applicable law, such
+partial invalidity or ineffectiveness shall not invalidate the remainder
+of the License, and in such case Affirmer hereby affirms that he or she
+will not (i) exercise any of his or her remaining Copyright and Related
+Rights in the Work or (ii) assert any associated claims and causes of
+action with respect to the Work, in either case contrary to Affirmer's
+express Statement of Purpose.
+
+4. Limitations and Disclaimers.
+
+ a. No trademark or patent rights held by Affirmer are waived, abandoned,
+    surrendered, licensed or otherwise affected by this document.
+ b. Affirmer offers the Work as-is and makes no representations or
+    warranties of any kind concerning the Work, express, implied,
+    statutory or otherwise, including without limitation warranties of
+    title, merchantability, fitness for a particular purpose, non
+    infringement, or the absence of latent or other defects, accuracy, or
+    the present or absence of errors, whether or not discoverable, all to
+    the greatest extent permissible under applicable law.
+ c. Affirmer disclaims responsibility for clearing rights of other persons
+    that may apply to the Work or any use thereof, including without
+    limitation any person's Copyright and Related Rights in the Work.
+    Further, Affirmer disclaims responsibility for obtaining any necessary
+    consents, permissions or other rights required for any use of the
+    Work.
+ d. Affirmer understands and acknowledges that Creative Commons is not a
+    party to this document and has no duty or obligation with respect to
+    this CC0 or use of the Work.

README.md

@@ -1,14 +1,9 @@
----
-title: Enginevibration
-emoji: 😻
+title: Predictive Maintenance Detector
+emoji: 🚨
 colorFrom: red
-colorTo: green
+colorTo: yellow
 sdk: gradio
-sdk_version: 5.39.0
+sdk_version: 3.41.2
 app_file: app.py
 pinned: false
-license: cc0-1.0
-short_description: deploys the mvp for engine vibration for zodiac boat engine
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---

app.py

@@ -0,0 +1,89 @@
+# --- app.py (Regression Version) ---
+import gradio as gr
+import numpy as np
+import pandas as pd
+import os
+import xgboost as xgb
+
+# --- Load Model and Threshold ---
+base_dir = os.path.dirname(os.path.abspath(__file__))
+model_path = os.path.join(base_dir, 'models', 'vibration_regressor.json')
+threshold_path = os.path.join(base_dir, 'models', 'regression_anomaly_threshold.npy')
+
+try:
+    model = xgb.XGBRegressor()
+    model.load_model(model_path)
+    anomaly_threshold = np.load(threshold_path)
+    print("Model and threshold loaded successfully.")
+except Exception as e:
+    print(f"CRITICAL ERROR: Could not load files. Details: {e}")
+    model = None
+    anomaly_threshold = None
+
+description = """
+<div align="center">
+  <img src="https://images.pexels.com/photos/6510296/pexels-photo-6510296.jpeg" width="600px" />
+</div>
+**Zodiac 1.0: Regression-Based Engine Diagnostics.**
+This advanced model predicts the **normal** vibration for your engine based on its current operating conditions. Enter the conditions and the actual vibration reading from your sensor to check for anomalies.
+"""
+
+def predict_anomaly(rpm, temp, fuel, sea_state, actual_vibration):
+    """Predicts if the actual vibration is an anomaly given the conditions."""
+    if model is None:
+        return "Error: Model not loaded.", "", ""
+
+    try:
+        # Map sea state string to integer
+        sea_state_map = {"Calm": 0, "Choppy": 1, "Stormy": 2}
+        sea_state_int = sea_state_map.get(sea_state, 0)
+        
+        # Create a DataFrame with the exact feature names the model was trained on
+        input_data = pd.DataFrame([{
+            'rpm': rpm,
+            'ambient_temp_c': temp,
+            'fuel_level_percent': fuel,
+            'sea_state': sea_state_int
+        }])
+        
+        # Predict the expected normal vibration
+        predicted_vibration = model.predict(input_data)[0]
+        
+        # Calculate the error
+        error = abs(actual_vibration - predicted_vibration)
+        
+        status = "🚨 ANOMALY DETECTED 🚨" if error > anomaly_threshold else "✅ Machine State: Normal"
+        
+        details = f"Predicted Normal Vibration: {predicted_vibration:.2f}\n"
+        details += f"Actual Measured Vibration: {actual_vibration:.2f}\n"
+        details += f"Calculated Error: {error:.4f}\n"
+        details += f"Anomaly Threshold: {anomaly_threshold:.4f}"
+        
+        return status, details
+    except Exception as e:
+        return f"An error occurred: {e}", ""
+
+# --- Create the Gradio Interface with sliders and dropdowns for conditions ---
+demo = gr.Interface(
+    fn=predict_anomaly,
+    inputs=[
+        gr.Slider(800, 5000, value=2500, label="Engine RPM"),
+        gr.Slider(25, 40, value=32, label="Ambient Temperature (°C)"),
+        gr.Slider(0, 100, value=75, label="Fuel Level (%)"),
+        gr.Dropdown(["Calm", "Choppy", "Stormy"], value="Calm", label="Current Sea State"),
+        gr.Number(label="Actual Measured Vibration (from your sensor)")
+    ],
+    outputs=[
+        gr.Textbox(label="Prediction Status"),
+        gr.Textbox(label="Analysis Details")
+    ],
+    title="Zodiac 1.0 Advanced Engine Diagnostics",
+    description=description,
+    theme=gr.themes.Soft()
+)
+
+if __name__ == "__main__":
+    if model is not None:
+        demo.launch()
+    else:
+        print("Gradio app will not launch because the model could not be loaded.")

data_logger.py

@@ -0,0 +1,37 @@
+# --- data_logger.py ---
+from flask import Flask, request
+import pandas as pd
+import os
+from datetime import datetime
+
+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()
+        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')]
+        }
+        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)
+        return "Data received successfully", 200
+    except Exception as e:
+        return "Error", 400
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000, debug=True)

generate_data.py

@@ -0,0 +1,52 @@
+# --- generate_data.py (Regression Version) ---
+import pandas as pd
+import numpy as np
+import os
+from datetime import datetime, timedelta
+
+def generate_data(filename="raw_data.csv", total_rows=10000):
+    """
+    Generates a dataset where vibration is a function of multiple operating conditions.
+    """
+    data_dir = 'data'
+    os.makedirs(data_dir, exist_ok=True)
+    filepath = os.path.join(data_dir, filename)
+
+    print(f"Generating regression dataset with {total_rows} rows...")
+    
+    # Simulate different conditions
+    rpms = np.random.randint(800, 4500, total_rows)
+    ambient_temps = np.random.uniform(28, 35, total_rows) # Tropical temps
+    fuel_levels = np.random.uniform(10, 100, total_rows) # Percentage
+    # 0=Calm, 1=Choppy, 2=Stormy
+    sea_states = np.random.choice([0, 1, 2], total_rows, p=[0.6, 0.3, 0.1])
+
+    # --- The Core Logic: Vibration is a function of conditions ---
+    # Base vibration increases with the square of RPM
+    base_vibration = 9.8 + (rpms / 1000)**2 
+    
+    # Temperature adds a small linear effect
+    temp_effect = (ambient_temps - 28) * 0.05
+    
+    # Sea state adds random noise
+    sea_effect = np.random.normal(0, sea_states * 0.2)
+    
+    # A developing fault adds a slow, growing trend over time
+    fault_trend = np.linspace(0, 1, total_rows) * 3.0
+    
+    # Combine all effects to get the final measured vibration
+    az_vibration = base_vibration + temp_effect + sea_effect + fault_trend
+
+    df = pd.DataFrame({
+        'rpm': rpms,
+        'ambient_temp_c': ambient_temps,
+        'fuel_level_percent': fuel_levels,
+        'sea_state': sea_states,
+        'az_vibration_actual': az_vibration
+    })
+    
+    df.to_csv(filepath, index=False)
+    print(f"Successfully generated and saved data to '{filepath}'")
+
+if __name__ == "__main__":
+    generate_data()

hardware/esp32_sensor_code.ino

@@ -0,0 +1,40 @@
+// --- hardware/esp32_sensor_code.ino ---
+#include <WiFi.h>
+#include <HTTPClient.h>
+#include <Wire.h>
+#include <Adafruit_MPU6050.h>
+#include <Adafruit_Sensor.h>
+#include <OneWire.h>
+#include <DallasTemperature.h>
+
+// --- Configuration ---
+const char* ssid = "YOUR_WIFI_SSID";
+const char* password = "YOUR_WIFI_PASSWORD";
+String serverName = "https://your-codespace-url-5000.app.github.dev/data";
+#define ONEWIRE_BUS 4
+
+// --- Global Objects ---
+Adafruit_MPU6050 mpu;
+OneWire oneWire(ONEWIRE_BUS);
+DallasTemperature sensors(&oneWire);
+
+void connectToWiFi() { /* ... connection logic ... */ }
+void setup() { /* ... sensor and wifi init ... */ }
+
+void loop() {
+  if(WiFi.status() == WL_CONNECTED) {
+    HTTPClient http;
+    sensors_event_t a, g, temp_event;
+    mpu.getEvent(&a, &g, &temp_event);
+    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) + "}";
+
+    http.begin(serverName);
+    http.addHeader("Content-Type", "application/json");
+    http.POST(jsonPayload);
+    http.end();
+  }
+  delay(2000);
+}

requirements.txt

@@ -0,0 +1,5 @@
+pandas
+numpy
+scikit-learn
+gradio
+xgboost

train_model.py

@@ -0,0 +1,68 @@
+# --- train_model.py (Regression Version) ---
+import pandas as pd
+import numpy as np
+import os
+import xgboost as xgb
+from sklearn.model_selection import train_test_split
+
+def main():
+    """Trains an XGBoost model to predict normal vibration."""
+    print("--- Starting Regression Model Training ---")
+    
+    data_path = os.path.join('data', 'raw_data.csv')
+    if not os.path.exists(data_path):
+        print(f"Error: {data_path} not found. Please run generate_data.py")
+        return
+
+    df = pd.read_csv(data_path)
+    df.dropna(inplace=True)
+
+    # --- Feature & Target Definition ---
+    # The model will learn to predict the 'actual' vibration based on the conditions
+    features = ['rpm', 'ambient_temp_c', 'fuel_level_percent', 'sea_state']
+    target = 'az_vibration_actual'
+
+    X = df[features]
+    y = df[target]
+
+    # --- Model Training ---
+    # We will train on the 'healthy' portion of the data (first 60%)
+    # This teaches the model what "normal" looks like before the fault gets bad
+    healthy_cutoff = int(len(df) * 0.6)
+    X_train = X.iloc[:healthy_cutoff]
+    y_train = y.iloc[:healthy_cutoff]
+
+    # Initialize and train the XGBoost Regressor
+    model = xgb.XGBRegressor(
+        objective='reg:squarederror',
+        n_estimators=1000,
+        learning_rate=0.05,
+        max_depth=5,
+        subsample=0.8,
+        colsample_bytree=0.8,
+        random_state=42,
+        n_jobs=-1 # Use all available CPU cores
+    )
+    
+    print("\nStarting model training...")
+    model.fit(X_train, y_train, verbose=False)
+    
+    # --- Save Model and Calculate Anomaly Threshold ---
+    model_dir = 'models'
+    os.makedirs(model_dir, exist_ok=True)
+    model.save_model(os.path.join(model_dir, 'vibration_regressor.json'))
+    print(f"\nModel saved to: {model_dir}/vibration_regressor.json")
+
+    # The key step: Use the trained model to predict what the vibration *should* have been
+    # for the healthy data, then find the error.
+    y_pred_healthy = model.predict(X_train)
+    errors = np.abs(y_train - y_pred_healthy)
+    
+    # The anomaly threshold is a level of error that is unusual for a healthy machine
+    anomaly_threshold = np.percentile(errors, 99) * 1.5 # 99th percentile, with a buffer
+    
+    np.save(os.path.join(model_dir, 'regression_anomaly_threshold.npy'), anomaly_threshold)
+    print(f"Anomaly threshold ({anomaly_threshold:.4f}) saved to: {model_dir}/regression_anomaly_threshold.npy")
+
+if __name__ == "__main__":
+    main()