Upload folder using huggingface_hub

Dockerfile

@@ -1,23 +1,16 @@
-# Use a minimal base image with Python 3.9 installed
-FROM python:3.9
+FROM python:3.10-slim
 
-# Set the working directory inside the container to /app
+# Set the working directory inside the container
 WORKDIR /app
 
-# Copy all files from the current directory on the host to the container's /app directory
+# Copy all files from the current directory to the container's working directory
 COPY . .
 
-# Install Python dependencies listed in requirements.txt
-RUN pip3 install -r requirements.txt
+# Install dependencies from the requirements file without using cache to reduce image size
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-WORKDIR $HOME/app
-
-COPY --chown=user . $HOME/app
-
-# Define the command to run the Streamlit app on port "8501" and make it accessible externally
-CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
+# Define the command to start the application using Gunicorn with 4 worker processes
+# - `-w 4`: Uses 4 worker processes for handling requests
+# - `-b 0.0.0.0:7860`: Binds the server to port 7860 on all network interfaces
+# - `app:app`: Runs the Flask app (assuming `app.py` contains the Flask instance named `app`)
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:predictive_maintenance_api"]

app.py

@@ -1,45 +1,104 @@
-import streamlit as st
-import pandas as pd
-from huggingface_hub import hf_hub_download
-import joblib
-
-# Download and load the model
-model_path = hf_hub_download(repo_id="VeerendraManikonda/predictive_maintenance", filename="best_predictive_maintenance_model_v1.joblib")
-model = joblib.load(model_path)
-
-# Streamlit UI for Machine Failure Prediction
-st.title("Predictive Maintenance for Engine Health")
-st.write("""
-This application predicts the potential Machine Failures based on the pitch parameters.
-Please enter the Machine Sensor details.
-""")
-
-# Input form
-with st.form("prediction_form"):
-    engine_rpm = st.number_input("Engine RPM", min_value=0.0, max_value=3000, value=700)
-    lub_oil_pressure = st.number_input("Lub Oil Pressure (bar/kPa)", min_value=0.0, max_value=10.0, value=2.5)
-    fuel_pressure = st.number_input("Fuel Pressure (bar/kPa)", min_value=0.0, max_value=30.0, value=12.0)
-    coolant_pressure = st.number_input("Coolant Pressure (bar/kPa)", min_value=0.0, max_value=10.0, value=3.0)
-    lub_oil_temperature = st.number_input("Lub Oil Temperature (°C)", min_value=0.0, max_value=150.0, value=85.0)
-    coolant_temperature = st.number_input("Coolant Temperature (°C)", min_value=0.0, max_value=150.0, value=80.0)
-
-    submit = st.form_submit_button("Predict")
-
-if submit:
-    # Convert inputs to DataFrame
-    input_data = pd.DataFrame([{
-        "engine_rpm": engine_rpm,
-        "lub_oil_pressure": lub_oil_pressure,
-        "fuel_pressure": fuel_pressure,
-        "coolant_pressure": coolant_pressure,
-        "lub_oil_temperature": lub_oil_temperature,
-        "coolant_temperature": coolant_temperature
-    }])
-
-    # Predict
-    prediction = model.predict(input_data)[0]
-
-    if prediction == 1:
-        st.success("Engine is likely to be failing")
-    else:
-        st.error("Engine is in good condition.")
+# Import necessary libraries
+import numpy as np
+import joblib  # For loading the serialized model
+import pandas as pd  # For data manipulation
+from flask import Flask, request, jsonify  # For creating the Flask API
+import os # For accessing environment variables if needed
+import logging # For logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Initialize the Flask application
+predictive_maintenance_api = Flask("Predictive Maintenance API")
+
+# Define the model path
+# Use the correct path as defined in the notebook
+#MODEL_PATH = "backend_files/Predictive_Maintenance_XGB_Tuned_model_v1_0.joblib"
+MODEL_PATH = "Predictive_Maintenance_XGB_Tuned_model_v1_0.joblib" # Corrected path
+
+# Load the trained machine learning model
+model = None
+try:
+    model = joblib.load(MODEL_PATH)
+    logging.info(f"Model loaded successfully from {MODEL_PATH}")
+except Exception as e:
+    logging.error(f"Error loading model: {e}")
+
+# Define a route for the home page (GET request)
+@predictive_maintenance_api.get('/')
+def home():
+    """
+    This function handles GET requests to the root URL ('/') of the API.
+    It returns a simple welcome message.
+    """
+    logging.info("Home page accessed.")
+    return "Welcome to the Predictive Maintenance API for Engine Health!"
+
+# Define an endpoint for engine condition prediction (POST request)
+@predictive_maintenance_api.post('/v1/engine_condition_prediction')
+def predict_engine_condition():
+    """
+    This function handles POST requests to the '/v1/engine_condition_prediction' endpoint.
+    It expects a JSON payload containing engine sensor data and returns
+    the predicted engine condition (0 for Normal, 1 for Faulty) as a JSON response.
+    """
+    logging.info("Prediction request received.")
+    if model is None:
+        logging.error("Model not loaded when prediction request came. Returning 500.")
+        return jsonify({"error": "Model not loaded. Please check server logs."}), 500
+
+    # Get the JSON data from the request body
+    engine_data = request.get_json()
+    logging.info(f"Received engine data: {engine_data}")
+
+    # Extract relevant features from the JSON data
+    # Features: 'Engine_RPM', 'Lub_Oil_Pressure', 'Fuel_Pressure', 'Coolant_Pressure', 'Lub_Oil_Temperature', 'Coolant_Temperature'
+    try:
+        sample_data = {
+            'Engine_RPM': [engine_data['Engine_RPM']],
+            'Lub_Oil_Pressure': [engine_data['Lub_Oil_Pressure']],
+            'Fuel_Pressure': [engine_data['Fuel_Pressure']],
+            'Coolant_Pressure': [engine_data['Coolant_Pressure']],
+            'Lub_Oil_Temperature': [engine_data['Lub_Oil_Temperature']],
+            'Coolant_Temperature': [engine_data['Coolant_Temperature']]
+        }
+        logging.debug(f"Extracted sample data: {sample_data}")
+    except KeyError as e:
+        logging.error(f"Missing data for feature: {e}. Returning 400.")
+        return jsonify({"error": f"Missing data for feature: {e}. Please provide all required sensor readings."}), 400
+    except Exception as e:
+        logging.error(f"Unexpected error during data extraction: {e}. Returning 400.")
+        return jsonify({"error": f"An unexpected error occurred during data processing: {e}"}), 400
+
+
+    # Convert the extracted data into a Pandas DataFrame
+    # Ensure the order of columns matches the training data
+    input_df = pd.DataFrame(sample_data)
+
+    # Make prediction
+    try:
+        prediction_proba = model.predict_proba(input_df)
+        predicted_class = model.predict(input_df)[0]
+        logging.info(f"Prediction made: class={predicted_class}, probabilities={prediction_proba}")
+    except Exception as e:
+        logging.error(f"Error during model prediction: {e}. Returning 500.")
+        return jsonify({"error": f"Error during model prediction: {e}"}), 500
+
+    # Map the predicted class to a readable string
+    condition_map = {0: "Normal", 1: "Faulty"}
+    predicted_condition_str = condition_map.get(predicted_class, "Unknown")
+
+    # Return the prediction as a JSON response
+    return jsonify({
+        "predicted_engine_condition_class": int(predicted_class), # Ensure it's a standard Python int
+        "predicted_engine_condition_label": predicted_condition_str,
+        "probability_normal": round(float(prediction_proba[0][0]), 4),
+        "probability_faulty": round(float(prediction_proba[0][1]), 4)
+    })
+
+# To run the Flask app (for local testing)
+if __name__ == '__main__':
+    # You might want to get the port from an environment variable for deployment
+    port = int(os.environ.get('PORT', 5000))
+    predictive_maintenance_api.run(host='0.0.0.0', port=port, debug=True)

requirements.txt

@@ -1,7 +1,10 @@
 pandas==2.2.2
-huggingface_hub==0.32.6
-streamlit==1.43.2
-joblib==1.5.1
-scikit-learn==1.6.0
+numpy==2.0.2
+scikit-learn==1.6.1
 xgboost==2.1.4
-mlflow==3.0.1
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.28.1
+uvicorn[standard]