Upload folder using huggingface_hub

Dockerfile

@@ -1,16 +1,23 @@
-FROM python:3.10-slim
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9
 
-# Set the working directory inside the container
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-# Copy all files from the current directory to the container's working directory
+# Copy all files from the current directory on the host to the container's /app directory
 COPY . .
 
-# Install dependencies from the requirements file without using cache to reduce image size
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
 
-# 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"]
+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"]

app.py

@@ -1,104 +1,45 @@
-# 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)
+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.")

requirements.txt

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