Deploy predictive maintenance Streamlit application

Dockerfile

@@ -0,0 +1,15 @@
+FROM python:3.10-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+COPY requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir --upgrade pip &&     pip install --no-cache-dir -r /app/requirements.txt
+
+COPY app.py /app/app.py
+
+EXPOSE 7860
+
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

app.py

@@ -0,0 +1,82 @@
+import os
+import joblib
+import pandas as pd
+import streamlit as st
+from huggingface_hub import hf_hub_download
+
+HF_MODEL_REPO_ID = os.getenv("HF_MODEL_REPO_ID", "premswan/engine-predictive-maintenance-model")
+MODEL_FILENAME = "best_engine_maintenance_model.joblib"
+FEATURE_COLUMNS = ["Engine_RPM", "Lub_Oil_Pressure", "Fuel_Pressure", "Coolant_Pressure", "Lub_Oil_Temperature", "Coolant_Temperature"]
+
+LABEL_MAP = {
+    0: "Normal / Healthy",
+    1: "Maintenance Required / Faulty"
+}
+
+
+@st.cache_resource
+def load_model():
+    # Download and load the trained model from Hugging Face Model Hub.
+    token = os.getenv("HF_TOKEN")
+    model_path = hf_hub_download(
+        repo_id=HF_MODEL_REPO_ID,
+        filename=MODEL_FILENAME,
+        token=token
+    )
+    return joblib.load(model_path)
+
+
+MODEL = load_model()
+
+st.set_page_config(
+    page_title="Engine Predictive Maintenance",
+    page_icon="🔧",
+    layout="wide"
+)
+
+st.title("Engine Predictive Maintenance Classifier")
+st.write(
+    "Enter engine sensor readings. The app loads the registered model from "
+    "Hugging Face Model Hub and predicts whether maintenance is required."
+)
+
+with st.form("prediction_form"):
+    st.subheader("Sensor Inputs")
+    sensor_values = {}
+    cols = st.columns(2)
+    for idx, feature in enumerate(FEATURE_COLUMNS):
+        with cols[idx % 2]:
+            sensor_values[feature] = st.number_input(
+                label=feature,
+                value=0.0,
+                format="%.6f"
+            )
+
+    submitted = st.form_submit_button("Predict Engine Condition")
+
+if submitted:
+    # Rubric requirement: get inputs and save them into a DataFrame.
+    input_df = pd.DataFrame([sensor_values], columns=FEATURE_COLUMNS)
+
+    prediction = int(MODEL.predict(input_df)[0])
+
+    if hasattr(MODEL, "predict_proba"):
+        probability_maintenance = float(MODEL.predict_proba(input_df)[0, 1])
+    else:
+        probability_maintenance = None
+
+    prediction_label = LABEL_MAP.get(prediction, str(prediction))
+
+    st.subheader("Prediction Output")
+    st.metric("Predicted Engine Condition", prediction_label)
+
+    if probability_maintenance is not None:
+        st.metric("Probability of Maintenance/Faulty Class", "%.4f" % probability_maintenance)
+
+    if prediction == 1:
+        st.error("Recommended action: Schedule inspection or preventive maintenance before continued operation.")
+    else:
+        st.success("Recommended action: Continue normal operation and keep monitoring sensor readings.")
+
+    st.subheader("Input DataFrame Used for Inference")
+    st.dataframe(input_df, use_container_width=True)

hf_streamlit_space/Dockerfile

@@ -0,0 +1,15 @@
+FROM python:3.10-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+COPY requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir --upgrade pip &&     pip install --no-cache-dir -r /app/requirements.txt
+
+COPY . /app
+EXPOSE 8501
+
+HEALTHCHECK CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8501/_stcore/health')" || exit 1
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

hf_streamlit_space/README.md

@@ -0,0 +1,30 @@
+---
+title: Engine Predictive Maintenance
+emoji: W
+colorFrom: blue
+colorTo: green
+sdk: streamlit
+sdk_version: 1.37.0
+app_file: app.py
+pinned: false
+---
+
+# Engine Predictive Maintenance Streamlit App
+
+This Hugging Face Space hosts a Streamlit application for the predictive maintenance model.
+
+## What the app does
+
+1. Loads the trained model from `premswan/engine-predictive-maintenance-model`.
+2. Accepts engine sensor inputs.
+3. Converts the inputs into a pandas dataframe.
+4. Predicts whether the engine is normal or requires maintenance.
+5. Displays the predicted class and maintenance probability, if available.
+
+## Model repository
+
+`premswan/engine-predictive-maintenance-model`
+
+## Dataset repository
+
+`premswan/engine-predictive-maintenance-data`

hf_streamlit_space/app.py

@@ -0,0 +1,71 @@
+import json
+import joblib
+import pandas as pd
+import streamlit as st
+from huggingface_hub import hf_hub_download
+
+DEFAULT_MODEL_REPO_ID = "premswan/engine-predictive-maintenance-model"
+MODEL_FILE = "best_engine_maintenance_model.joblib"
+METADATA_FILE = "model_metadata.json"
+
+st.set_page_config(page_title="Engine Predictive Maintenance", page_icon="W", layout="centered")
+
+@st.cache_resource
+def load_model_and_metadata(model_repo_id: str = DEFAULT_MODEL_REPO_ID):
+    # Load model and metadata from Hugging Face Model Hub.
+    model_path = hf_hub_download(repo_id=model_repo_id, filename=MODEL_FILE)
+    metadata_path = hf_hub_download(repo_id=model_repo_id, filename=METADATA_FILE)
+    model = joblib.load(model_path)
+    with open(metadata_path, "r", encoding="utf-8") as file:
+        metadata = json.load(file)
+    return model, metadata
+
+model, metadata = load_model_and_metadata()
+feature_columns = metadata.get("feature_columns", ['Engine_RPM', 'Lub_Oil_Pressure', 'Fuel_Pressure', 'Coolant_Pressure', 'Lub_Oil_Temperature', 'Coolant_Temperature'])
+
+st.title("Engine Predictive Maintenance App")
+st.write("Enter engine sensor readings to predict whether the engine is normal or needs maintenance attention.")
+st.subheader("Sensor Inputs")
+
+default_values = {
+    "Engine_RPM": 800.0,
+    "Lub_Oil_Pressure": 3.2,
+    "Fuel_Pressure": 6.5,
+    "Coolant_Pressure": 2.4,
+    "Lub_Oil_Temperature": 78.0,
+    "Coolant_Temperature": 80.0,
+}
+
+user_inputs = {}
+for feature in feature_columns:
+    user_inputs[feature] = st.number_input(
+        label=feature,
+        value=float(default_values.get(feature, 0.0)),
+        step=0.1,
+        format="%.4f"
+    )
+
+# Save inputs into a dataframe as required by the deployment rubric.
+input_df = pd.DataFrame([user_inputs], columns=feature_columns)
+st.subheader("Input DataFrame")
+st.dataframe(input_df, use_container_width=True)
+
+if st.button("Predict Engine Condition"):
+    prediction = int(model.predict(input_df)[0])
+    probability_maintenance = None
+    if hasattr(model, "predict_proba"):
+        probability_maintenance = float(model.predict_proba(input_df)[0, 1])
+
+    if prediction == 1:
+        st.error("Prediction: Maintenance / Faulty condition")
+        st.write("Recommended action: inspect engine health and schedule preventive maintenance.")
+    else:
+        st.success("Prediction: Normal / Healthy condition")
+        st.write("Recommended action: continue normal monitoring.")
+
+    if probability_maintenance is not None:
+        st.metric("Maintenance Probability", f"{probability_maintenance:.2%}")
+
+    st.write("Raw prediction output:", prediction)
+
+st.caption("Model loaded from Hugging Face Model Hub: " + DEFAULT_MODEL_REPO_ID)

hf_streamlit_space/deployment_config.json

@@ -0,0 +1,19 @@
+{
+  "model_repo_id": "premswan/engine-predictive-maintenance-model",
+  "space_repo_id": "premswan/engine-predictive-maintenance-streamlit",
+  "dataset_repo_id": "premswan/engine-predictive-maintenance-data",
+  "app_file": "app.py",
+  "docker_base_image": "python:3.10-slim",
+  "streamlit_port": 8501,
+  "feature_columns": [
+    "Engine_RPM",
+    "Lub_Oil_Pressure",
+    "Fuel_Pressure",
+    "Coolant_Pressure",
+    "Lub_Oil_Temperature",
+    "Coolant_Temperature"
+  ],
+  "target_column": "Engine_Condition",
+  "model_file": "best_engine_maintenance_model.joblib",
+  "metadata_file": "model_metadata.json"
+}

hf_streamlit_space/push_to_hf_space.py

@@ -0,0 +1,16 @@
+import os
+from pathlib import Path
+from huggingface_hub import HfApi, login
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+HF_SPACE_ID = os.getenv("HF_SPACE_ID", "premswan/engine-predictive-maintenance-streamlit")
+SPACE_FOLDER = Path(__file__).resolve().parent
+
+if not HF_TOKEN:
+    raise ValueError("HF_TOKEN environment variable is required to push the Hugging Face Space.")
+
+login(token=HF_TOKEN, add_to_git_credential=True)
+api = HfApi(token=HF_TOKEN)
+api.create_repo(repo_id=HF_SPACE_ID, repo_type="space", space_sdk="streamlit", exist_ok=True, private=False, token=HF_TOKEN)
+api.upload_folder(folder_path=str(SPACE_FOLDER), repo_id=HF_SPACE_ID, repo_type="space", path_in_repo=".", commit_message="Deploy Streamlit predictive maintenance app", token=HF_TOKEN)
+print(f"Deployment completed: https://huggingface.co/spaces/{HF_SPACE_ID}")

hf_streamlit_space/requirements.txt

@@ -0,0 +1,7 @@
+streamlit
+pandas
+numpy
+scikit-learn
+xgboost
+joblib
+huggingface_hub

push_to_hf_space.py

@@ -0,0 +1,32 @@
+import os
+from pathlib import Path
+from huggingface_hub import HfApi
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+SPACE_REPO_ID = os.getenv("HF_SPACE_REPO_ID", "premswan/engine-predictive-maintenance-space")
+DEPLOYMENT_DIR = Path(__file__).resolve().parent
+
+if not HF_TOKEN:
+    raise ValueError("HF_TOKEN environment variable is required to push files to Hugging Face Space.")
+
+api = HfApi(token=HF_TOKEN)
+
+api.create_repo(
+    repo_id=SPACE_REPO_ID,
+    repo_type="space",
+    space_sdk="docker",
+    exist_ok=True,
+    private=False,
+    token=HF_TOKEN
+)
+
+api.upload_folder(
+    folder_path=str(DEPLOYMENT_DIR),
+    repo_id=SPACE_REPO_ID,
+    repo_type="space",
+    path_in_repo=".",
+    commit_message="Deploy predictive maintenance Streamlit app",
+    token=HF_TOKEN
+)
+
+print(f"Deployment files pushed to: https://huggingface.co/spaces/{SPACE_REPO_ID}")

requirements.txt

@@ -0,0 +1,7 @@
+streamlit
+pandas
+numpy
+scikit-learn
+joblib
+huggingface_hub
+xgboost

streamlit_hf_space/.streamlit/config.toml

@@ -0,0 +1,7 @@
+[server]
+headless = true
+enableCORS = false
+enableXsrfProtection = false
+
+[browser]
+gatherUsageStats = false

streamlit_hf_space/Dockerfile

@@ -0,0 +1,17 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+ENV STREAMLIT_SERVER_PORT=7860
+ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
+
+COPY requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir --upgrade pip &&     pip install --no-cache-dir -r /app/requirements.txt
+
+COPY . /app
+
+EXPOSE 7860
+
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

streamlit_hf_space/README.md

@@ -0,0 +1,31 @@
+---
+title: Engine Predictive Maintenance
+emoji: 🛠️
+colorFrom: blue
+colorTo: green
+sdk: streamlit
+sdk_version: 1.35.0
+app_file: app.py
+pinned: false
+---
+
+# Engine Predictive Maintenance Streamlit App
+
+This Streamlit app loads the registered model from `premswan/engine-predictive-maintenance-model` and predicts whether an engine is operating normally or may require maintenance.
+
+## Inputs
+
+The app accepts these sensor readings:
+
+- `Engine_RPM`
+- `Lub_Oil_Pressure`
+- `Fuel_Pressure`
+- `Coolant_Pressure`
+- `Lub_Oil_Temperature`
+- `Coolant_Temperature`
+
+## Output
+
+- Predicted engine condition
+- Maintenance probability, when supported by the model
+- Operational recommendation

streamlit_hf_space/app.py

@@ -0,0 +1,95 @@
+import os
+import joblib
+import pandas as pd
+import streamlit as st
+from huggingface_hub import hf_hub_download
+
+# ------------------------------------------------------------
+# Deployment configuration
+# ------------------------------------------------------------
+# MODEL_REPO_ID can be overridden in Hugging Face Space secrets/variables.
+MODEL_REPO_ID = os.getenv("MODEL_REPO_ID", "premswan/engine-predictive-maintenance-model")
+MODEL_FILENAME = "best_engine_maintenance_model.joblib"
+FEATURE_COLUMNS = [
+    "Engine_RPM",
+    "Lub_Oil_Pressure",
+    "Fuel_Pressure",
+    "Coolant_Pressure",
+    "Lub_Oil_Temperature",
+    "Coolant_Temperature"
+]
+FEATURE_DEFAULTS = {
+    "Engine_RPM": 800.0,
+    "Lub_Oil_Pressure": 3.2,
+    "Fuel_Pressure": 6.5,
+    "Coolant_Pressure": 2.4,
+    "Lub_Oil_Temperature": 78.0,
+    "Coolant_Temperature": 80.0
+}
+FEATURE_HELP = {
+    "Engine_RPM": "Engine speed in revolutions per minute.",
+    "Lub_Oil_Pressure": "Lubricating oil pressure reading.",
+    "Fuel_Pressure": "Fuel pressure reading.",
+    "Coolant_Pressure": "Coolant pressure reading.",
+    "Lub_Oil_Temperature": "Lubricating oil temperature in Celsius.",
+    "Coolant_Temperature": "Coolant temperature in Celsius."
+}
+
+st.set_page_config(
+    page_title="Engine Predictive Maintenance",
+    page_icon="🛠️",
+    layout="centered"
+)
+
+@st.cache_resource
+def load_model():
+    # Load the registered model from Hugging Face Model Hub.
+    token = os.getenv("HF_TOKEN")
+    model_path = hf_hub_download(
+        repo_id=MODEL_REPO_ID,
+        filename=MODEL_FILENAME,
+        token=token
+    )
+    return joblib.load(model_path)
+
+st.title("Engine Predictive Maintenance")
+st.write(
+    "Enter engine sensor readings to predict whether the engine is operating normally "
+    "or may require maintenance."
+)
+
+model = load_model()
+
+# ------------------------------------------------------------
+# Capture input readings and save them into a dataframe
+# ------------------------------------------------------------
+input_values = {}
+for feature in FEATURE_COLUMNS:
+    input_values[feature] = st.number_input(
+        label=feature,
+        value=float(FEATURE_DEFAULTS.get(feature, 0.0)),
+        help=FEATURE_HELP.get(feature, "Enter sensor value")
+    )
+
+input_df = pd.DataFrame([input_values], columns=FEATURE_COLUMNS)
+input_df.to_csv("latest_input.csv", index=False)
+
+st.subheader("Input DataFrame")
+st.dataframe(input_df, use_container_width=True)
+
+if st.button("Predict Engine Condition"):
+    prediction = int(model.predict(input_df)[0])
+
+    probability_maintenance = None
+    if hasattr(model, "predict_proba"):
+        probability_maintenance = float(model.predict_proba(input_df)[0, 1])
+
+    if prediction == 1:
+        st.error("Prediction: Maintenance / Faulty condition")
+        st.write("Recommended action: inspect the engine before continuing heavy operation.")
+    else:
+        st.success("Prediction: Normal / Healthy condition")
+        st.write("Recommended action: continue normal monitoring.")
+
+    if probability_maintenance is not None:
+        st.metric("Maintenance Probability", f"{probability_maintenance:.2%}")

streamlit_hf_space/deployment_config.json

@@ -0,0 +1,23 @@
+{
+  "space_repo_id": "premswan/engine-predictive-maintenance-streamlit",
+  "model_repo_id": "premswan/engine-predictive-maintenance-model",
+  "model_filename": "best_engine_maintenance_model.joblib",
+  "sdk": "streamlit",
+  "port": 7860,
+  "feature_columns": [
+    "Engine_RPM",
+    "Lub_Oil_Pressure",
+    "Fuel_Pressure",
+    "Coolant_Pressure",
+    "Lub_Oil_Temperature",
+    "Coolant_Temperature"
+  ],
+  "runtime_files": [
+    "app.py",
+    "requirements.txt",
+    "Dockerfile",
+    ".streamlit/config.toml",
+    "README.md",
+    "push_to_hf_space.py"
+  ]
+}

streamlit_hf_space/push_to_hf_space.py

@@ -0,0 +1,33 @@
+import os
+from pathlib import Path
+from huggingface_hub import HfApi, login
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+SPACE_REPO_ID = os.getenv("SPACE_REPO_ID", "premswan/engine-predictive-maintenance-streamlit")
+SPACE_DIR = Path(__file__).resolve().parent
+
+if not HF_TOKEN:
+    raise RuntimeError("HF_TOKEN environment variable is required to push the Hugging Face Space.")
+
+login(token=HF_TOKEN, add_to_git_credential=True)
+api = HfApi(token=HF_TOKEN)
+
+api.create_repo(
+    repo_id=SPACE_REPO_ID,
+    repo_type="space",
+    space_sdk="streamlit",
+    exist_ok=True,
+    private=False,
+    token=HF_TOKEN
+)
+
+api.upload_folder(
+    folder_path=str(SPACE_DIR),
+    repo_id=SPACE_REPO_ID,
+    repo_type="space",
+    path_in_repo=".",
+    commit_message="Deploy Streamlit predictive maintenance app",
+    token=HF_TOKEN
+)
+
+print(f"Space deployed: https://huggingface.co/spaces/{SPACE_REPO_ID}")

streamlit_hf_space/requirements.txt

@@ -0,0 +1,7 @@
+streamlit
+pandas
+numpy
+scikit-learn
+xgboost
+joblib
+huggingface_hub

streamlit_hf_space/sample_input.csv

@@ -0,0 +1,2 @@
+Engine_RPM,Lub_Oil_Pressure,Fuel_Pressure,Coolant_Pressure,Lub_Oil_Temperature,Coolant_Temperature
+800.0,3.2,6.5,2.4,78.0,80.0