Upload folder using huggingface_hub

DockerFile

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+
+# Use a Python base image
+FROM python:3.9-slim
+
+# Set the working directory
+WORKDIR /app
+
+# Copy the requirements file and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the application script
+COPY app.py .
+
+# Expose the port Streamlit runs on
+EXPOSE 8501
+
+# Set the entry point to run the Streamlit application
+ENTRYPOINT ["streamlit", "run"]
+CMD ["app.py"]

app.py

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+
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+from huggingface_hub import hf_hub_download
+import os
+
+
+MODEL_REPO_ID = "RajendrakumarPachaiappan/engine-predictive-model"
+MODEL_FILE = "final_random_forest_model.joblib" 
+SCALER_FILE = "standard_scaler.joblib"
+
+FEATURE_COLS = ['Engine rpm', 'Lub oil pressure', 'Fuel pressure', 
+                'Coolant pressure', 'lub oil temp', 'Coolant temp']
+
+
+@st.cache_resource
+def load_model_and_scaler():
+    """Downloads and loads the model and scaler from Hugging Face Hub."""
+    st.info("Loading model and scaler from Hugging Face Hub...")
+    try:
+        model_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=MODEL_FILE)
+        model = joblib.load(model_path)
+        scaler_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=SCALER_FILE)
+        scaler = joblib.load(scaler_path)
+        st.success("Artifacts loaded successfully!")
+        return model, scaler
+    except Exception as e:
+        st.error(f"Error loading artifacts from Hugging Face Hub: {e}")
+        return None, None
+
+model, scaler = load_model_and_scaler()
+
+# Streamlit UI and Prediction Logic
+st.set_page_config(page_title="Predictive Maintenance", layout="wide")
+st.title("Engine Health Predictor")
+st.markdown("Use the sliders to simulate real-time sensor data and predict the **Engine Condition** (0=Healthy, 1=Faulty).")
+
+col1, col2, col3 = st.columns(3)
+
+with col1:
+    
+    Engine_rpm = st.slider("Engine RPM (rev/min)", min_value=60, max_value=2300, value=791, step=10)
+    Lub_oil_pressure = st.slider("Lub Oil Pressure (bar)", min_value=0.0, max_value=7.3, value=3.3, step=0.1)
+    Fuel_pressure = st.slider("Fuel Pressure (bar)", min_value=0.0, max_value=22.0, value=6.7, step=0.1)
+
+with col2:
+
+    Coolant_pressure = st.slider("Coolant Pressure (bar)", min_value=0.0, max_value=7.5, value=2.3, step=0.1)
+    Lub_oil_temp = st.slider("Lub Oil Temp (°C)", min_value=71.0, max_value=90.0, value=78.0, step=0.1)
+    Coolant_temp = st.slider("Coolant Temp (°C)", min_value=60.0, max_value=200.0, value=78.5, step=0.5)
+    
+# Prediction
+if st.button("Predict Engine Condition", type="primary"):
+    if model and scaler:
+        
+        input_data = pd.DataFrame({
+            'Engine rpm': [Engine_rpm],
+            'Lub oil pressure': [Lub_oil_pressure],
+            'Fuel pressure': [Fuel_pressure],
+            'Coolant pressure': [Coolant_pressure],
+            'lub oil temp': [Lub_oil_temp], 
+            'Coolant temp': [Coolant_temp]
+        }, index=[0])
+        
+        
+        scaled_data = scaler.transform(input_data)
+        
+        prediction = model.predict(scaled_data)[0]
+        prediction_proba = model.predict_proba(scaled_data)[0]
+
+        # Display Results
+        st.subheader("Prediction Result:")
+        if prediction == 1:
+            st.error(f"**FAULTY (Requires Maintenance)**")
+            st.markdown(f"**Confidence (Faulty):** `{prediction_proba[1]*100:.2f}%`")
+            st.warning("**Actionable Insight:** The model predicts a high risk of failure. Schedule maintenance immediately.")
+        else:
+            st.success(f"**HEALTHY (Normal Operation)**")
+            st.markdown(f"**Confidence (Healthy):** `{prediction_proba[0]*100:.2f}%`")
+            st.info("Engine is operating within normal parameters. Continue monitoring.")

hosting.py

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+
+from huggingface_hub import HfApi
+import os
+
+api = HfApi(token=os.getenv("HF_TOKEN"))
+api.upload_folder(
+    folder_path="/content/Predictive_Maintenance_Project/deployment",
+   
+    repo_id="RajendrakumarPachaiappan/EnginePredictionModel",
+
+    repo_type="space",
+    path_in_repo="",
+)

requirements.txt

@@ -1,3 +1,5 @@
-altair
 pandas
-streamlit
+scikit-learn
+joblib
+streamlit
+huggingface-hub