Upload folder using huggingface_hub

Dockerfile

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+# Use a minimal base image with Python 3.10 installed
+FROM python:3.10-slim
+
+#Install neccesary libraries:
+RUN apt-get update && apt-get install -y build-essential && rm -rf /var/lib/apt/lists/*
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+#Below ones don't work in huggingface streamlit spaces:
+#ENV HUGGINGFACE_USER_NAME=${HUGGINGFACE_USER_NAME}
+#ENV HUGGINGFACE_MODEL_NAME=${HUGGINGFACE_MODEL_NAME}
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+# Expose container port for Streamlit:
+EXPOSE 7860
+
+# Define the command to run the Streamlit app on port "7860" and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false", "--gatherUsageStats=false"]

README.md

@@ -1,12 +1,9 @@
 ---
-title: EngineFailurePredictionSpace
-emoji: 🐢
-colorFrom: yellow
-colorTo: gray
+title: Engine Failure Predictor
 sdk: docker
+sdk_version: latest
+app_port: 7860
+app_file: app.py
 pinned: false
 license: mit
-short_description: Engine Failure Prediction Streamlit Space
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+import os
+
+# To ensure app starts loading quickly we set the title first:
+st.set_page_config(page_title="Engine Failure Prediction", layout="centered")
+
+# Common constants
+HUGGINGFACE_USER_NAME = os.getenv('HUGGINGFACE_USER_NAME')
+HUGGINGFACE_MODEL_NAME = os.getenv('HUGGINGFACE_MODEL_NAME')
+
+# Download the model from the Model Hub
+@st.cache_resource # Use caching to avoid re-downloading on every slider move
+def load_remote_model():
+    try:
+        repo_id = f"{HUGGINGFACE_USER_NAME}/{HUGGINGFACE_MODEL_NAME}"
+        model_path = hf_hub_download(
+            repo_id=repo_id,
+            filename="model.joblib"
+        )
+        return joblib.load(model_path)
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return e
+
+# Streamlit UI Setup
+st.title("Engine Failure Prediction")
+st.write("""
+This tool predicts engine health based on real-time telemetry.
+Adjust the sliders below to simulate engine sensor data.
+""")
+
+st.divider()
+
+# Create UI Layout
+col1, col2 = st.columns(2)
+
+with col1:
+    engine_rpm = st.number_input("Engine RPM", min_value=20, max_value=2500, value=791, step=1, help="Rotations per minute")
+    lub_oil_pressure = st.number_input("Lub Oil Pressure (bar)", min_value=0.0, max_value=8.0, value=3.3, step=0.1)
+    fuel_pressure = st.number_input("Fuel Pressure (bar)", min_value=0.0, max_value=25.0, value=6.6, step=0.1)
+
+with col2:
+    coolant_pressure = st.number_input("Coolant Pressure (bar)", min_value=0.0, max_value=8.0, value=2.3, step=0.1)
+    lub_oil_temp = st.number_input("Lub Oil Temp (°C)", min_value=30.0, max_value=100.0, value=77.6, step=0.1)
+    coolant_temp = st.number_input("Coolant Temp (°C)", min_value=30.0, max_value=200.0, value=78.4, step=0.1)
+
+# Prepare input data matching the exact training schema
+# these keys match the 'numeric_scaling' list in our model training script
+input_dict = {
+    "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
+}
+
+input_data = pd.DataFrame([input_dict])
+
+# Prediction Logic
+# We use 0.5 but a lower value could be slightly more sensitive to failures (maximizing Recall)
+classification_threshold = 0.5
+
+st.divider()
+
+if st.button("Generate Prediction", type="primary"):
+    # loading the model(it will be cached so only first time it will actually download)
+    model = load_remote_model()
+
+    # The 'model' here is the Scikit-Learn Pipeline
+    # It automatically runs the StandardScaler on input_data before passing to XGBoost
+    prediction_proba = model.predict_proba(input_data)[0, 1]
+
+    # Apply custom threshold
+    prediction = 1 if prediction_proba >= classification_threshold else 0
+
+    if prediction == 1:
+        st.error(f"### ⚠️ CRITICAL: Engine Failure Likely\n**Probability of Failure:** {prediction_proba:.2%}")
+        st.write("Immediate maintenance inspection recommended to avoid service disruption.")
+    else:
+        st.success(f"### ✅ NORMAL: Engine Healthy\n**Probability of Failure:** {prediction_proba:.2%}")
+        st.write("Engine parameters are within safe operating margins.")
+
+# Add technical metadata for your portfolio
+with st.expander("View Model & System Details"):
+    st.write(f"**Model Source:** Hugging Face Hub ({HUGGINGFACE_MODEL_NAME})")
+    st.write(f"**Threshold Applied:** {classification_threshold}")
+    st.write("**Architecture:** Pipeline(StandardScaler -> XGBoost)")

requirements.txt

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+pandas==2.2.2
+huggingface_hub==0.32.6
+streamlit==1.43.2
+joblib==1.5.1
+scikit-learn==1.6.0
+xgboost==2.1.4