Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,23 @@
-FROM python:3.13.5-slim
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9
 
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# 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
 
-EXPOSE 8501
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+COPY --chown=user . $HOME/app
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# 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

@@ -0,0 +1,76 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+import os
+from huggingface_hub import login, HfApi
+
+# Download and load the predictive maintenance model
+HF_TOKEN = os.getenv("HF_TOKEN")
+if not HF_TOKEN:
+    st.error("HF_TOKEN is not set. Cannot download model.")
+else:
+    try:
+        model_path = hf_hub_download(repo_id="sudha1726/predictive_maintainance_model", filename="best_predictive_maintainance_model_v1.joblib", token=HF_TOKEN)
+        model = joblib.load(model_path)
+        st.success("Predictive Maintenance Model loaded successfully!")
+    except Exception as e:
+        st.error(f"Error loading model from Hugging Face: {e}")
+        model = None
+
+# Streamlit UI for Predictive Maintenance
+st.title("Engine Predictive Maintenance App")
+st.write("""
+This application predicts whether an engine requires maintenance based on real-time sensor data.
+Please enter the engine sensor readings below to get a prediction.
+""")
+
+if model:
+    # User input fields for engine sensor data
+    engine_rpm = st.number_input("Engine RPM", min_value=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=3.0, format="%.2f")
+    fuel_pressure = st.number_input("Fuel Pressure (bar/kPa)", min_value=0.0, max_value=30.0, value=7.0, format="%.2f")
+    coolant_pressure = st.number_input("Coolant Pressure (bar/kPa)", min_value=0.0, max_value=10.0, value=2.5, format="%.2f")
+    lub_oil_temp = st.number_input("Lub Oil Temperature (°C)", min_value=0.0, max_value=100.0, value=75.0, format="%.2f")
+    coolant_temp = st.number_input("Coolant Temperature (°C)", min_value=0.0, max_value=200.0, value=80.0, format="%.2f")
+
+    # Assemble input data into 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 temp': lub_oil_temp,
+        'Coolant temp': coolant_temp
+    }])
+
+    # Prediction
+    if st.button("Predict Engine Condition"):
+        prediction = model.predict(input_data)[0]
+        result = "requires maintenance (Faulty)" if prediction == 1 else "does not require maintenance (Normal)"
+        st.subheader("Prediction Result:")
+        st.success(f"The model predicts the engine **{result}**")
+
+    st.write("---")
+st.subheader("Upload Deployment to Hugging Face Space")
+
+if st.button("Upload Deployment Files"):
+    if not HF_TOKEN:
+        st.error("HF_TOKEN is not set. Cannot upload files.")
+    else:
+        login(token=HF_TOKEN)
+        api = HfApi()
+        repo_id = "sudha1726/predictive-maintainanace"  # your Space repo
+        folder_path = "predictive_maintainance_project/deployment"
+
+        try:
+            api.upload_folder(
+                folder_path=folder_path,
+                repo_id=repo_id,
+                repo_type="space",
+                path_in_repo=""
+            )
+            st.success("Deployment files uploaded successfully to Hugging Face Space!")
+        except Exception as e:
+            st.error(f"Error uploading deployment files: {e}")
+

requirements.txt

@@ -1,3 +1,7 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+huggingface_hub==0.32.6
+streamlit==1.43.2
+joblib==1.5.1
+scikit-learn==1.6.1
+xgboost==2.1.4
+mlflow==3.0.1