Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,36 @@
+# Use Python 3.10 slim for smaller image size
 FROM python:3.10-slim
 
+# Set working directory
 WORKDIR /app
 
-# Copy requirements and install dependencies
+# Copy requirements and install dependencies first (better caching)
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Copy application file
-COPY app.py .
+# Create non-root user for security (HF Spaces best practice)
+RUN useradd -m -u 1000 user
 
-# Expose Streamlit default port
-EXPOSE 7860
+# Switch to non-root user
+USER user
+
+# Set user environment variables
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Set working directory to user's app directory
+WORKDIR $HOME/app
 
-# Set environment variables for Streamlit
-ENV STREAMLIT_SERVER_PORT=7860
-ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
+# Copy application files with correct ownership
+COPY --chown=user . $HOME/app
+
+# Expose Hugging Face Spaces standard port
+EXPOSE 7860
 
-# Run the application
-CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]
+# Run Streamlit on port 7860 with production settings
+CMD ["streamlit", "run", "app.py", \
+     "--server.port=7860", \
+     "--server.address=0.0.0.0", \
+     "--server.headless=true", \
+     "--server.fileWatcherType=none", \
+     "--browser.gatherUsageStats=false"]

app.py

@@ -1,15 +1,19 @@
 """
 Streamlit Application for Engine Predictive Maintenance
-Production-ready deployment with proper error handling
+With detailed logging for debugging
 """
 
 import streamlit as st
 import pandas as pd
-from huggingface_hub import hf_hub_download, login
-import joblib
 import os
+import sys
 
-# Page Configuration
+# Print to console (will show in HF Space logs)
+print("=" * 70, file=sys.stderr)
+print("APP STARTING - INITIALIZATION", file=sys.stderr)
+print("=" * 70, file=sys.stderr)
+
+# Page Configuration MUST be first
 st.set_page_config(
     page_title="Engine Predictive Maintenance",
     page_icon="🔧",
@@ -17,6 +21,18 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
+# Import after page config
+try:
+    print("Importing huggingface_hub...", file=sys.stderr)
+    from huggingface_hub import hf_hub_download, login
+    print("Importing joblib...", file=sys.stderr)
+    import joblib
+    print("✓ All imports successful", file=sys.stderr)
+except Exception as e:
+    print(f"✗ Import error: {e}", file=sys.stderr)
+    st.error(f"Import failed: {e}")
+    st.stop()
+
 # Custom CSS
 st.markdown("""
 <style>
@@ -63,29 +79,58 @@ st.markdown("""
 
 @st.cache_resource
 def load_model():
-    """Load model from Hugging Face with authentication"""
+    """Load model from Hugging Face with detailed logging"""
+    
+    print("\n" + "=" * 70, file=sys.stderr)
+    print("LOADING MODEL FROM HUGGING FACE", file=sys.stderr)
+    print("=" * 70, file=sys.stderr)
+    
     try:
-        # Authenticate
+        # Check for token
         hf_token = os.environ.get("HF_TOKEN")
+        print(f"HF_TOKEN found: {hf_token is not None}", file=sys.stderr)
+        
         if hf_token:
+            print("Authenticating with Hugging Face...", file=sys.stderr)
             login(token=hf_token)
+            print("✓ Authentication successful", file=sys.stderr)
+        else:
+            print("⚠ No HF_TOKEN - attempting public access", file=sys.stderr)
         
         # Download model
+        print("\nDownloading model...", file=sys.stderr)
+        print("  Repo: Quantum9999/xgb-predictive-maintenance", file=sys.stderr)
+        print("  File: xgb_tuned_model.joblib", file=sys.stderr)
+        
         model_path = hf_hub_download(
             repo_id="Quantum9999/xgb-predictive-maintenance",
             filename="xgb_tuned_model.joblib",
             token=hf_token
         )
+        print(f"✓ Model downloaded: {model_path}", file=sys.stderr)
         
         # Load model
+        print("Loading model into memory...", file=sys.stderr)
         model = joblib.load(model_path)
+        print("✓ Model loaded successfully", file=sys.stderr)
+        print("=" * 70 + "\n", file=sys.stderr)
+        
         return model, None
         
     except Exception as e:
-        return None, str(e)
+        error_msg = f"Model loading failed: {str(e)}"
+        print(f"✗ {error_msg}", file=sys.stderr)
+        import traceback
+        print(f"Traceback:\n{traceback.format_exc()}", file=sys.stderr)
+        print("=" * 70 + "\n", file=sys.stderr)
+        return None, error_msg
 
 
 def main():
+    """Main application"""
+    
+    print("Starting main application...", file=sys.stderr)
+    
     # Header
     st.markdown(
         '<div class="main-header">🔧 Engine Predictive Maintenance System</div>',
@@ -100,9 +145,24 @@ def main():
     model, error = load_model()
     
     if model is None:
-        st.error(f"❌ Failed to load prediction model: {error}")
-        st.info("Please check Hugging Face configuration and ensure HF_TOKEN is set correctly.")
-        return
+        st.error(f"❌ Failed to load prediction model")
+        st.code(error)
+        
+        with st.expander("🔍 Troubleshooting"):
+            st.write("**Possible Issues:**")
+            st.write("1. HF_TOKEN not set in Space secrets")
+            st.write("2. Model repository is private")
+            st.write("3. Model filename is incorrect")
+            st.write("4. Network connectivity issue")
+            
+            st.write("\n**Current Configuration:**")
+            st.write(f"- HF_TOKEN set: {os.environ.get('HF_TOKEN') is not None}")
+            st.write("- Expected repo: Quantum9999/xgb-predictive-maintenance")
+            st.write("- Expected file: xgb_tuned_model.joblib")
+        
+        st.stop()
+    
+    st.success("✓ Model loaded successfully!")
 
     # Sidebar
     with st.sidebar:
@@ -120,14 +180,13 @@ def main():
           - 0: Normal Operation
           - 1: Maintenance Required
         - **Training Data**: 19,535 records
-        - **Test Accuracy**: ~92%
         """)
         
         st.header("🎯 How to Use")
         st.markdown("""
-        1. Enter current sensor readings in the input fields
-        2. Click **'Predict Engine Condition'**
-        3. Review prediction and confidence scores
+        1. Enter current sensor readings
+        2. Click 'Predict Engine Condition'
+        3. Review prediction and confidence
         4. Take action based on results
         """)
         
@@ -146,7 +205,7 @@ def main():
     st.header("📝 Enter Engine Sensor Readings")
     st.markdown("---")
 
-    # Create two columns for input
+    # Input columns
     col1, col2 = st.columns(2)
 
     with col1:
@@ -215,18 +274,22 @@ def main():
     if st.button("🔍 Predict Engine Condition", use_container_width=True, type="primary"):
         # Prepare input data
         input_df = 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_temp,
-            "Coolant Temperature": coolant_temp
+            "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
         }])
 
         try:
+            print(f"Making prediction with input: {input_df.to_dict()}", file=sys.stderr)
+            
             # Make prediction
             prediction = model.predict(input_df)[0]
             proba = model.predict_proba(input_df)[0]
+            
+            print(f"Prediction: {prediction}, Probabilities: {proba}", file=sys.stderr)
 
             # Display results
             st.markdown("---")
@@ -297,7 +360,12 @@ def main():
                     """)
         
         except Exception as e:
-            st.error(f"❌ Prediction error: {e}")
+            error_msg = f"Prediction error: {e}"
+            print(f"✗ {error_msg}", file=sys.stderr)
+            import traceback
+            print(f"Traceback:\n{traceback.format_exc()}", file=sys.stderr)
+            
+            st.error(f"❌ {error_msg}")
             st.info("Please verify all sensor values are within valid ranges and try again.")
     
     # Footer
@@ -312,4 +380,12 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    print("Entering main()...", file=sys.stderr)
+    try:
+        main()
+        print("✓ Main completed successfully", file=sys.stderr)
+    except Exception as e:
+        print(f"✗ FATAL ERROR: {e}", file=sys.stderr)
+        import traceback
+        print(f"Traceback:\n{traceback.format_exc()}", file=sys.stderr)
+        st.error(f"Application error: {e}")