Update Dockerfile

Dockerfile

@@ -1,90 +1,31 @@
-# Multi-stage build: Train models then run app
-
-# ============================================
-# Stage 1: Training Stage
-# ============================================
-FROM python:3.10-slim as trainer
-
-WORKDIR /app
-
-# Install system dependencies for training
-RUN apt-get update && apt-get install -y \
-    git \
-    build-essential \
-    && rm -rf /var/lib/apt/lists/*
-
-# Copy requirements and install
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Copy only necessary files for training
-COPY models/ models/
-COPY data/ data/
-
-# Create output directory
-RUN mkdir -p models/model_weights/custom_models
-
-# Train models
-RUN echo "🔄 Starting model training..." && \
-    python models/train_model.py && \
-    echo "✅ Training completed!"
-
-# Verify models were created and have predict_proba
-RUN echo "🔍 Verifying models..." && \
-    ls -lah models/model_weights/custom_models/ && \
-    python -c "import joblib; \
-    m_en = joblib.load('models/model_weights/custom_models/english_model.pkl'); \
-    m_bn = joblib.load('models/model_weights/custom_models/bengali_model.pkl'); \
-    print(f'✅ English Model: {type(m_en).__name__}'); \
-    print(f'✅ Has predict_proba: {hasattr(m_en, \"predict_proba\")}'); \
-    print(f'✅ Bengali Model: {type(m_bn).__name__}'); \
-    print(f'✅ Has predict_proba: {hasattr(m_bn, \"predict_proba\")}')"
-
-# ============================================
-# Stage 2: Runtime Stage
-# ============================================
+# Create Dockerfile
 FROM python:3.10-slim
 
 WORKDIR /app
 
-# Install runtime system dependencies
+# Install system dependencies
 RUN apt-get update && apt-get install -y \
     git \
     && rm -rf /var/lib/apt/lists/*
 
-# Copy requirements and install
+# Copy requirements first for better caching
 COPY requirements.txt .
+
+# Install Python dependencies
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Copy application code
+# Copy application files
 COPY . .
 
-# Copy trained models from training stage
-COPY --from=trainer /app/models/model_weights/custom_models/*.pkl models/model_weights/custom_models/
-COPY --from=trainer /app/models/model_weights/custom_models/metadata.json models/model_weights/custom_models/
-
-# Create cache directories for transformers
+# Create cache directories
 RUN mkdir -p /tmp/transformers_cache /tmp/huggingface
 
 # Set environment variables
 ENV TRANSFORMERS_CACHE=/tmp/transformers_cache
 ENV HF_HOME=/tmp/huggingface
-ENV PYTHONUNBUFFERED=1
-
-# Verify models exist in final image
-RUN echo "🔍 Final verification..." && \
-    ls -lah models/model_weights/custom_models/ && \
-    python -c "import joblib; \
-    m = joblib.load('models/model_weights/custom_models/english_model.pkl'); \
-    print(f'✅ Final Stage - Model loaded: {type(m).__name__}'); \
-    print(f'✅ Has predict_proba: {hasattr(m, \"predict_proba\")}')"
 
 # Expose port 7860 (Hugging Face Spaces default)
 EXPOSE 7860
 
-# Health check
-HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
-    CMD python -c "import requests; requests.get('http://localhost:7860/health', timeout=5)" || exit 1
-
 # Run the application
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "1"]
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"] | Out-File -FilePath Dockerfile -Encoding utf8