Update Dockerfile

Dockerfile

@@ -1,35 +1,20 @@
-# Use the official Ollama Docker image
-FROM ollama/ollama:latest
+# Use a Python base image
+FROM python:3.9-slim-buster
 
-# Set the working directory inside the container
+# Set working directory
 WORKDIR /app
 
-# Define the model you want to download (replace with your chosen sub-500MB model)
-# Examples: smollm:135m, smollm:360m, tinyllama:1.1b-chat-v1.0-q2_K
-ENV OLLAMA_MODEL_NAME="smollm:135m"
+# Install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
 
-# Pull the model during the Docker build process
-# This ensures the model is present when the container starts,
-# reducing startup time and avoiding dynamic downloads.
-# The `ollama pull` command can be slow, so pulling it once here is efficient.
-RUN ollama pull ${OLLAMA_MODEL_NAME}
+# Copy your application code
+COPY app.py .
 
-# Set OLLAMA_HOST to listen on all network interfaces inside the container
-# This is crucial for Hugging Face Spaces to be able to connect to Ollama.
-ENV OLLAMA_HOST="0.0.0.0:11434"
+# Expose the port your API will run on (Hugging Face Spaces uses 7860 by default for Gradio/Streamlit,
+# but for custom Docker, you can choose, e.g., 8000 for FastAPI or 5000 for Flask).
+EXPOSE 8000
 
-# Allow all origins for the API (CORS configuration)
-ENV OLLAMA_ORIGINS="*"
-
-# Expose the port Ollama listens on
-EXPOSE 11434
-
-# Command to start the Ollama server.
-# The base image typically has ENTRYPOINT ["ollama"], so CMD just provides the argument.
-CMD ["serve"]
-
-# Optional: Add metadata for Hugging Face Spaces (improves discoverability)
-LABEL org.opencontainers.image.title="Ollama with a tiny model"
-LABEL org.opencontainers.image.description="Runs Ollama with a pre-loaded model less than 500MB"
-LABEL org.opencontainers.image.licenses="MIT"
-LABEL org.opencontainers.image.authors="Your Name/Org"
+# Command to run your FastAPI/Flask app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"] # For FastAPI
+# CMD ["python", "app.py"] # For a simple Flask app