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@@ -1,64 +1,16 @@
-# Use a base image with Python and ideally CUDA support for GPU acceleration if available
-# Adjust the base image based on your needs and the environment of your deployment target (Hugging Face Spaces offers GPU options)
-# For CPU-only:
-# FROM python:3.9-slim
 
-# For CUDA support (example, check specific requirements for your CUDA version and OS):
-# FROM nvidia/cuda:11.6.2-cudnn8-runtime-ubuntu20.04
-# ENV PYTHON_VERSION=3.9
-# RUN apt-get update && apt-get install -y python${PYTHON_VERSION} python3-pip && rm -rf /var/lib/apt/lists/*
-# RUN ln -s /usr/bin/python3 /usr/bin/python
+# Use a lightweight Python image as the base
+FROM python:3.9-slim
 
-# Let's use a general Python image for demonstration. If you plan to use GPU,
-# you'll need a base image with CUDA drivers and potentially install additional libs.
-FROM python:3.9
-
-# Set the working directory in the container
+# Set the working directory
 WORKDIR /app
 
-# Copy the backend directory, including the requirements.txt and app.py
-COPY backend_files/ /app/backend_files/
-
-# Copy the serialized vector database directory
-# Ensure 'merck_manuals' matches the directory name you used to persist the Chroma DB
-# The path is relative to the directory containing the Dockerfile, which is now backend_files
-COPY ../merck_manuals/ /app/merck_manuals/
-
-
-# Install Python dependencies
-# Use --no-cache-dir to prevent caching, good for smaller image size
-# Use --upgrade pip to ensure you have the latest pip
-RUN pip install --upgrade pip
-RUN pip install --no-cache-dir -r /app/backend_files/requirements.txt
-
-# Copy the frontend directory (we will add this later)
-# If your frontend is a separate Gradio app calling the backend,
-# COPY front_end_files/ /app/front_end_files/
-# If your frontend is integrated into the backend (e.g., a single Gradio app), adjust paths
-
-# Expose the port the Flask application runs on
-# Hugging Face Spaces automatically exposes port 7860 for Gradio or 5000 for Flask/Streamlit
-# If using Gradio as the main app, it typically runs on 7860
-# If running Flask as the main app, it typically runs on 5000
-# EXPOSE 5000 # For Flask backend
 
-# Command to run the application
-# This depends on how you run your application.
-# If using Gunicorn to serve the Flask app:
-# CMD ["gunicorn", "--bind", "0.0.0.0:5000", "backend_files.app:app"]
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
 
-# If using Gradio as the main application entry point (recommended for HF Spaces with UI):
-# You would typically have a single app.py that launches Gradio,
-# and the Gradio app makes calls to the Flask backend (potentially running on the same Space internally or separately).
-# For a setup where Gradio runs and calls the Flask backend within the same container:
-# You might need a script to start both or configure Gunicorn/another server to handle both.
-# A simpler approach for HF Spaces is often to have one main app (e.g., Gradio) that handles everything or calls an external backend.
-# Assuming a setup where Gradio is the main entry point and lives in front_end_files:
-# CMD ["python", "-u", "/app/front_end_files/app.py"]
-# Or if the Gradio app is directly in the root of the container:
-# CMD ["python", "-u", "app.py"]
+# Install Python dependencies listed in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
 
-# Let's assume for now your main entry point will be in backend_files/app.py
-# and you will run it with Gunicorn as is common for Flask production deployment.
-# If you integrate Gradio directly into app.py, adjust this command.
-CMD ["gunicorn", "--bind", "0.0.0.0:5000", "backend_files.app:app"]
+# Command to run the application using Gunicorn
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:app"]