Add Docker Compose setup and simplify environment configuration

.env.example

@@ -0,0 +1 @@
+HF_TOKEN=

.gitignore

@@ -1,3 +1 @@
-.venv
-.streamlit
-transformers.js
+.env

Dockerfile

@@ -24,6 +24,6 @@ RUN pip install --no-cache-dir -U pip \
 
 COPY . .
 
-EXPOSE 8501
+EXPOSE $PORT
 
 CMD ["sh", "-c", "streamlit run app.py --server.port $PORT --server.address 0.0.0.0"]

README.md

@@ -14,60 +14,44 @@ short_description: Convert a Hugging Face model to ONNX format
 
 ## Overview
 
-This project provides a Streamlit application that converts Hugging Face models to ONNX (Open Neural Network Exchange) format, enabling broader model compatibility and deployment options. The application streamlines the process of downloading, converting, and uploading models to Hugging Face.
+This project provides a Streamlit application that facilitates the conversion of Hugging Face models to ONNX format, downloading, converting, and uploading models to Hugging Face.
 
-## Features
+## Docker Compose usage
 
-- **One-Click Model Conversion**: Convert Hugging Face models to ONNX format with minimal configuration
-- **User-Friendly Interface**: Intuitive Streamlit-based web interface
-- **Quantization Support**: Automatic model quantization for reduced size and faster inference
+### 1. Prepare environment variables
 
-## Prerequisites
+Copy the provided template and fill in your Hugging Face write token:
 
-- Python 3.8+
-- Hugging Face account and API token
-- Git
+```bash
+cp .env.example .env
+```
 
-## Installation
+### 2. Start the application
 
-1. Clone the repository:
+```bash
+docker compose up
+```
 
-   ```bash
-   git clone https://huggingface.co/spaces/onnx-community/convert-to-onnx
-   cd convert-to-onnx
-   ```
+Access the interface at `http://localhost:8501`.
 
-2. Install dependencies:
+Enter a Hugging Face model ID (e.g., `EleutherAI/pythia-14m`).
 
-   ```bash
-   pip install -r requirements.txt
-   ```
+After a successful conversion, the ONNX export is available under `{username}/{model-name}-ONNX` unless you opt into reusing the same repository.
 
-3. Configure Hugging Face credentials:
+To stop the service, press `Ctrl+C` (or run `docker compose down`). Add `-d` to run detached.
 
-   ```bash
-   # Option 1: Environment variables
-   export HF_TOKEN="your_token"
+## Direct Docker usage (optional)
 
-   # Option 2: Create .streamlit/secrets.toml
-   echo 'HF_TOKEN = "your_token"' > .streamlit/secrets.toml
-   ```
+If you prefer not to use Docker Compose, you can still build and run manually:
 
-## Usage
+```bash
+docker build -t convert-to-onnx .
 
-1. Start the application:
-
-   ```bash
-   streamlit run app.py
-   ```
-
-2. Access the web interface at `http://localhost:8501`
-
-3. Enter a Hugging Face model ID (e.g., `EleutherAI/pythia-14m`)
-
-4. Click "Proceed" to start the conversion
-
-The converted model will be available in your Hugging Face account as `{username}/{model-name}-ONNX`.
+docker run --rm \
+  -p 8501:8501 \
+  -e HF_TOKEN="your_write_token" \
+  convert-to-onnx
+```
 
 ## Development
 

app.py

@@ -27,7 +27,7 @@ class Config:
     @classmethod
     def from_env(cls) -> "Config":
         """Create config from environment variables and secrets."""
-        system_token = os.getenv("HF_TOKEN") or st.secrets.get("HF_TOKEN")
+        system_token = os.getenv("HF_TOKEN")
         user_token = st.session_state.get("user_hf_token")
 
         if user_token:

docker-compose.yml

@@ -0,0 +1,7 @@
+services:
+  app:
+    build: .
+    ports:
+      - "8501:8501"
+    environment:
+      - HF_TOKEN=${HF_TOKEN}

run.sh

@@ -1,3 +0,0 @@
-#!/bin/sh
-pip install -r requirements.txt
-streamlit run app.py