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| # Sentiment Checker (Docker + Hugging Face Spaces) |
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| A minimal interactive web app that performs sentiment analysis using a Hugging Face model. |
| Users can type text, click a button, and instantly receive a prediction. |
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| --- |
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| ## 🚀 Live Concept |
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| - Type text into a box |
| - Click **Check** |
| - Get sentiment (POSITIVE / NEGATIVE) |
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| --- |
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| ## 🧠 Why This Project Exists |
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| Machine learning apps often fail to run across different environments due to: |
| - dependency conflicts |
| - mismatched library versions |
| - complex setup requirements |
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| This project demonstrates how Docker solves these issues and enables seamless deployment on Hugging Face Spaces. |
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| --- |
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| ## 🧰 Tech Stack |
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| - Python |
| - FastAPI |
| - Transformers (by Hugging Face) |
| - Docker |
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| --- |
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| ## 📁 Project Structure |
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| ``` |
| |
| . |
| ├── app.py |
| ├── requirements.txt |
| └── Dockerfile |
| |
| ```` |
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| --- |
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| ## ⚙️ How It Works |
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| 1. The frontend is a simple HTML page served by FastAPI |
| 2. User input is sent to the `/predict` endpoint |
| 3. A pre-trained sentiment model processes the text |
| 4. The result is returned and displayed instantly |
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| --- |
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| ## 🐳 Docker Setup |
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| ### Build the image |
| ```bash |
| docker build -t sentiment-app . |
| ```` |
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| ### Run the container |
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| ```bash |
| docker run -p 7860:7860 sentiment-app |
| ``` |
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| Then open: [http://localhost:7860](http://localhost:7860) |
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| --- |
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| ## 🤗 Deployment (Hugging Face Spaces) |
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| This app is designed to run on Hugging Face Spaces using Docker. |
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| Steps: |
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| 1. Create a new Space |
| 2. Select **Docker** as the SDK |
| 3. Upload project files |
| 4. The app will automatically build and deploy |
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| --- |
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| ## 💡 Why Docker Matters |
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| Without Docker: |
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| * Manual installation of dependencies |
| * Version conflicts (e.g., Torch, Transformers) |
| * Inconsistent results across machines |
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| With Docker: |
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| * Reproducible environment |
| * One-step deployment |
| * Works the same everywhere |
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| --- |
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| ## ⚠️ Notes |
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| * The first run may take longer due to model download |
| * Subsequent requests are much faster |
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| --- |
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| ## ✅ Key Takeaway |
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| Docker enables reliable, reproducible deployment of machine learning applications, making it easy to share and run apps on platforms like Hugging Face Spaces without additional setup. |
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| ``` |
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