successfully created the complete codebase for the Sinhala NLP Deployment project

.dockerignore

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+__pycache__/
+.pytest_cache/
+.git/
+.github/
+tests/
+README.md
+*.pyc
+*.pyo
+*.pyd
+.Python
+env/
+venv/

.github/workflows/deploy.yml

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+name: Deploy to Hugging Face Spaces
+
+on:
+  push:
+    branches: [ "main" ]
+
+jobs:
+  build-and-test:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.10'
+        
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+        
+    - name: Run Tests
+      run: |
+        pytest tests/
+
+  deploy-to-hf:
+    needs: build-and-test
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+      with:
+        fetch-depth: 0 # Fetch all history for push
+        
+    - name: Push to Hugging Face Spaces
+      env:
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        HF_USERNAME: ${{ secrets.HF_USERNAME }}
+        HF_SPACE_NAME: ${{ secrets.HF_SPACE_NAME }}
+      run: |
+        git config --global user.email "actions@github.com"
+        git config --global user.name "GitHub Actions"
+        git remote add hf https://$HF_USERNAME:$HF_TOKEN@huggingface.co/spaces/$HF_USERNAME/$HF_SPACE_NAME
+        git push -f hf main

Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.10-slim
+
+# Set the working directory in the container
+WORKDIR /code
+
+# Copy the requirements file into the container
+COPY ./requirements.txt /code/requirements.txt
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r /code/requirements.txt
+
+# Create a non-root user for security (Hugging Face Spaces requirement)
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container
+COPY --chown=user . $HOME/app
+
+# Pre-download the Hugging Face model during the build stage
+# This saves time and bandwidth during container startup
+RUN python -c "from transformers import pipeline; pipeline('sentiment-analysis', model='keshan/sinhala-sentiment-analysis')"
+
+# Make port 7860 available to the world outside this container
+EXPOSE 7860
+
+# Run main.py when the container launches
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]

app/main.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from app.model import predict_sentiment
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = FastAPI(
+    title="Sinhala Sentiment Analysis API",
+    description="A robust REST API for predicting sentiment of Sinhala text.",
+    version="1.0.0"
+)
+
+class SentimentRequest(BaseModel):
+    text: str
+
+class SentimentResponse(BaseModel):
+    label: str
+    score: float
+
+@app.get("/")
+def read_root():
+    return {"message": "Welcome to the Sinhala Sentiment Analysis API. Use POST /predict to analyze text."}
+
+@app.post("/predict", response_model=SentimentResponse)
+def predict(request: SentimentRequest):
+    if not request.text or len(request.text.strip()) == 0:
+        raise HTTPException(status_code=400, detail="Text cannot be empty.")
+    
+    try:
+        result = predict_sentiment(request.text)
+        return result
+    except Exception as e:
+        logger.error(f"Prediction error: {e}")
+        raise HTTPException(status_code=500, detail="Internal server error during prediction.")

app/model.py

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+from transformers import pipeline
+import logging
+
+logger = logging.getLogger(__name__)
+
+# Using a robust Sinhala sentiment analysis model from Hugging Face
+MODEL_NAME = "keshan/sinhala-sentiment-analysis"
+
+try:
+    logger.info(f"Loading model {MODEL_NAME}...")
+    sentiment_pipeline = pipeline("sentiment-analysis", model=MODEL_NAME)
+    logger.info("Model loaded successfully.")
+except Exception as e:
+    logger.error(f"Error loading model: {e}")
+    sentiment_pipeline = None
+
+def predict_sentiment(text: str):
+    if not sentiment_pipeline:
+        raise RuntimeError("Model pipeline is not initialized.")
+    
+    result = sentiment_pipeline(text)[0]
+    return {
+        "label": result["label"],
+        "score": float(result["score"])
+    }

requirements.txt

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+fastapi>=0.110.0
+uvicorn>=0.28.0
+transformers>=4.38.2
+torch>=2.2.1
+pydantic>=2.6.4
+pytest>=8.1.1
+httpx>=0.27.0

tests/test_api.py

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+from fastapi.testclient import TestClient
+from app.main import app
+
+client = TestClient(app)
+
+def test_read_root():
+    response = client.get("/")
+    assert response.status_code == 200
+    assert "Welcome" in response.json()["message"]
+
+def test_predict_sentiment_positive():
+    # "This is a very good creation." in Sinhala
+    response = client.post("/predict", json={"text": "මෙය ඉතා හොඳ නිර්මාණයක්."}) 
+    assert response.status_code == 200
+    assert "label" in response.json()
+    assert "score" in response.json()
+
+def test_predict_sentiment_empty():
+    response = client.post("/predict", json={"text": ""})
+    assert response.status_code == 400