Add Docker containerization support

- Create Dockerfile with Python 3.11 base image
- Add .dockerignore to exclude unnecessary files
- Configure FastAPI app for containerized deployment
- Add sentiment analysis endpoint with DistilBERT

.dockerignore

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+# Python
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.Python
+
+# Virtual environment
+.venv/
+venv/
+ENV/
+
+# Git
+.git/
+.gitignore
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Logs
+*.log
+
+# Test
+.pytest_cache/
+.coverage
+
+# Documentation (not needed in container)
+README.md
+docs/

Dockerfile

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+# Base image with Python pre-installed
+FROM python:3.11-slim
+
+# Set working directory inside container
+WORKDIR /app
+
+# Copy requirements first (for layer caching)
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY src/ ./src/
+
+# Document the port (informational)
+EXPOSE 8000
+
+# Health check for production monitoring
+HEALTHCHECK --interval=30s --timeout=3s --start-period=40s \
+  CMD curl -f http://localhost:8000/health || exit 1
+
+# Command to run when container starts
+CMD ["uvicorn", "src.main:app", "--host", "0.0.0.0", "--port", "8000"]

requirements.txt

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+fastapi==0.115.5
+uvicorn[standard]==0.32.1
+transformers==4.46.3
+torch==2.5.1
+pydantic==2.10.3
+pytest==8.3.4
+httpx==0.28.1

src/main.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, Field
+from transformers import pipeline
+import time
+
+app = FastAPI(
+    title="Sentiment Analysis API",
+    description="Analyze text sentiment using transformers",
+    version="1.0.0"
+)
+
+# Load model once at startup
+print("Loading sentiment analysis model...")
+sentiment_analyzer = pipeline(
+    "sentiment-analysis",
+    model="distilbert-base-uncased-finetuned-sst-2-english"
+)
+print("Model loaded!")
+
+class TextRequest(BaseModel):
+    text: str = Field(..., min_length=1, max_length=512, 
+                     example="I love this product!")
+
+class SentimentResponse(BaseModel):
+    text: str
+    sentiment: str
+    confidence: float
+    processing_time_ms: int
+
+@app.get("/")
+def root():
+    """Health check endpoint"""
+    return {
+        "status": "healthy",
+        "service": "sentiment-api",
+        "version": "1.0.0"
+    }
+
+@app.post("/analyze", response_model=SentimentResponse)
+def analyze_sentiment(request: TextRequest):
+    """
+    Analyze sentiment of input text.
+    Returns sentiment (POSITIVE/NEGATIVE) with confidence score.
+    """
+    start_time = time.time()
+    
+    try:
+        # Run inference
+        result = sentiment_analyzer(request.text)[0]
+        
+        processing_time = int((time.time() - start_time) * 1000)
+        
+        return SentimentResponse(
+            text=request.text,
+            sentiment=result['label'],
+            confidence=round(result['score'], 4),
+            processing_time_ms=processing_time
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/health")
+def health():
+    """Kubernetes-style health check"""
+    return {"status": "ok"}