Add initial implementation of Spam Detection API with Docker support

Dockerfile

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+# Dockerfile
+
+# Use a lean official Python image
+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements and install dependencies
+COPY requirements.txt /app/
+
+RUN pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cpu
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the application code
+COPY main.py /app/
+COPY tests/ /app/tests/
+# The port is often fixed by the cloud host (e.g., 7860 for HF Spaces)
+# We use 8000 as a standard, and the cloud host will map its port to this one.
+EXPOSE 8000
+
+# Command to run the application using Uvicorn
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+# main.py
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+from contextlib import asynccontextmanager
+import torch
+
+# --- MLOps Configuration ---
+HF_MODEL_NAME = "roshana1s/spam-message-classifier"
+CLASSIFIER_PIPELINE = None
+
+# Pydantic model for request body
+class Message(BaseModel):
+    text: str
+
+# --- LIFESPAN MANAGER (The Modern Fix) ---
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # STARTUP: Load the model
+    global CLASSIFIER_PIPELINE
+    print(f"Loading model {HF_MODEL_NAME}...")
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(HF_MODEL_NAME)
+        model = AutoModelForSequenceClassification.from_pretrained(HF_MODEL_NAME)
+        
+        CLASSIFIER_PIPELINE = pipeline(
+            "text-classification",
+            model=model,
+            tokenizer=tokenizer,
+            device=0 if torch.cuda.is_available() else -1 
+        )
+        print("Model loaded successfully.")
+    except Exception as e:
+        print(f"Failed to load model: {e}")
+    
+    yield # This point is where the app runs
+    
+    # SHUTDOWN: Clean up resources (if needed)
+    print("Shutting down model resources...")
+    CLASSIFIER_PIPELINE = None
+
+# Initialize App with Lifespan
+app = FastAPI(
+    title="Spam Detection API",
+    lifespan=lifespan 
+)
+
+@app.get("/")
+def health_check():
+    return {"status": "ok", "model_loaded": CLASSIFIER_PIPELINE is not None}
+
+@app.post("/predict")
+def predict_spam(item: Message):
+    if CLASSIFIER_PIPELINE is None:
+        raise HTTPException(status_code=503, detail="Model is not ready.")
+
+    try:
+        results = CLASSIFIER_PIPELINE(item.text)
+        label = results[0]['label'] 
+        score = results[0]['score']
+        output_label = "spam" if label == 'LABEL_1' else "ham"
+        
+        return {
+            "prediction": output_label, 
+            "confidence_score": score,
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

requirements.txt

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+# This line tells pip to look for CPU versions first
+--extra-index-url https://download.pytorch.org/whl/cpu
+
+fastapi
+uvicorn[standard]
+transformers
+torch # PyTorch is the framework for this model
+pydantic
+pytest
+httpx