update readme

Dockerfile

@@ -0,0 +1,22 @@
+# First stage: Install dependencies
+FROM python:3.9-slim as builder
+WORKDIR /app
+
+# Copy requirements and install dependencies
+COPY app/requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir -r /app/requirements.txt
+
+# Second stage: Copy app code and define production image
+FROM python:3.9-slim
+WORKDIR /app
+COPY --from=builder /usr/local/lib/python3.9/site-packages /usr/local/lib/python3.9/site-packages
+COPY app /app
+
+# Expose port for the API
+EXPOSE 8000
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+
+# Command to run the FastAPI app
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

README.md

@@ -1,11 +1,28 @@
 ---
-title: Bangla Spam Detection
-emoji: 📚
-colorFrom: purple
-colorTo: pink
-sdk: docker
-pinned: false
-license: mit
+tags:
+- bangla
+- spam-detection
+- text-classification
+- transformers
+- fastapi
+datasets:
+- custom
+metrics:
+- accuracy
+model-index:
+- name: bangla_spam_detection
+  results:
+  - task:
+      type: text-classification
+      name: Spam Detection
+    dataset:
+      name: Custom Dataset
+      type: custom
+    metrics:
+      - type: accuracy
+        value: 0.95 # example value
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Bangla Spam Detection API
+
+This project uses a Hugging Face transformer model to detect spam in Bangla SMS messages...

app/api/v1/endpoints/predict.py

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+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from app.models.nlp_model import classify_text
+
+router = APIRouter()
+
+class SMSInput(BaseModel):
+    text: str
+
+@router.post("/predict")
+async def predict_sms(input: SMSInput):
+    try:
+        prediction = classify_text(input.text)
+        return {"text": input.text, "prediction": prediction}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail="Model prediction error.")

app/core/config.py

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+from pydantic import BaseSettings
+
+class Settings(BaseSettings):
+    PROJECT_NAME: str = "Bangla Spam Detection API"
+    PROJECT_VERSION: str = "1.0.0"
+    MODEL_NAME: str = "Bijoy09/bangla_spam_detection"
+
+    class Config:
+        env_file = ".env"
+
+settings = Settings()

app/main.py

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+from fastapi import FastAPI
+from app.api.v1.endpoints import predict
+from app.core.config import settings
+
+app = FastAPI(title=settings.PROJECT_NAME, version=settings.PROJECT_VERSION)
+
+# Include router for the prediction endpoint
+app.include_router(predict.router, prefix="/api/v1")

app/models/nlp_model.py

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+from transformers import pipeline
+from app.core.config import settings
+
+# Initialize the NLP model pipeline
+pipe = pipeline("text-classification", model=settings.MODEL_NAME)
+
+def classify_text(text: str) -> str:
+    result = pipe(text)[0]
+    return result["label"]

app/requirements.txt

@@ -0,0 +1,6 @@
+fastapi
+uvicorn
+transformers
+torch
+pydantic
+python-dotenv

docker-compose.yml

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+version: "3.8"
+
+services:
+  api:
+    build:
+      context: .
+    environment:
+      - MODEL_NAME=${MODEL_NAME}
+    ports:
+      - "8000:8000"
+    deploy:
+      replicas: 3
+      update_config:
+        parallelism: 2
+    networks:
+      - app-network
+
+networks:
+  app-network:
+    driver: bridge