..

Dockerfile

@@ -1,20 +1,20 @@
-# Start from the official Python image
+# Use official Python image
 FROM python:3.9
 
-# Create a non-root user to run the app
+# Create non-root user
 RUN useradd -m -u 1000 user
 USER user
 ENV PATH="/home/user/.local/bin:$PATH"
 
-# Set the working directory
+# Set working dir
 WORKDIR /app
 
-# Install dependencies
-COPY --chown=user ./requirements.txt requirements.txt
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
+# Install requirements
+COPY --chown=user requirements.txt requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
 
-# Copy the app files
-COPY --chown=user . /app
+# Copy project files
+COPY --chown=user . .
 
-# Run the FastAPI app
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+# Run FastAPI app
+CMD ["uvicorn", "server:app", "--host", "0.0.0.0", "--port", "7860"]

db_pool.py

@@ -0,0 +1,23 @@
+import os
+import psycopg2
+from psycopg2 import pool
+
+DB_URL = os.getenv("DATABASE_URL")
+_pool = None
+
+def init_db_pool():
+    global _pool
+    if DB_URL:
+        _pool = psycopg2.pool.SimpleConnectionPool(1, 5, dsn=DB_URL)
+        print("✅ DB pool initialized")
+
+def close_db_pool():
+    global _pool
+    if _pool:
+        _pool.closeall()
+        print("✅ DB pool closed")
+
+def get_conn():
+    if not _pool:
+        raise RuntimeError("DB pool not initialized")
+    return _pool.getconn()

logger.py

@@ -0,0 +1,16 @@
+import traceback
+import db_pool
+
+def sync_log_to_db(texts, results):
+    values = [(t, r["blur"], r["score"]) for t, r in zip(texts, results)]
+    try:
+        conn = db_pool.get_conn()
+        with conn.cursor() as cur:
+            cur.executemany("INSERT INTO cases(text, blur, score) VALUES(%s, %s, %s)", values)
+            conn.commit()
+        print(f"✅ Logged {len(values)} rows")
+        return True
+    except Exception as e:
+        print("❌ Logging failed:", e)
+        traceback.print_exc()
+        return False

model_loader.py

@@ -0,0 +1,16 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+tokenizer = None
+model = None
+
+def load_model():
+    global tokenizer, model
+
+    model_id = "medoxz543/hate-speech"
+    print(f"🔄 Loading model from Hugging Face Hub: {model_id}")
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForSequenceClassification.from_pretrained(model_id).to(DEVICE)
+    model.eval()
+    print("✅ Model ready")

requirements.txt

@@ -2,4 +2,5 @@ fastapi
 uvicorn
 transformers
 torch
-emoji
+emoji
+psycopg2-binary

routes.py

@@ -0,0 +1,43 @@
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from datetime import datetime
+import torch
+import model_loader
+import os
+
+if os.getenv("DATABASE_URL"):
+    from logger import sync_log_to_db
+
+router = APIRouter()
+DEVICE = model_loader.DEVICE
+
+def predict(texts: list[str]) -> list[float]:
+    enc = model_loader.tokenizer(texts, padding=True, truncation=True, max_length=70, return_tensors="pt")
+    enc = {k: v.to(DEVICE) for k, v in enc.items()}
+    with torch.no_grad():
+        probs = model_loader.model(**enc).logits.softmax(-1)[:, 1]
+    return probs.cpu().tolist()
+
+class TextRequest(BaseModel):
+    texts: list[str]
+
+@router.get("/")
+def root():
+    return {"status": "🟢 HF Space is running"}
+
+@router.post("/check-text")
+def check_text(payload: TextRequest):
+    if not payload.texts:
+        raise HTTPException(400, "No texts provided")
+    scores = predict(payload.texts)
+    results = [{"blur": s >= 0.5, "score": round(s, 4)} for s in scores]
+    return {"timestamp": datetime.utcnow().isoformat(timespec="seconds"), "results": results}
+
+@router.post("/log-results")
+def log_results(payload: TextRequest):
+    if not os.getenv("DATABASE_URL"):
+        raise HTTPException(503, "Database is not configured")
+    scores = predict(payload.texts)
+    results = [{"blur": s >= 0.5, "score": round(s, 4)} for s in scores]
+    success = sync_log_to_db(payload.texts, results)
+    return {"logged": success}

server.py

@@ -0,0 +1,29 @@
+import os
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from routes import router
+from model_loader import load_model
+import db_pool
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.include_router(router)
+
+@app.on_event("startup")
+def startup():
+    print("🚀 Starting Hugging Face Space API")
+    load_model()
+    if os.getenv("DATABASE_URL"):
+        db_pool.init_db_pool()
+
+@app.on_event("shutdown")
+def shutdown():
+    db_pool.close_db_pool()