Add backend files

Dockerfile

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+# Dockerfile لـ Hugging Face Spaces
+FROM python:3.11-slim
+
+# تثبيت متطلبات النظام
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# إعداد متغيرات البيئة
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    HF_HOME=/tmp/hf_home \
+    TRANSFORMERS_CACHE=/tmp/hf_home
+
+# إنشاء مستخدم غير root (مطلب Hugging Face Spaces)
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+WORKDIR /home/user/app
+
+# نسخ requirements أولاً (للاستفادة من Docker cache)
+COPY --chown=user requirements.txt .
+RUN pip install --user --upgrade pip && \
+    pip install --user -r requirements.txt
+
+# نسخ بقية الملفات
+COPY --chown=user . .
+
+# Hugging Face Spaces يستخدم المنفذ 7860
+EXPOSE 7860
+
+# تشغيل الخادم
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+"""
+Arabic Consumer Complaint Severity Classifier — Hugging Face Spaces Version
+============================================================================
+نسخة معدّلة للنشر على Hugging Face Spaces (Docker mode).
+الفرق الرئيسي عن النسخة المحلية: المنفذ 7860 (المعياري في HF Spaces).
+"""
+
+from contextlib import asynccontextmanager
+from pathlib import Path
+import os
+
+import torch
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from pydantic import BaseModel, Field
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+
+# ============================================================================
+#  CONFIGURATION
+# ============================================================================
+
+MODEL_PATH = os.getenv("MODEL_PATH", "./saved_model")
+MAX_LENGTH = 256
+
+LABELS_EN = ["Low", "Medium", "High", "Critical"]
+LABELS_AR = ["منخفضة", "متوسطة", "عالية", "حرجة"]
+
+SEVERITY_COLORS = ["#1F9D55", "#D69E2E", "#DD6B20", "#C53030"]
+SEVERITY_DESCRIPTIONS = [
+    "شكوى ذات تأثير محدود، تُعالَج ضمن المسار العادي.",
+    "شكوى تستوجب المتابعة من الجهة المختصّة في وقت معقول.",
+    "شكوى ذات أولوية عالية وتحتاج إلى معالجة سريعة.",
+    "شكوى حرجة تستدعي تدخّلاً فورياً وعاجلاً.",
+]
+
+state: dict = {}
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    print(f"[startup] Loading model from: {MODEL_PATH}")
+    if not Path(MODEL_PATH).exists():
+        print(f"[error] MODEL_PATH '{MODEL_PATH}' not found.")
+        state["model"] = None
+        state["tokenizer"] = None
+    else:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+        model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
+        model.to(device).eval()
+        state["tokenizer"] = tokenizer
+        state["model"] = model
+        state["device"] = device
+        print(f"[startup] Model ready on {device} | num_labels={model.config.num_labels}")
+    yield
+    state.clear()
+
+
+app = FastAPI(
+    title="Arabic Complaint Severity Classifier",
+    description="Thesis demo — Vision 2030 consumer protection NLP",
+    version="1.0.0",
+    lifespan=lifespan,
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+BASE_DIR = Path(__file__).parent
+app.mount("/static", StaticFiles(directory=BASE_DIR / "static"), name="static")
+templates = Jinja2Templates(directory=str(BASE_DIR / "templates"))
+
+
+class ComplaintRequest(BaseModel):
+    complaint: str = Field(..., min_length=5)
+    product_name: str | None = None
+    store_type: str | None = None
+    violation_type: str | None = None
+
+
+def predict_severity(text: str) -> dict:
+    tokenizer = state.get("tokenizer")
+    model = state.get("model")
+    if model is None or tokenizer is None:
+        raise RuntimeError("Model not loaded.")
+
+    device = state["device"]
+    inputs = tokenizer(text, return_tensors="pt", truncation=True,
+                       padding=True, max_length=MAX_LENGTH).to(device)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        probs = torch.softmax(logits, dim=-1).cpu().numpy()[0]
+
+    pred_idx = int(probs.argmax())
+    return {
+        "severity_ar": LABELS_AR[pred_idx],
+        "severity_en": LABELS_EN[pred_idx],
+        "severity_index": pred_idx,
+        "confidence": float(probs[pred_idx]),
+        "color": SEVERITY_COLORS[pred_idx],
+        "description": SEVERITY_DESCRIPTIONS[pred_idx],
+        "all_probabilities": {LABELS_EN[i]: float(probs[i]) for i in range(len(LABELS_EN))},
+        "input_length": len(text),
+    }
+
+
+@app.get("/", response_class=HTMLResponse)
+async def root(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+
+@app.post("/api/predict")
+async def predict(req: ComplaintRequest):
+    if state.get("model") is None:
+        raise HTTPException(503, "المودل غير محمّل")
+    parts = []
+    if req.product_name:
+        parts.append(f"السلعة: {req.product_name.strip()}")
+    if req.violation_type:
+        parts.append(f"نوع المخالفة: {req.violation_type.strip()}")
+    parts.append(req.complaint.strip())
+    full_text = " | ".join(parts)
+    try:
+        return predict_severity(full_text)
+    except Exception as e:
+        raise HTTPException(500, f"Prediction error: {e}")
+
+
+@app.get("/api/health")
+async def health():
+    return {
+        "status": "ok",
+        "model_loaded": state.get("model") is not None,
+        "device": str(state.get("device", "n/a")),
+        "labels": LABELS_AR,
+    }
+
+
+if __name__ == "__main__":
+    import uvicorn
+    # Hugging Face Spaces يستخدم port 7860
+    uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=False)

requirements.txt

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+fastapi==0.115.0
+uvicorn[standard]==0.32.0
+transformers==4.45.0
+torch>=2.0.0
+jinja2==3.1.4
+python-multipart==0.0.12
+pydantic==2.9.0