Add Docker build and deploy pipeline

ai-moderation/.dockerignore

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+__pycache__
+*.pyc
+.env
+venv
+.venv
+models
+.idea
+.git

ai-moderation/Dockerfile

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+FROM python:3.12-slim
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1
+WORKDIR /app
+
+# torch ставим отдельно в облегчённой версии для процессора (без CUDA)
+RUN pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
+
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+COPY app ./app
+
+RUN useradd -r -u 1001 app \
+ && mkdir -p /models \
+ && chown app /models
+USER app
+
+ENV MODEL_CACHE_DIR=/models
+EXPOSE 8000
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

ai-moderation/app/ml/image/weapon_detector.py

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+import torch
+from PIL import Image
+from transformers import Owlv2ForObjectDetection, Owlv2Processor
+
+from app.config.settings import settings
+from app.ml.image.weapon_prediction_result import WeaponPredictionResult
+
+
+class WeaponDetector:
+
+  QUERIES = [
+    "a gun",
+    "a pistol",
+    "a revolver",
+    "a rifle",
+    "a shotgun",
+    "an assault rifle",
+    "a knife",
+    "a machete",
+    "a sword",
+    "a bomb",
+    "a grenade",
+    "a crossbow",
+    "a bow and arrow",
+    "brass knuckles",
+    "a nunchaku"
+  ]
+
+  def __init__(self):
+    self.device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Loading OWLv2 weapon detector on {self.device}")
+
+    self.model = Owlv2ForObjectDetection.from_pretrained(
+        settings.WEAPON_MODEL
+    )
+    self.processor = Owlv2Processor.from_pretrained(
+        settings.WEAPON_MODEL
+    )
+
+    self.model.to(self.device)
+    self.model.eval()
+
+  def predict(
+      self,
+      image: Image.Image
+  ) -> WeaponPredictionResult:
+    inputs = self.processor(
+        text=self.QUERIES,
+        images=image,
+        return_tensors="pt"
+    )
+
+    inputs = {
+      key: value.to(self.device)
+      for key, value in inputs.items()
+    }
+
+    with torch.no_grad():
+      outputs = self.model(**inputs)
+
+    target_sizes = torch.tensor([image.size[::-1]])
+
+    results = self.processor.post_process_grounded_object_detection(
+        outputs,
+        threshold=0.0,
+        target_sizes=target_sizes
+    )[0]
+
+    scores = results["scores"].tolist()
+    labels = results["labels"].tolist()
+    boxes = results["boxes"].tolist()
+
+    image_area = image.width * image.height
+    label_scores = {}
+
+    for score, label_index, box in zip(scores, labels, boxes):
+      box_area = (box[2] - box[0]) * (box[3] - box[1])
+
+      if box_area < image_area * settings.WEAPON_MIN_AREA_FRACTION:
+        continue
+
+      if score >= settings.WEAPON_THRESHOLD:
+        query = self.QUERIES[label_index]
+        label_scores[query] = max(
+            label_scores.get(query, 0.0),
+            score
+        )
+
+    if not label_scores:
+      return WeaponPredictionResult(
+          label="normal",
+          score=0.0
+      )
+
+    best_label = max(
+        label_scores,
+        key=label_scores.get
+    )
+
+    print("====================")
+    print("WEAPON RESULT")
+    print("Scores:", label_scores)
+
+    return WeaponPredictionResult(
+        label=best_label,
+        score=label_scores[best_label],
+        detected_labels=sorted(label_scores.keys()),
+        box_count=len(label_scores)
+    )

ai-moderation/app/ml/image/weapon_prediction_result.py

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+from dataclasses import dataclass, field
+
+
+@dataclass(slots=True)
+class WeaponPredictionResult:
+  label: str
+
+  score: float
+
+  detected_labels: list[str] = field(
+      default_factory=list
+  )
+
+  box_count: int = 0