feat(ai-service): container YOLO FastAPI pour détection plantes

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

ai-service/Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY main.py .
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8070"]

ai-service/main.py

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+import io
+import os
+from typing import List
+
+from fastapi import FastAPI, File, UploadFile
+from PIL import Image
+
+app = FastAPI(title="AI Plant Detection Service")
+
+_model = None
+MODEL_CACHE_DIR = os.environ.get("MODEL_CACHE_DIR", "/models")
+
+
+def get_model():
+    global _model
+    if _model is None:
+        from ultralytics import YOLO
+        os.makedirs(MODEL_CACHE_DIR, exist_ok=True)
+        _model = YOLO("foduucom/plant-leaf-detection-and-classification")
+    return _model
+
+
+@app.get("/health")
+def health():
+    return {"status": "ok"}
+
+
+@app.post("/detect")
+async def detect(file: UploadFile = File(...)):
+    data = await file.read()
+    img = Image.open(io.BytesIO(data)).convert("RGB")
+
+    model = get_model()
+    results = model.predict(img, conf=0.25, iou=0.45, verbose=False)
+
+    detections = []
+    if results and results[0].boxes:
+        boxes = results[0].boxes
+        names = model.names
+        for i in range(min(3, len(boxes))):
+            cls_id = int(boxes.cls[i].item())
+            conf = float(boxes.conf[i].item())
+            detections.append({
+                "class_name": names[cls_id],
+                "confidence": round(conf, 3),
+            })
+    return detections

ai-service/requirements.txt

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+fastapi==0.115.5
+uvicorn[standard]==0.32.1
+ultralytics==8.3.0
+Pillow==11.1.0
+python-multipart==0.0.12