Upload api.py

api.py

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+from fastapi import FastAPI, UploadFile, File, HTTPException, Security, Depends
+from fastapi.security.api_key import APIKeyHeader
+from fastapi.responses import FileResponse, JSONResponse
+import uvicorn
+import shutil
+import os
+from ultralytics import YOLO
+
+# ==========================
+# 🔑 Sécurité : API Key
+# ==========================
+API_KEY = "super-secret-key"  # <-- Change ici avant de partager
+api_key_header = APIKeyHeader(name="X-API-Key")
+
+def verify_api_key(api_key: str = Security(api_key_header)):
+    if api_key != API_KEY:
+        raise HTTPException(status_code=403, detail="Forbidden")
+    return api_key
+
+# ==========================
+# 🚀 Application
+# ==========================
+app = FastAPI(
+    title="Stroke Detection API",
+    version="1.0.0",
+    description="""
+    🚑 Stroke Detection API using YOLOv8
+
+    ⚠️ **Disclaimer**: This API is for **research/demo purposes only**.  
+    It is **not a certified medical tool**. Do not use for medical decisions.
+    """
+)
+
+# Charger ton modèle YOLOv8
+model = YOLO("best.pt")
+
+# ==========================
+# 📦 Endpoint JSON
+# ==========================
+@app.post("/v1/predict/")
+async def predict(
+    file: UploadFile = File(...),
+    api_key: str = Depends(verify_api_key)
+):
+    try:
+        # Sauvegarde temporaire
+        temp_file = f"temp_{file.filename}"
+        with open(temp_file, "wb") as buffer:
+            shutil.copyfileobj(file.file, buffer)
+
+        # Prédiction YOLO
+        results = model.predict(temp_file)
+
+        output = []
+        for r in results:
+            for box in r.boxes:
+                output.append({
+                    "class": r.names[int(box.cls[0].item())],
+                    "confidence": float(box.conf[0].item()),
+                    "bbox": box.xyxy[0].tolist()  # [x1, y1, x2, y2]
+                })
+
+        return JSONResponse(content={"predictions": output})
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+    finally:
+        if os.path.exists(temp_file):
+            os.remove(temp_file)
+
+# ==========================
+# 📦 Endpoint Image
+# ==========================
+@app.post("/v1/predict_image/")
+async def predict_image(
+    file: UploadFile = File(...),
+    api_key: str = Depends(verify_api_key)
+):
+    try:
+        # Sauvegarde temporaire
+        temp_file = f"temp_{file.filename}"
+        with open(temp_file, "wb") as buffer:
+            shutil.copyfileobj(file.file, buffer)
+
+        # Prédiction YOLO avec sauvegarde image annotée
+        results = model.predict(temp_file, save=True, project="runs", name="detect", exist_ok=True)
+
+        # Récupérer la dernière image annotée
+        output_dir = results[0].save_dir
+        output_path = os.path.join(output_dir, os.path.basename(temp_file))
+
+        return FileResponse(output_path, media_type="image/png")
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+    finally:
+        if os.path.exists(temp_file):
+            os.remove(temp_file)
+
+# ==========================
+# 🚀 Lancement local
+# ==========================
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)