add

app.py

@@ -18,7 +18,7 @@ import uvicorn
 BASE_DIR = Path(__file__).resolve().parent
 MODEL_DIR = BASE_DIR / "models"
 PYTORCH_PATH = MODEL_DIR / "pytorch_model.pth"
-TENSORFLOW_PATH = MODEL_DIR / "model_best.keras"
+TENSORFLOW_PATH = MODEL_DIR / "model_best.keras" 
 
 CLASSES = ["buildings", "forest", "glacier", "mountain", "sea", "street"]
 CONFIDENCE_THRESHOLD = 0.6
@@ -202,9 +202,17 @@ def predict(
             raise HTTPException(status_code=400, detail=f"Impossible de lire l'image: {exc}")
     else:
         try:
-            img = load_image_from_url(image_url)
-        except ValueError as exc:
-            raise HTTPException(status_code=400, detail=str(exc))
+            # Accepte URL HTTP/HTTPS, data URL (data:image/...;base64,...) ou base64 pur
+            if image_url.startswith("data:"):
+                img = load_image_from_url(image_url)
+            elif image_url.startswith(("http://", "https://")):
+                img = load_image_from_url(image_url)
+            else:
+                # Assume chaîne base64 pure
+                image_data = base64.b64decode(image_url)
+                img = Image.open(io.BytesIO(image_data)).convert("RGB")
+        except Exception as exc:
+            raise HTTPException(status_code=400, detail=f"Impossible de traiter l'image: {exc}")
 
     predicted_class, confidence, all_probs = classify(img, model_choice)
     return {