3

app.py

@@ -25,20 +25,22 @@ async def predict_ndvi_api(file: UploadFile = File(...)):
         norm_img = normalize_rgb(np.array(img))
         pred_ndvi = predict_ndvi(model, norm_img)
 
-        # Prepare visualization image
-        vis_img_io = create_visualization(norm_img, pred_ndvi)
-        vis_img_io.seek(0)
-        vis_img_base64 = base64.b64encode(vis_img_io.read()).decode("utf-8")
-
-        # Convert NDVI array to nested list (e.g., for JSON)
-        ndvi_list = pred_ndvi.tolist()
-
-        return JSONResponse(
-            content={
-                "ndvi_array": ndvi_list,
-                "visualization": vis_img_base64
-            }
-        )
+        # Create preview (small visualization)
+        img_io = create_visualization(norm_img, pred_ndvi)
+        img_io.seek(0)
+        preview_img = Image.open(img_io)
+        preview_img.thumbnail((256, 256))  # Resize for fast rendering
+        preview_bytes = BytesIO()
+        preview_img.save(preview_bytes, format="PNG")
+        preview_bytes.seek(0)
+
+        img_base64 = base64.b64encode(preview_bytes.read()).decode("utf-8")
+        ndvi_array = pred_ndvi.tolist()  # Convert NumPy array to list
+
+        return {
+            "preview_base64": img_base64,
+            "ndvi_array": ndvi_array  # This can be big depending on image size
+        }
+
     except Exception as e:
         return JSONResponse(status_code=500, content={"error": str(e)})
-