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PiperOrigin-RevId: 680700455

health_acoustic_representations/hear_demo.ipynb

@@ -351,47 +351,29 @@
         "            print(f\"Attempt {retries} failed. Retrying in {delay} seconds...\")\n",
         "            time.sleep(delay)\n",
         "\n",
-        "    raise Exception(\"Unexpected error in make_prediction_with_exponential_backoff\")\n",
-        "\n",
-        "\n",
-        "# 1000 batches of 4 clips. This is the format expected for the raw audio endpoint\n",
-        "# (202). For the GCS URI endpoint, you would need to format this appropriately.\n",
-        "instances = np.random.uniform((1000, 4, 32000))  # update with your data\n",
-        "\n",
-        "with concurrent.futures.ThreadPoolExecutor(max_workers=50) as executor:\n",
-        "  futures = [executor.submit(make_prediction_with_exponential_backoff, client, endpoint, instance) for instance in instances]\n",
-        "\n",
-        "  for future in concurrent.futures.as_completed(futures):\n",
-        "    try:\n",
-        "      responses.append(future.result())\n",
-        "    except Exception as e:\n",
-        "      print(\"An error occurred:\", e)"
+        "    raise Exception(\"Unexpected error in make_prediction_with_exponential_backoff\")\n"
       ],
       "outputs": [],
       "execution_count": null
     },
     {
       "metadata": {
-        "id": "zGWp-xf8ny0G"
+        "id": "Vx3gaUe0cb_h"
       },
       "cell_type": "code",
       "source": [
-        "import time\n",
-        "import concurrent.futures\n",
-        "\n",
-        "start_time = time.time()\n",
-        "responses = []\n",
+        "# 1000 batches of 4 clips. This is the format expected for the raw audio endpoint\n",
+        "# (202). For the GCS URI endpoint, you would need to format this appropriately.\n",
+        "instances = np.random.uniform((1000, 4, 32000))  # update with your data\n",
         "\n",
         "with concurrent.futures.ThreadPoolExecutor(max_workers=50) as executor:\n",
-        "  futures = [executor.submit(make_prediction, client, endpoint, instance) for instance in instances]\n",
+        "  futures = [executor.submit(make_prediction_with_exponential_backoff, client, endpoint, instance) for instance in instances]\n",
         "\n",
         "  for future in concurrent.futures.as_completed(futures):\n",
         "    try:\n",
         "      responses.append(future.result())\n",
         "    except Exception as e:\n",
-        "      print(\"An error occurred:\", e)\n",
-        "\n",
-        "print(time.time() - start_time)"
+        "      print(\"An error occurred:\", e)"
       ],
       "outputs": [],
       "execution_count": null