{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import obspy\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3000, 3)\n"
     ]
    }
   ],
   "source": [
    "waveform = obspy.read()\n",
    "array = np.array([x.data for x in waveform]).T\n",
    "print(array.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[{'phase_index': 470, 'phase_score': 0.954, 'phase_type': 'P'}, {'phase_index': 570, 'phase_score': 0.839, 'phase_type': 'S'}]]\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "import json\n",
    "\n",
    "API_URL = \"https://api-inference.huggingface.co/models/zhuwq/PhaseNet\"\n",
    "# API_URL = \"https://api-inference.huggingface.co/models/zhuwq/test-model\"\n",
    "headers = {\"Authorization\": \"Bearer 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\"}\n",
    "\n",
    "def query(payload):\n",
    "    response = requests.post(API_URL, headers=headers, json=payload)\n",
    "    return response.json()\n",
    "    # return json.loads(response.content.decode(\"utf-8\"))\n",
    "\n",
    "# array = np.random.rand(10, 3).tolist()\n",
    "inputs = json.dumps(array.tolist())\n",
    "data = {\n",
    "\t# \"inputs\": \"I like you. I love you\",\n",
    "    \"inputs\": inputs,\n",
    "    \"options\":{\"wait_for_model\": True},\n",
    "}\n",
    "\n",
    "output = query(data)\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "0efb5d07c150d814a79610ed835fac9f37a29f75f64726a0e33cb3dca03bca5e"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}