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{
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"import obspy\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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": 15,
"metadata": {},
"outputs": [],
"source": [
"waveform.write(\"test.mseed\", format=\"MSEED\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[{'label': 'I like you. I love you', 'score': 0.2}, {'args': []}, {}]]\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",
" \"arg\": 1,\n",
" # \"inputs\": inputs,\n",
" \"options\": {\"wait_for_model\": True},\n",
" \"parameters\": {\"max_length\": 500}\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
}
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