zhuwq0 commited on
Commit
e68aadc
·
1 Parent(s): 300fbbb

update spaces

Browse files
Files changed (5) hide show
  1. .gitattributes +1 -0
  2. README.md +9 -0
  3. pipeline.py +1 -1
  4. requirements.txt +4 -1
  5. tests/test_api.ipynb +21 -10
.gitattributes CHANGED
@@ -38,3 +38,4 @@ model/190703-214543/loss.log filter=lfs diff=lfs merge=lfs -text
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  model/190703-214543/model_95.ckpt.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
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  model/190703-214543/model_95.ckpt.index filter=lfs diff=lfs merge=lfs -text
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  model/190703-214543/model_95.ckpt.meta filter=lfs diff=lfs merge=lfs -text
 
 
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  model/190703-214543/model_95.ckpt.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
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  model/190703-214543/model_95.ckpt.index filter=lfs diff=lfs merge=lfs -text
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  model/190703-214543/model_95.ckpt.meta filter=lfs diff=lfs merge=lfs -text
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+ tests/test.mseed filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -1,4 +1,13 @@
1
  ---
 
 
 
 
 
 
 
 
 
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  tags:
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  - text-classification
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  library_name: generic
 
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  ---
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+ title: Test Model
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+ emoji: 🐨
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+ colorFrom: pink
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+ colorTo: blue
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+ sdk: gradio
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+ sdk_version: 3.15.0
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+ app_file: app.py
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+ pinned: false
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+ license: mit
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  tags:
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  - text-classification
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  library_name: generic
pipeline.py CHANGED
@@ -32,7 +32,7 @@ class PreTrainedPipeline():
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  self.sess = sess
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  self.model = model
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- def __call__(self, inputs: str) -> List[List[Dict[str, float]]]:
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  """
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  Args:
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  inputs (:obj:`str`):
 
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  self.sess = sess
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  self.model = model
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+ def __call__(self, inputs: str, **kwargs) -> List[List[Dict[str, float]]]:
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  """
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  Args:
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  inputs (:obj:`str`):
requirements.txt CHANGED
@@ -1 +1,4 @@
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- tensorflow
 
 
 
 
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+ tensorflow
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+ numpy
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+ obspy
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+ pandas
tests/test_api.ipynb CHANGED
@@ -2,7 +2,7 @@
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  "cells": [
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  {
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  "cell_type": "code",
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- "execution_count": 27,
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  "metadata": {},
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  "outputs": [],
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  "source": [
@@ -12,7 +12,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 28,
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  "metadata": {},
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  "outputs": [
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  {
@@ -31,14 +31,23 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 29,
 
 
 
 
 
 
 
 
 
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  "metadata": {},
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  "outputs": [
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  {
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  "name": "stdout",
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  "output_type": "stream",
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  "text": [
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- "[[{'phase_index': 470, 'phase_score': 0.954, 'phase_type': 'P'}, {'phase_index': 570, 'phase_score': 0.839, 'phase_type': 'S'}]]\n"
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  ]
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  }
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  ],
@@ -47,8 +56,8 @@
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  "import numpy as np\n",
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  "import json\n",
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  "\n",
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- "API_URL = \"https://api-inference.huggingface.co/models/zhuwq/PhaseNet\"\n",
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- "# API_URL = \"https://api-inference.huggingface.co/models/zhuwq/test-model\"\n",
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  "headers = {\"Authorization\": \"Bearer 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\"}\n",
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  "\n",
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  "def query(payload):\n",
@@ -57,11 +66,13 @@
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  " # return json.loads(response.content.decode(\"utf-8\"))\n",
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  "\n",
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  "# array = np.random.rand(10, 3).tolist()\n",
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- "inputs = json.dumps(array.tolist())\n",
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  "data = {\n",
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- "\t# \"inputs\": \"I like you. I love you\",\n",
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- " \"inputs\": inputs,\n",
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- " \"options\":{\"wait_for_model\": True},\n",
 
 
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  "}\n",
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  "\n",
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  "output = query(data)\n",
 
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  "cells": [
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  {
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  "cell_type": "code",
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+ "execution_count": 12,
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  "metadata": {},
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  "outputs": [],
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  "source": [
 
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  },
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  {
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  "cell_type": "code",
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+ "execution_count": 13,
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  "metadata": {},
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  "outputs": [
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  {
 
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  },
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  {
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  "cell_type": "code",
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+ "execution_count": 15,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "waveform.write(\"test.mseed\", format=\"MSEED\")"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 9,
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  "metadata": {},
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  "outputs": [
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  {
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  "name": "stdout",
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  "output_type": "stream",
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  "text": [
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+ "[[{'label': 'I like you. I love you', 'score': 0.2}, {'args': []}, {}]]\n"
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  ]
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  }
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  ],
 
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  "import numpy as np\n",
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  "import json\n",
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  "\n",
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+ "# API_URL = \"https://api-inference.huggingface.co/models/zhuwq/PhaseNet\"\n",
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+ "API_URL = \"https://api-inference.huggingface.co/models/zhuwq/test-model\"\n",
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  "headers = {\"Authorization\": \"Bearer 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\"}\n",
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  "\n",
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  "def query(payload):\n",
 
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  " # return json.loads(response.content.decode(\"utf-8\"))\n",
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  "\n",
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  "# array = np.random.rand(10, 3).tolist()\n",
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+ "# inputs = json.dumps(array.tolist())\n",
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  "data = {\n",
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+ "\t\"inputs\": \"I like you. I love you\",\n",
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+ " \"arg\": 1,\n",
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+ " # \"inputs\": inputs,\n",
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+ " \"options\": {\"wait_for_model\": True},\n",
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+ " \"parameters\": {\"max_length\": 500}\n",
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  "}\n",
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  "\n",
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  "output = query(data)\n",