119 lines
2.7 KiB
Plaintext
119 lines
2.7 KiB
Plaintext
|
{
|
||
|
"cells": [
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 7,
|
||
|
"metadata": {},
|
||
|
"outputs": [],
|
||
|
"source": [
|
||
|
"import requests\n",
|
||
|
"from bs4 import BeautifulSoup\n",
|
||
|
"from pathlib import Path\n",
|
||
|
"from tqdm import tqdm"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": null,
|
||
|
"metadata": {},
|
||
|
"outputs": [],
|
||
|
"source": [
|
||
|
"this_dir_path = Path().resolve()\n",
|
||
|
"data_path = this_dir_path / \"Data\"\n",
|
||
|
"input_verb_path = data_path / \"verbs_noor.txt\"\n",
|
||
|
"htmls_path = data_path / \"htmls_noor\"\n",
|
||
|
"output_verb_path = data_path / \"verbs_noor.json\""
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": null,
|
||
|
"metadata": {},
|
||
|
"outputs": [],
|
||
|
"source": [
|
||
|
"with open(input_verb_path, \"r\") as f:\n",
|
||
|
" verbs = [verb[:-1] for verb in f.readlines()]"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": null,
|
||
|
"metadata": {},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"name": "stderr",
|
||
|
"output_type": "stream",
|
||
|
"text": [
|
||
|
"100%|██████████| 3/3 [00:01<00:00, 2.62it/s]\n"
|
||
|
]
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"for verb in tqdm(verbs):\n",
|
||
|
" response = requests.get(\"http://search.dadegan.ir/\", params={\"q\": verb})\n",
|
||
|
" with open(htmls_path / (verb + \".html\"), \"w\") as f:\n",
|
||
|
" f.write(response.text)"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 16,
|
||
|
"metadata": {},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"name": "stderr",
|
||
|
"output_type": "stream",
|
||
|
"text": [
|
||
|
"100%|██████████| 4167/4167 [00:22<00:00, 186.02it/s]\n"
|
||
|
]
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"result = {}\n",
|
||
|
"\n",
|
||
|
"for verb in tqdm(verbs):\n",
|
||
|
" with open(htmls_path / (verb + \".html\"), \"r\") as f:\n",
|
||
|
" text = f.read()\n",
|
||
|
" soup = BeautifulSoup(text, \"html.parser\")\n",
|
||
|
"\n",
|
||
|
" stems = soup.findAll(\"td\", {\"class\": \"c3\"})[1:]\n",
|
||
|
" past_stem = stems[0].text\n",
|
||
|
" present_stem = stems[1].text\n",
|
||
|
"\n",
|
||
|
" result[verb] = {}\n",
|
||
|
"\n",
|
||
|
" result[verb][\"past_stem\"] = past_stem\n",
|
||
|
" result[verb][\"present_stem\"] = present_stem"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": null,
|
||
|
"metadata": {},
|
||
|
"outputs": [],
|
||
|
"source": []
|
||
|
}
|
||
|
],
|
||
|
"metadata": {
|
||
|
"kernelspec": {
|
||
|
"display_name": ".env",
|
||
|
"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": "3.10.15"
|
||
|
}
|
||
|
},
|
||
|
"nbformat": 4,
|
||
|
"nbformat_minor": 2
|
||
|
}
|