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align=\"left\"\u003e Veri Etiketleme Aracı \u003c/h1\u003e\nBu araç veri kazıma aşamasından sonra verilerin istenilen kategoriye göre etiketlenmesi ve kullanılmayacak olan verilerin database'ten silinmesi ile makine öğrenmesi modellerini kullanmak için etiketleme adımınını otomatikleştirmek amaçlı yapılmıştır.\n\n\n\n[streamlit-main-2022-12-04-04-12-22.webm](https://user-images.githubusercontent.com/92747017/205483943-8559ed1e-d764-4313-9d13-12bb1bd51566.webm)\n\n\n\n\u003ch2 align=\"left\"\u003e Geliştirme Ortamını Ayarlamak \u003c/h2\u003e\n\u003ca href=\"https://www.python.org/downloads/\" target=\"blank\"\u003e\u003cimg align=\"center\" src=\"https://img.shields.io/pypi/pyversions/Scrapy.svg\"\u003e\u003c/a\u003e\n\n\n```\npython3 --version\n```\n\n- Virtual environment oluşturma ve aktif etme\n  \n```\ncd venv-folder-path \npython3 -m venv \u003cvenv-name\u003e\nsource \u003cvenv-name\u003e /bin/activate\n```  \n  \n  \n- Terminal üzerinden streamlit, psycopg2(postgresql) ve pandas indirme\n\n\u003ca href=\"https://docs.streamlit.io/library/get-started/installation\" target=\"blank\"\u003e\u003cimg align=\"center\" src=\"https://docs.streamlit.io/logo.svg\"\u003e\u003c/a\u003e\n\n```\npip install streamlit\n```\n\n\u003ca href=\"https://pypi.org/project/psycopg2/\" target=\"blank\"\u003e\u003cimg align=\"center\" src=\"https://pypi.org/static/images/logo-small.95de8436.svg\"\u003e\u003c/a\u003e\n```\npip install psycopg2\n```\n\u003ca href=\"https://pypi.org/project/pandas/\" target=\"blank\"\u003e\u003cimg align=\"center\" src=\"https://warehouse-camo.ingress.cmh1.psfhosted.org/49dfa37dbb471fb97b1d94f27e3b343fb74ebcc1/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f70616e6461732e737667\"\u003e\u003c/a\u003e\n```\npip install pandas\n```\n\u003ch2 align=\"left\"\u003e Veri Etiketleme Aracı Uygulama Aşamaları \u003c/h2\u003e\n\n- İlk olarak, pgAdmin veya psql gibi herhangi bir istemci aracını kullanarak PostgreSQL veritabanı sunucusunda oturum açın.\n- İkinci olarak, PostgreSQL veritabanı sunucusunda adlandırılmış yeni bir veritabanı oluşturmak için aşağıdaki ifadeyi kullanın\n\n```\nCREATE DATABASE complaints;\n```\n- Postgresql üzerinde tablo oluşturun\n\n```\ncreate table complaints(\n\tid integer primary key,\n\tname varchar(50),\n\ttext text,\n\tlink varchar(150)\n)\n```\n\n- Tabloya bank.csv'yi import edin\n```\nCOPY persons(id, name, text, link)\nFROM 'csv-path\\bank.csv'\nDELIMITER ','\nCSV HEADER;\n```\n- python dosyanızda kütüphaneleri import edin.\n\n```\nimport psycopg2\nimport streamlit\nimport pandas as pd\n```\n\n- PostgreSQL veritabanına bağlanın\n\n```\nhostname = \"localhost\"\nport_id = portId\ndatabase = \"databaseName\"\nusername = \"username\"\npassword = \"password\"\n\ndef sqlData(raw_code):\n    with psycopg2.connect(host=hostname,port=port_id,dbname=database,user=username,password=password) as conn:\n        with conn.cursor() as curs:\n            curs.execute(raw_code)\n            conn.commit()\n            data = curs.fetchall()\n            return data\n          \n          \ndata = sqlExecute(\"select * from complaints\")\ndf = pd.DataFrame(data, columns=[\"id\", \"name\", \"text\", \"link\"])\nst.dataframe(df)\n```\n- Web sayfasında görüntülemek için streamlit'i terminal üzerinden çalıştırın\n\n```\nstreamlit run main.py\n```\n\n\n\n\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Felifftosunn%2Fdata-labeling","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Felifftosunn%2Fdata-labeling","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Felifftosunn%2Fdata-labeling/lists"}