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 Text-Extractor-Demo (Textor)Environment Setup Create folder text_extractor_demo at desired locationOpen anaconda prompt, create new environment using command, conda create -n text_extractor_env python=3.7Activate the environment using conda activate text_extractor_envChange to text_extractor_demo folderClone repositories using below two commands. Change the username in url before executing the git clone command.git clone clone cloning repos, text-extractor-backend and text-extractor-frontend folders will be createdChange to text-extractor-frontend folder, run the below commandnpm installChange to text-extractor-backend folder, you will see requirements.txt file. Run the below command, pip install -r requirements.txtnltk.download()Select popular package in nltk pop up screen and click downloadNow, go to browser and enter following link the installer Distribution and OS according to your systemDownload the SQLiteStudio .exe file and install it in the systemStarting Demo Start API LayerOpen Anaconda prompt and activate text_extractor_env environment using conda activate text_extractor_envChange directory to text-extractor-backend, run below commandssetx FLASK_APP=app.pyflask runFlask server will start running. On successful Flask server run , below logs are seen. * Serving Flask app "app.py" (lazy loading) * Environment: production WARNING: Do not use the development server in a production environment. Use a production WSGI server instead. * Debug mode: on * Running on (Press CTRL+C to quit)Start Front-end LayerOpen Anaconda prompt and activate text_extractor_env environment using conda activate text_extractor_envChange directory to text-extractor-frontend, run below commandsng serveStart Database ClientOpen SQLiteStudio application installed.Click on “Add a Database” option Window will open as shown below. Here, in ‘File’ section, add the app.db file present in text-extractor-backend folder. 4. Click on View-> Databases to see the tables present.Running the demoOpen the browserEnter the url ScriptCurrently we will use following files for demo purposes and it is shared in text-extractor-backend/data folder “10k-AIG-Dec-31-18.pdf”, “10q-AIG-Sep-30-18.pdf”, “10q-aflac-Sep-30-18.pdf”, “10k-AIG-Dec-31-17.pdf”In UI, upload “10k-AIG-Dec-31-18.pdf” file from text-extractor-backend/data extraction process will take place in backend and this will update revenues table in database which we can query.We can keep the above file uploaded before the demo.Let’s upload another file “10q-AIG-Sep-30-18.pdf”. While uploading and info extraction is going on for the file. Let’s ask below questions to query the table,What is the total revenue of AIG in 2018? (Upload “10k-AIG-Dec-31-18.pdf”)Give the Policy fees of AIG in 2017. (Upload “10k-AIG-Dec-31-18.pdf”)Give the Policy fees of AIG in 3rd quarter of 2017. (Upload “10q-AIG-Sep-30-18.pdf”)Find the premium revenue of AIG in 2018 for quarter 3. (Upload “10q-AIG-Sep-30-18.pdf”)What is the Net investment income for Aflac in 2018,quarter 3? ( Upload 10q-aflac-Sep-30-18.pdf)Find total realized investment gains of Aflac in year 2018, quarter 3? ( Upload 10q-aflac-Sep-30-18.pdf)What is the Net investment income of AIG in 2014? (Upload “10k-AIG-Dec-31-17.pdf”)Other InfoCurrently, below attributes can be queried,PremiumsTotal revenuePolicy feesNet investment incomeTotal realised investment gainsCurrently, below company names data can be queried,AIGAflac ................
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