Commit
•
1108a3a
1
Parent(s):
82acdca
Upload 29 files
Browse files- .idea/.gitignore +8 -0
- .idea/inspectionProfiles/profiles_settings.xml +6 -0
- .idea/invoice_extractor.iml +8 -0
- .idea/material_theme_project_new.xml +12 -0
- .idea/misc.xml +7 -0
- .idea/modules.xml +8 -0
- .idea/vcs.xml +6 -0
- .idea/workspace.xml +164 -0
- Extracted_Invoice.csv +4 -0
- Extracted_Invoice1.csv +13 -0
- Extracted_Invoice_001.csv +6 -0
- Invoice_001.pdf +0 -0
- Political_Promises_Fine_Tuning.csv +11 -0
- __pycache__/utils.cpython-311.pyc +0 -0
- app.py +40 -0
- extracted_data.csv +1 -0
- pdf_reader.py +74 -0
- pdfkit-invoice/.gitignore +2 -0
- pdfkit-invoice/README.md +38 -0
- pdfkit-invoice/createInvoice.js +193 -0
- pdfkit-invoice/index.js +31 -0
- pdfkit-invoice/logo.png +0 -0
- pdfkit-invoice/package-lock.json +1395 -0
- pdfkit-invoice/package.json +16 -0
- pdfkit-invoice/screenshot.png +0 -0
- pillmatePPTXBackground.webp +0 -0
- real_promises_only.py +114 -0
- report.md +0 -0
- utils.py +79 -0
.idea/.gitignore
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# Default ignored files
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/shelf/
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/workspace.xml
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# Editor-based HTTP Client requests
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/httpRequests/
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# Datasource local storage ignored files
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/dataSources/
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/dataSources.local.xml
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.idea/inspectionProfiles/profiles_settings.xml
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<component name="InspectionProjectProfileManager">
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<settings>
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<option name="USE_PROJECT_PROFILE" value="false" />
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<version value="1.0" />
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</settings>
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</component>
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.idea/invoice_extractor.iml
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<?xml version="1.0" encoding="UTF-8"?>
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$" />
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<orderEntry type="jdk" jdkName="llm_env" jdkType="Python SDK" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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</module>
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.idea/material_theme_project_new.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="MaterialThemeProjectNewConfig">
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<option name="metadata">
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<MTProjectMetadataState>
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<option name="migrated" value="true" />
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<option name="pristineConfig" value="false" />
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<option name="userId" value="13c809ac:18ec6bfab90:-7ffe" />
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</MTProjectMetadataState>
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</option>
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</component>
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</project>
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.idea/misc.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="Black">
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<option name="sdkName" value="C:\Users\kosti\anaconda3" />
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</component>
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<component name="ProjectRootManager" version="2" project-jdk-name="llm_env" project-jdk-type="Python SDK" />
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</project>
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.idea/modules.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectModuleManager">
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<modules>
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<module fileurl="file://$PROJECT_DIR$/.idea/invoice_extractor.iml" filepath="$PROJECT_DIR$/.idea/invoice_extractor.iml" />
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</modules>
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</component>
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</project>
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.idea/vcs.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="VcsDirectoryMappings">
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<mapping directory="$PROJECT_DIR$/pdfkit-invoice" vcs="Git" />
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</component>
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</project>
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.idea/workspace.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="AutoImportSettings">
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<option name="autoReloadType" value="SELECTIVE" />
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<component name="ChangeListManager">
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<list default="true" id="5d5b8dc8-e7fe-43d9-8802-74478f493adb" name="Changes" comment="" />
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<option name="SHOW_DIALOG" value="false" />
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<option name="HIGHLIGHT_CONFLICTS" value="true" />
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<component name="FileTemplateManagerImpl">
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<option name="RECENT_TEMPLATES">
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<list>
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<option value="Python Script" />
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</list>
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</option>
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</component>
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<component name="Git.Settings">
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<option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$/pdfkit-invoice" />
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</component>
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<component name="ProjectColorInfo">{
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"associatedIndex": 4
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}</component>
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<component name="ProjectId" id="2f2dqHtOqUJDiIt51G8oHM6rauH" />
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<component name="ProjectViewState">
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<option name="hideEmptyMiddlePackages" value="true" />
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<option name="showLibraryContents" value="true" />
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</component>
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<component name="PropertiesComponent">{
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"keyToString": {
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"Python.pdf_reader.executor": "Run",
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"Python.real_promises_only.executor": "Run",
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"RunOnceActivity.ShowReadmeOnStart": "true",
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"git-widget-placeholder": "master",
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"node.js.detected.package.eslint": "true",
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"node.js.detected.package.tslint": "true",
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"node.js.selected.package.eslint": "(autodetect)",
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"node.js.selected.package.tslint": "(autodetect)",
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"nodejs_package_manager_path": "npm",
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"vue.rearranger.settings.migration": "true"
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}
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}</component>
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<component name="RunManager" selected="Python.real_promises_only">
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<configuration name="app" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true">
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<module name="invoice_extractor" />
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<option name="ENV_FILES" value="" />
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<option name="INTERPRETER_OPTIONS" value="" />
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<option name="PARENT_ENVS" value="true" />
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<envs>
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<env name="PYTHONUNBUFFERED" value="1" />
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</envs>
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<option name="SDK_HOME" value="" />
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<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
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<option name="IS_MODULE_SDK" value="true" />
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<option name="ADD_CONTENT_ROOTS" value="true" />
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<option name="ADD_SOURCE_ROOTS" value="true" />
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<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
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<option name="SCRIPT_NAME" value="C:\Users\kosti\PycharmProjects\invoice_extractor\app.py" />
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<option name="PARAMETERS" value="" />
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<option name="SHOW_COMMAND_LINE" value="false" />
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<option name="EMULATE_TERMINAL" value="false" />
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<option name="MODULE_MODE" value="false" />
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<option name="REDIRECT_INPUT" value="false" />
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<option name="INPUT_FILE" value="" />
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<method v="2" />
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</configuration>
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<configuration name="pdf_reader" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
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<module name="invoice_extractor" />
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<option name="ENV_FILES" value="" />
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<option name="INTERPRETER_OPTIONS" value="" />
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<option name="PARENT_ENVS" value="true" />
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<envs>
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<env name="PYTHONUNBUFFERED" value="1" />
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<option name="SDK_HOME" value="" />
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<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
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<option name="IS_MODULE_SDK" value="true" />
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<option name="ADD_CONTENT_ROOTS" value="true" />
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<option name="ADD_SOURCE_ROOTS" value="true" />
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<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
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<option name="SCRIPT_NAME" value="$PROJECT_DIR$/pdf_reader.py" />
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<option name="PARAMETERS" value="" />
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<option name="SHOW_COMMAND_LINE" value="false" />
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<option name="EMULATE_TERMINAL" value="false" />
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<option name="MODULE_MODE" value="false" />
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<option name="REDIRECT_INPUT" value="false" />
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<option name="INPUT_FILE" value="" />
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<method v="2" />
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</configuration>
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<configuration name="real_promises_only" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
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<module name="invoice_extractor" />
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<option name="ENV_FILES" value="" />
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<option name="PARENT_ENVS" value="true" />
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<env name="PYTHONUNBUFFERED" value="1" />
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<option name="SDK_HOME" value="" />
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<option name="ADD_SOURCE_ROOTS" value="true" />
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<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
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<option name="SCRIPT_NAME" value="$PROJECT_DIR$/real_promises_only.py" />
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<option name="PARAMETERS" value="" />
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<option name="SHOW_COMMAND_LINE" value="false" />
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<option name="EMULATE_TERMINAL" value="false" />
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<option name="MODULE_MODE" value="false" />
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<option name="REDIRECT_INPUT" value="false" />
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<option name="INPUT_FILE" value="" />
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<method v="2" />
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</configuration>
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<recent_temporary>
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<item itemvalue="Python.real_promises_only" />
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<item itemvalue="Python.pdf_reader" />
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</recent_temporary>
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</component>
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<component name="SharedIndexes">
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<component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
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<component name="TaskManager">
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<task active="true" id="Default" summary="Default task">
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<changelist id="5d5b8dc8-e7fe-43d9-8802-74478f493adb" name="Changes" comment="" />
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<created>1713003295790</created>
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<option name="number" value="Default" />
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<option name="presentableId" value="Default" />
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<updated>1713003295790</updated>
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<servers />
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<component name="TypeScriptGeneratedFilesManager">
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<component name="XDebuggerManager">
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<breakpoint-manager>
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<breakpoints>
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line">
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<url>file://$PROJECT_DIR$/app.py</url>
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<line>8</line>
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<option name="timeStamp" value="1" />
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</line-breakpoint>
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</breakpoint-manager>
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</component>
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<component name="com.intellij.coverage.CoverageDataManagerImpl">
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<SUITE FILE_PATH="coverage/invoice_extractor$real_promises_only.coverage" NAME="real_promises_only Coverage Results" MODIFIED="1713113163159" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
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+
<SUITE FILE_PATH="coverage/invoice_extractor$pdf_reader.coverage" NAME="pdf_reader Coverage Results" MODIFIED="1713010596714" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
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</component>
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</project>
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Extracted_Invoice.csv
ADDED
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"
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{'Invoice no.': '001', 'Description': ['Widget A', 'Widget B'], 'Quantity': ['2', '5'], 'Date': '2024-04-13', 'Unit price': ['$30.00', '$20.00'], 'Amount': ['$60.00', '$100.00'], 'Total': '$168.00', 'email': 'info@gptwidgets.com', 'phone number': '(123) 456-7890', 'address': ['123 AI Lane', 'Model Town', 'OpenAI'], 'customer name': 'John Doe', 'customer address': '789 Coding Rd, Dev City', 'customer email': 'johndoe@example.com', 'customer phone': '(987) 654-3210'}"
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Extracted_Invoice1.csv
ADDED
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"
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- Invoice no.: 001
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5 |
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- Description: Widget A, Widget B
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6 |
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- Quantity: 2, 5
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7 |
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- Date: 2024-04-13
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8 |
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- Unit price: $30.00, $20.00
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- Amount: $60.00, $100.00
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- Total: $160.00
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- Email: info@gptwidgets.com
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- Phone number: (123) 456-7890
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13 |
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- Address: 123 AI Lane, Model Town, OpenAI"
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Extracted_Invoice_001.csv
ADDED
@@ -0,0 +1,6 @@
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|
1 |
+
0
|
2 |
+
" {'email': 'info@gptwidgets.com', 'phone number': '(123) 456-7890', 'address': '123 AI Lane, Model Town, OpenAI'}
|
3 |
+
|
4 |
+
{'Invoice no.': '001', 'Description': 'Widget A', 'Quantity': '2', 'Date': '2024-04-13', 'Unit price': '30.00', 'Amount': '60.00', 'Total': '160.00'}
|
5 |
+
{'Invoice no.': '001', 'Description': 'Widget B', 'Quantity': '5', 'Date': '2024-04-13', 'Unit price': '20.00', 'Amount': '100.00', 'Total': '160.00'}
|
6 |
+
{'email': 'info@gptwidgets.com', 'phone number': '(123) 456-7890', 'address': '123 AI Lane, Model Town, OpenAI'}"
|
Invoice_001.pdf
ADDED
Binary file (1.63 kB). View file
|
|
Political_Promises_Fine_Tuning.csv
ADDED
@@ -0,0 +1,11 @@
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|
1 |
+
text,label
|
2 |
+
"We will build 10,000 kilometres of subway lines in the next ten years.",promise
|
3 |
+
We expect the Japanese to increase tariffs next year.,not promise
|
4 |
+
We will work towards making Canada a better place.,not promise
|
5 |
+
AI will cause the loss of a million jobs next year.,not promise
|
6 |
+
The government will provide free college education to all students by 2025.,promise
|
7 |
+
Interest rates are expected to rise by next quarter.,not promise
|
8 |
+
"We will eliminate the income tax for individuals earning less than $30,000 by next year.",promise
|
9 |
+
The economy is predicted to grow by 4% in the coming fiscal year.,not promise
|
10 |
+
Our administration will ensure the construction of 500 new schools in rural areas within five years.,promise
|
11 |
+
Climate change will likely increase temperatures globally.,not promise
|
__pycache__/utils.cpython-311.pyc
ADDED
Binary file (4.02 kB). View file
|
|
app.py
ADDED
@@ -0,0 +1,40 @@
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|
1 |
+
import streamlit as st
|
2 |
+
from dotenv import load_dotenv
|
3 |
+
|
4 |
+
from utils import *
|
5 |
+
|
6 |
+
|
7 |
+
def main():
|
8 |
+
load_dotenv()
|
9 |
+
|
10 |
+
st.set_page_config(page_title="Invoice Extractor")
|
11 |
+
st.title("Invoice Extractor Bot...")
|
12 |
+
st.subheader("I can help you in extracting invoice data")
|
13 |
+
|
14 |
+
# Upload the Invoices (pdf files)
|
15 |
+
pdf = st.file_uploader("Upload invoices here", type=["pdf"], accept_multiple_files=True)
|
16 |
+
|
17 |
+
submit = st.button("Extract Data")
|
18 |
+
|
19 |
+
if submit:
|
20 |
+
with st.spinner("Loading invoices..."):
|
21 |
+
df = create_docs(pdf)
|
22 |
+
# st.write('managed to extract data from pdf')
|
23 |
+
# st.write(df)
|
24 |
+
st.write(df.head())
|
25 |
+
|
26 |
+
# Saving the dataframe as CSV file
|
27 |
+
data_as_csv = df.to_csv(index=False).encode('utf-8')
|
28 |
+
st.download_button(
|
29 |
+
"Download CSV",
|
30 |
+
data_as_csv,
|
31 |
+
"invoice_extracted_data.csv",
|
32 |
+
"text/csv",
|
33 |
+
key="download-tools-csv",
|
34 |
+
)
|
35 |
+
|
36 |
+
st.success("Hope I was able to save your time <3")
|
37 |
+
|
38 |
+
|
39 |
+
if __name__ == '__main__':
|
40 |
+
main()
|
extracted_data.csv
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
|
pdf_reader.py
ADDED
@@ -0,0 +1,74 @@
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|
1 |
+
# import pandas as pd
|
2 |
+
# import json
|
3 |
+
#
|
4 |
+
#
|
5 |
+
# # Function to parse the LLM output and create a DataFrame
|
6 |
+
# def process_llm_data(llm_data):
|
7 |
+
# # Assuming llm_data is a string that exactly matches the dictionary format
|
8 |
+
# # Safely convert string representation of dictionary to a dictionary
|
9 |
+
# data_dict = eval(llm_data.strip())
|
10 |
+
#
|
11 |
+
# # Convert the dictionary to a DataFrame
|
12 |
+
# # Since our example has comma-separated values in strings for some fields, let's handle that
|
13 |
+
# for key, value in data_dict.items():
|
14 |
+
# if ',' in value:
|
15 |
+
# data_dict[key] = [v.strip() for v in value.split(',')]
|
16 |
+
# else:
|
17 |
+
# data_dict[key] = [value]
|
18 |
+
#
|
19 |
+
# df = pd.DataFrame(data_dict)
|
20 |
+
#
|
21 |
+
# # Saving the DataFrame to a CSV file
|
22 |
+
# csv_file_path = "Invoice_Data.csv"
|
23 |
+
# df.to_csv(csv_file_path, index=False)
|
24 |
+
#
|
25 |
+
# return csv_file_path
|
26 |
+
#
|
27 |
+
#
|
28 |
+
# # Example usage
|
29 |
+
# llm_extracted_data = """
|
30 |
+
# {
|
31 |
+
# 'Invoice no.': '001',
|
32 |
+
# 'Description': 'Widget A, Widget B',
|
33 |
+
# 'Quantity': '2, 5',
|
34 |
+
# 'Date': '2024-04-13',
|
35 |
+
# 'Unit price': '$30.00, $20.00',
|
36 |
+
# 'Amount': '$60.00, $100.00',
|
37 |
+
# 'Total': '$160.00',
|
38 |
+
# 'Email': 'info@gptwidgets.com',
|
39 |
+
# 'Phone number': '(123) 456-7890',
|
40 |
+
# 'Address': '123 AI Lane, Model Town, OpenAI'
|
41 |
+
# }
|
42 |
+
# """
|
43 |
+
#
|
44 |
+
# # Call the function to process the data and create a CSV
|
45 |
+
# csv_path = process_llm_data(llm_extracted_data)
|
46 |
+
# print(f"Data saved to CSV at: {csv_path}")
|
47 |
+
|
48 |
+
import pandas as pd
|
49 |
+
from utils import get_pdf_text, extracted_data
|
50 |
+
from dotenv import load_dotenv
|
51 |
+
|
52 |
+
load_dotenv()
|
53 |
+
|
54 |
+
|
55 |
+
def save_data_to_csv(data, filename):
|
56 |
+
# Convert the dictionary to a DataFrame
|
57 |
+
df = pd.DataFrame([data])
|
58 |
+
print(df)
|
59 |
+
# Save the DataFrame to a CSV file
|
60 |
+
csv_path = f"{filename}.csv"
|
61 |
+
df.to_csv(csv_path, index=False)
|
62 |
+
print(f"Data saved to {csv_path}")
|
63 |
+
|
64 |
+
|
65 |
+
# Example usage
|
66 |
+
print('filename is {}'.format("Invoice_001.pdf"))
|
67 |
+
raw_data = get_pdf_text("Invoice_001.pdf")
|
68 |
+
print('raw_data is {}'.format(raw_data))
|
69 |
+
|
70 |
+
llm_extracted_data = extracted_data(raw_data)
|
71 |
+
print('llm_extracted_data is {}'.format(llm_extracted_data))
|
72 |
+
|
73 |
+
# Assuming llm_extracted_data is a dictionary containing the extracted fields
|
74 |
+
save_data_to_csv(llm_extracted_data, "Extracted_Invoice1")
|
pdfkit-invoice/.gitignore
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
node_modules
|
2 |
+
invoice.pdf
|
pdfkit-invoice/README.md
ADDED
@@ -0,0 +1,38 @@
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
1 |
+
# PDFKit Invoices
|
2 |
+
|
3 |
+
<center>
|
4 |
+
<a href="https://pspdfkit.com/web">
|
5 |
+
<img src="./screenshot.png" alt="Screenshot of an invoice PDF in the PSPDFKit for Web reader." width="1009">
|
6 |
+
</a>
|
7 |
+
</center>
|
8 |
+
|
9 |
+
## Prerequisites
|
10 |
+
|
11 |
+
- [Node.js](http://nodejs.org/) (with npm or Yarn)
|
12 |
+
|
13 |
+
## Getting Started
|
14 |
+
|
15 |
+
This repository is an example of how to make PDF invoices with PDFKit.
|
16 |
+
|
17 |
+
There are two important fields in this this repository:
|
18 |
+
|
19 |
+
- [`index.js`](index.js) is the main entry point. It defines the data structure used to create the invoices.
|
20 |
+
- [`createInvoice.js`](createInvoice.js) exports a function that can be used to create invoice PDFs.
|
21 |
+
|
22 |
+
To get started, use the following commands:
|
23 |
+
|
24 |
+
```bash
|
25 |
+
git clone https://github.com/PSPDFKit-labs/pdfkit-invoice.git
|
26 |
+
|
27 |
+
npm install # Install dependencies
|
28 |
+
|
29 |
+
npm start # This will create an invoice.pdf file in the root of the project.
|
30 |
+
```
|
31 |
+
|
32 |
+
To learn more about this project, make sure to read the accompanying [blog post](https://pspdfkit.com/blog/2019/generate-invoices-pdfkit-node)
|
33 |
+
|
34 |
+
## Contributing
|
35 |
+
|
36 |
+
Please ensure
|
37 |
+
[you have signed our CLA](https://pspdfkit.com/guides/web/current/miscellaneous/contributing/) so that we can
|
38 |
+
accept your contributions.
|
pdfkit-invoice/createInvoice.js
ADDED
@@ -0,0 +1,193 @@
|
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|
|
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|
|
|
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|
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|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
1 |
+
const fs = require("fs");
|
2 |
+
const PDFDocument = require("pdfkit");
|
3 |
+
|
4 |
+
function createInvoice(invoice, path) {
|
5 |
+
let doc = new PDFDocument({ size: "A4", margin: 50 });
|
6 |
+
|
7 |
+
generateHeader(doc);
|
8 |
+
generateCustomerInformation(doc, invoice);
|
9 |
+
generateInvoiceTable(doc, invoice);
|
10 |
+
generateFooter(doc);
|
11 |
+
|
12 |
+
doc.end();
|
13 |
+
doc.pipe(fs.createWriteStream(path));
|
14 |
+
}
|
15 |
+
|
16 |
+
function generateHeader(doc) {
|
17 |
+
doc
|
18 |
+
.image("logo.png", 50, 45, { width: 50 })
|
19 |
+
.fillColor("#444444")
|
20 |
+
.fontSize(20)
|
21 |
+
.text("ACME Inc.", 110, 57)
|
22 |
+
.fontSize(10)
|
23 |
+
.text("ACME Inc.", 200, 50, { align: "right" })
|
24 |
+
.text("123 Main Street", 200, 65, { align: "right" })
|
25 |
+
.text("New York, NY, 10025", 200, 80, { align: "right" })
|
26 |
+
.moveDown();
|
27 |
+
}
|
28 |
+
|
29 |
+
function generateCustomerInformation(doc, invoice) {
|
30 |
+
doc
|
31 |
+
.fillColor("#444444")
|
32 |
+
.fontSize(20)
|
33 |
+
.text("Invoice", 50, 160);
|
34 |
+
|
35 |
+
generateHr(doc, 185);
|
36 |
+
|
37 |
+
const customerInformationTop = 200;
|
38 |
+
|
39 |
+
doc
|
40 |
+
.fontSize(10)
|
41 |
+
.text("Invoice Number:", 50, customerInformationTop)
|
42 |
+
.font("Helvetica-Bold")
|
43 |
+
.text(invoice.invoice_nr, 150, customerInformationTop)
|
44 |
+
.font("Helvetica")
|
45 |
+
.text("Invoice Date:", 50, customerInformationTop + 15)
|
46 |
+
.text(formatDate(new Date()), 150, customerInformationTop + 15)
|
47 |
+
.text("Balance Due:", 50, customerInformationTop + 30)
|
48 |
+
.text(
|
49 |
+
formatCurrency(invoice.subtotal - invoice.paid),
|
50 |
+
150,
|
51 |
+
customerInformationTop + 30
|
52 |
+
)
|
53 |
+
|
54 |
+
.font("Helvetica-Bold")
|
55 |
+
.text(invoice.shipping.name, 300, customerInformationTop)
|
56 |
+
.font("Helvetica")
|
57 |
+
.text(invoice.shipping.address, 300, customerInformationTop + 15)
|
58 |
+
.text(
|
59 |
+
invoice.shipping.city +
|
60 |
+
", " +
|
61 |
+
invoice.shipping.state +
|
62 |
+
", " +
|
63 |
+
invoice.shipping.country,
|
64 |
+
300,
|
65 |
+
customerInformationTop + 30
|
66 |
+
)
|
67 |
+
.moveDown();
|
68 |
+
|
69 |
+
generateHr(doc, 252);
|
70 |
+
}
|
71 |
+
|
72 |
+
function generateInvoiceTable(doc, invoice) {
|
73 |
+
let i;
|
74 |
+
const invoiceTableTop = 330;
|
75 |
+
|
76 |
+
doc.font("Helvetica-Bold");
|
77 |
+
generateTableRow(
|
78 |
+
doc,
|
79 |
+
invoiceTableTop,
|
80 |
+
"Item",
|
81 |
+
"Description",
|
82 |
+
"Unit Cost",
|
83 |
+
"Quantity",
|
84 |
+
"Line Total"
|
85 |
+
);
|
86 |
+
generateHr(doc, invoiceTableTop + 20);
|
87 |
+
doc.font("Helvetica");
|
88 |
+
|
89 |
+
for (i = 0; i < invoice.items.length; i++) {
|
90 |
+
const item = invoice.items[i];
|
91 |
+
const position = invoiceTableTop + (i + 1) * 30;
|
92 |
+
generateTableRow(
|
93 |
+
doc,
|
94 |
+
position,
|
95 |
+
item.item,
|
96 |
+
item.description,
|
97 |
+
formatCurrency(item.amount / item.quantity),
|
98 |
+
item.quantity,
|
99 |
+
formatCurrency(item.amount)
|
100 |
+
);
|
101 |
+
|
102 |
+
generateHr(doc, position + 20);
|
103 |
+
}
|
104 |
+
|
105 |
+
const subtotalPosition = invoiceTableTop + (i + 1) * 30;
|
106 |
+
generateTableRow(
|
107 |
+
doc,
|
108 |
+
subtotalPosition,
|
109 |
+
"",
|
110 |
+
"",
|
111 |
+
"Subtotal",
|
112 |
+
"",
|
113 |
+
formatCurrency(invoice.subtotal)
|
114 |
+
);
|
115 |
+
|
116 |
+
const paidToDatePosition = subtotalPosition + 20;
|
117 |
+
generateTableRow(
|
118 |
+
doc,
|
119 |
+
paidToDatePosition,
|
120 |
+
"",
|
121 |
+
"",
|
122 |
+
"Paid To Date",
|
123 |
+
"",
|
124 |
+
formatCurrency(invoice.paid)
|
125 |
+
);
|
126 |
+
|
127 |
+
const duePosition = paidToDatePosition + 25;
|
128 |
+
doc.font("Helvetica-Bold");
|
129 |
+
generateTableRow(
|
130 |
+
doc,
|
131 |
+
duePosition,
|
132 |
+
"",
|
133 |
+
"",
|
134 |
+
"Balance Due",
|
135 |
+
"",
|
136 |
+
formatCurrency(invoice.subtotal - invoice.paid)
|
137 |
+
);
|
138 |
+
doc.font("Helvetica");
|
139 |
+
}
|
140 |
+
|
141 |
+
function generateFooter(doc) {
|
142 |
+
doc
|
143 |
+
.fontSize(10)
|
144 |
+
.text(
|
145 |
+
"Payment is due within 15 days. Thank you for your business.",
|
146 |
+
50,
|
147 |
+
780,
|
148 |
+
{ align: "center", width: 500 }
|
149 |
+
);
|
150 |
+
}
|
151 |
+
|
152 |
+
function generateTableRow(
|
153 |
+
doc,
|
154 |
+
y,
|
155 |
+
item,
|
156 |
+
description,
|
157 |
+
unitCost,
|
158 |
+
quantity,
|
159 |
+
lineTotal
|
160 |
+
) {
|
161 |
+
doc
|
162 |
+
.fontSize(10)
|
163 |
+
.text(item, 50, y)
|
164 |
+
.text(description, 150, y)
|
165 |
+
.text(unitCost, 280, y, { width: 90, align: "right" })
|
166 |
+
.text(quantity, 370, y, { width: 90, align: "right" })
|
167 |
+
.text(lineTotal, 0, y, { align: "right" });
|
168 |
+
}
|
169 |
+
|
170 |
+
function generateHr(doc, y) {
|
171 |
+
doc
|
172 |
+
.strokeColor("#aaaaaa")
|
173 |
+
.lineWidth(1)
|
174 |
+
.moveTo(50, y)
|
175 |
+
.lineTo(550, y)
|
176 |
+
.stroke();
|
177 |
+
}
|
178 |
+
|
179 |
+
function formatCurrency(cents) {
|
180 |
+
return "$" + (cents / 100).toFixed(2);
|
181 |
+
}
|
182 |
+
|
183 |
+
function formatDate(date) {
|
184 |
+
const day = date.getDate();
|
185 |
+
const month = date.getMonth() + 1;
|
186 |
+
const year = date.getFullYear();
|
187 |
+
|
188 |
+
return year + "/" + month + "/" + day;
|
189 |
+
}
|
190 |
+
|
191 |
+
module.exports = {
|
192 |
+
createInvoice
|
193 |
+
};
|
pdfkit-invoice/index.js
ADDED
@@ -0,0 +1,31 @@
|
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|
|
|
|
1 |
+
const { createInvoice } = require("./createInvoice.js");
|
2 |
+
|
3 |
+
const invoice = {
|
4 |
+
shipping: {
|
5 |
+
name: "John Doe",
|
6 |
+
address: "1234 Main Street",
|
7 |
+
city: "San Francisco",
|
8 |
+
state: "CA",
|
9 |
+
country: "US",
|
10 |
+
postal_code: 94111
|
11 |
+
},
|
12 |
+
items: [
|
13 |
+
{
|
14 |
+
item: "TC 100",
|
15 |
+
description: "Toner Cartridge",
|
16 |
+
quantity: 2,
|
17 |
+
amount: 6000
|
18 |
+
},
|
19 |
+
{
|
20 |
+
item: "USB_EXT",
|
21 |
+
description: "USB Cable Extender",
|
22 |
+
quantity: 1,
|
23 |
+
amount: 2000
|
24 |
+
}
|
25 |
+
],
|
26 |
+
subtotal: 8000,
|
27 |
+
paid: 0,
|
28 |
+
invoice_nr: 1234
|
29 |
+
};
|
30 |
+
|
31 |
+
createInvoice(invoice, "invoice.pdf");
|
pdfkit-invoice/logo.png
ADDED
![]() |
pdfkit-invoice/package-lock.json
ADDED
@@ -0,0 +1,1395 @@
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1355 |
+
"tiny-inflate": "^1.0.0"
|
1356 |
+
}
|
1357 |
+
},
|
1358 |
+
"which-boxed-primitive": {
|
1359 |
+
"version": "1.0.2",
|
1360 |
+
"resolved": "https://registry.npmjs.org/which-boxed-primitive/-/which-boxed-primitive-1.0.2.tgz",
|
1361 |
+
"integrity": "sha512-bwZdv0AKLpplFY2KZRX6TvyuN7ojjr7lwkg6ml0roIy9YeuSr7JS372qlNW18UQYzgYK9ziGcerWqZOmEn9VNg==",
|
1362 |
+
"requires": {
|
1363 |
+
"is-bigint": "^1.0.1",
|
1364 |
+
"is-boolean-object": "^1.1.0",
|
1365 |
+
"is-number-object": "^1.0.4",
|
1366 |
+
"is-string": "^1.0.5",
|
1367 |
+
"is-symbol": "^1.0.3"
|
1368 |
+
}
|
1369 |
+
},
|
1370 |
+
"which-collection": {
|
1371 |
+
"version": "1.0.1",
|
1372 |
+
"resolved": "https://registry.npmjs.org/which-collection/-/which-collection-1.0.1.tgz",
|
1373 |
+
"integrity": "sha512-W8xeTUwaln8i3K/cY1nGXzdnVZlidBcagyNFtBdD5kxnb4TvGKR7FfSIS3mYpwWS1QUCutfKz8IY8RjftB0+1A==",
|
1374 |
+
"requires": {
|
1375 |
+
"is-map": "^2.0.1",
|
1376 |
+
"is-set": "^2.0.1",
|
1377 |
+
"is-weakmap": "^2.0.1",
|
1378 |
+
"is-weakset": "^2.0.1"
|
1379 |
+
}
|
1380 |
+
},
|
1381 |
+
"which-typed-array": {
|
1382 |
+
"version": "1.1.8",
|
1383 |
+
"resolved": "https://registry.npmjs.org/which-typed-array/-/which-typed-array-1.1.8.tgz",
|
1384 |
+
"integrity": "sha512-Jn4e5PItbcAHyLoRDwvPj1ypu27DJbtdYXUa5zsinrUx77Uvfb0cXwwnGMTn7cjUfhhqgVQnVJCwF+7cgU7tpw==",
|
1385 |
+
"requires": {
|
1386 |
+
"available-typed-arrays": "^1.0.5",
|
1387 |
+
"call-bind": "^1.0.2",
|
1388 |
+
"es-abstract": "^1.20.0",
|
1389 |
+
"for-each": "^0.3.3",
|
1390 |
+
"has-tostringtag": "^1.0.0",
|
1391 |
+
"is-typed-array": "^1.1.9"
|
1392 |
+
}
|
1393 |
+
}
|
1394 |
+
}
|
1395 |
+
}
|
pdfkit-invoice/package.json
ADDED
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"name": "pdfkit-invoice",
|
3 |
+
"version": "1.0.0",
|
4 |
+
"description": "",
|
5 |
+
"main": "index.js",
|
6 |
+
"scripts": {
|
7 |
+
"start": "node index.js",
|
8 |
+
"test": "echo \"Error: no test specified\" && exit 1"
|
9 |
+
},
|
10 |
+
"keywords": [],
|
11 |
+
"author": "",
|
12 |
+
"license": "ISC",
|
13 |
+
"dependencies": {
|
14 |
+
"pdfkit": "^0.13.0"
|
15 |
+
}
|
16 |
+
}
|
pdfkit-invoice/screenshot.png
ADDED
![]() |
pillmatePPTXBackground.webp
ADDED
![]() |
real_promises_only.py
ADDED
@@ -0,0 +1,114 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
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|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# promises detector fine tuning for llama2-7B
|
2 |
+
from datasets import load_dataset
|
3 |
+
from accelerate import find_executable_batch_size # 8 is the recommended from the book
|
4 |
+
from transformers import TrainingArguments, BitsAndBytesConfig
|
5 |
+
from transformers import LlamaForCausalLM, LlamaTokenizer
|
6 |
+
from peft import PeftModel, LoraConfig
|
7 |
+
from trl import SFTTrainer
|
8 |
+
|
9 |
+
|
10 |
+
tune_data = load_dataset("csv", data_files='Political_Promises_Fine_Tuning.csv')
|
11 |
+
|
12 |
+
optim = "paged_adamw_32bit"
|
13 |
+
learning_rate = 3e-4
|
14 |
+
weight_decay = .01
|
15 |
+
lr_scheduler_type = 'cosine'
|
16 |
+
warmup_ratio = .03 # The proportion of training steps
|
17 |
+
|
18 |
+
# on a 16GB GPU the params are:
|
19 |
+
gradient_accumulation_steps = 4
|
20 |
+
bf16 = True
|
21 |
+
gradient_checkpointing = True
|
22 |
+
|
23 |
+
# Label smoothing
|
24 |
+
# Label 0 will be transformed to label_smoothing_factor/num_labels
|
25 |
+
# Label 1 will be transformed to 1 + label_smoothing_factor(-1 + 1/ num_labels)
|
26 |
+
|
27 |
+
label_smoothing_factor = .1
|
28 |
+
# neftune stands for Noise Embedding Instruction Fine-Tuning (noise ~U(-1,1))
|
29 |
+
neftune_noise_alpha = 5
|
30 |
+
|
31 |
+
# batch-size (the bigger, the faster and more memory heavy and prone to local minima/overfitting)
|
32 |
+
# Tip: Reduce max_seq_len to support larger batch_size
|
33 |
+
per_device_train_batch_size = find_executable_batch_size()
|
34 |
+
per_device_eval_batch_size = find_executable_batch_size()
|
35 |
+
|
36 |
+
max_grad_norm = 2
|
37 |
+
group_by_length = True
|
38 |
+
max_train_epochs = 3
|
39 |
+
|
40 |
+
# peft = parameter efficient fine tuning
|
41 |
+
# LORA:
|
42 |
+
r = 64
|
43 |
+
lora_alpha = 8
|
44 |
+
lora_dropout = 0.1
|
45 |
+
|
46 |
+
# Working with reduced precision with bitsandbytes
|
47 |
+
use_4bit = True
|
48 |
+
bnb_4bit_compute_dtype = 'float16'
|
49 |
+
bnb_4bit_quant_type = 'nf4'
|
50 |
+
use_nested_quant = False
|
51 |
+
|
52 |
+
# trl = Transformer Reinforcement Learning, for supervised fine-tuning
|
53 |
+
max_seq_length = 128
|
54 |
+
# Packing is used to place multiple instructions in the same input sequence
|
55 |
+
|
56 |
+
packing = True
|
57 |
+
|
58 |
+
train_params = TrainingArguments(
|
59 |
+
optim=optim,
|
60 |
+
learning_rate=learning_rate,
|
61 |
+
weight_decay=weight_decay,
|
62 |
+
warmup_ratio=warmup_ratio,
|
63 |
+
gradient_accumulation_steps=gradient_accumulation_steps,
|
64 |
+
bf16=bf16,
|
65 |
+
gradient_checkpointing=gradient_checkpointing,
|
66 |
+
label_smoothing_factor=label_smoothing_factor,
|
67 |
+
neftune_noise_alpha=neftune_noise_alpha,
|
68 |
+
per_device_train_batch_size=per_device_train_batch_size,
|
69 |
+
per_device_eval_batch_size=per_device_eval_batch_size,
|
70 |
+
max_grad_norm=max_grad_norm,
|
71 |
+
group_by_length=group_by_length,
|
72 |
+
num_train_epochs=max_train_epochs,
|
73 |
+
output_dir='./model_outputs',
|
74 |
+
save_steps=50,
|
75 |
+
logging_steps=10
|
76 |
+
)
|
77 |
+
|
78 |
+
quantize_params = BitsAndBytesConfig(
|
79 |
+
use_4bit=use_4bit,
|
80 |
+
bnb_4bit_compute_dtype=bnb_4bit_compute_dtype,
|
81 |
+
bnb_4bit_quant_type=bnb_4bit_quant_type,
|
82 |
+
use_nested_quant=use_nested_quant,
|
83 |
+
)
|
84 |
+
|
85 |
+
|
86 |
+
lora_params = LoraConfig (
|
87 |
+
r = 64,
|
88 |
+
lora_alpha = 8,
|
89 |
+
lora_dropout = 0.1
|
90 |
+
)
|
91 |
+
|
92 |
+
model = LlamaForCausalLM.from_pretrained(
|
93 |
+
pretrained_model_name_or_path = 'meta-llama/Llama-2-7b',
|
94 |
+
quantization_config=quantize_params,
|
95 |
+
device_map='auto'
|
96 |
+
)
|
97 |
+
|
98 |
+
tokenizer = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-7b')
|
99 |
+
|
100 |
+
tune_data = load_dataset("csv", data_files='/path/to/finetune_data.csv')
|
101 |
+
|
102 |
+
sft = SFTTrainer (
|
103 |
+
model = model,
|
104 |
+
args = train_params,
|
105 |
+
train_dataset = tune_data,
|
106 |
+
tokenizer = tokenizer
|
107 |
+
peft_config = lora_params,
|
108 |
+
max_seq_length = 128,
|
109 |
+
dataset_text_field = 'text',
|
110 |
+
packing = True
|
111 |
+
)
|
112 |
+
|
113 |
+
sft.train()
|
114 |
+
sft.model.save_pretrained('/path/to/llama-2-it.csv')
|
report.md
ADDED
File without changes
|
utils.py
ADDED
@@ -0,0 +1,79 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from langchain_openai import OpenAI
|
2 |
+
from pypdf import PdfReader
|
3 |
+
import pandas as pd
|
4 |
+
import re
|
5 |
+
import replicate
|
6 |
+
from langchain.prompts import PromptTemplate
|
7 |
+
|
8 |
+
|
9 |
+
def get_pdf_text(pdf_doc):
|
10 |
+
text = ''
|
11 |
+
pdf_reader = PdfReader(pdf_doc)
|
12 |
+
for page in pdf_reader.pages:
|
13 |
+
text += page.extract_text()
|
14 |
+
return text
|
15 |
+
|
16 |
+
|
17 |
+
def extracted_data(pages_data):
|
18 |
+
template = """Extract all the following values : invoice no. Description, Quantinty, data, Unit price, Amount, Total
|
19 |
+
email, phone number and address from this data: {pages}
|
20 |
+
|
21 |
+
Expected output: remove any dollar symbols {{'Invoice no.': '1001329', 'Description': 'Office Chair', 'Quantity': '2', 'Date': '5/4/2018', 'Unit price': '100', 'Amount': '20', 'Total': '12020' and so on}}"""
|
22 |
+
|
23 |
+
prompt_template = PromptTemplate(input_variables=["pages"], template=template)
|
24 |
+
|
25 |
+
llm = OpenAI(temperature=.7)
|
26 |
+
full_response = llm(prompt_template.format(pages=pages_data))
|
27 |
+
|
28 |
+
# input_prompt = {
|
29 |
+
# "top_p": .9,
|
30 |
+
# "prompt": prompt_template.format(pages=pages_data),
|
31 |
+
# "temperature": .1,
|
32 |
+
# "max_new_tokens": 500
|
33 |
+
# }
|
34 |
+
# full_response = ''
|
35 |
+
# for event in replicate.stream("meta/llama-2-70b-chat", input=input_prompt):
|
36 |
+
# full_response += event
|
37 |
+
|
38 |
+
return full_response
|
39 |
+
|
40 |
+
|
41 |
+
def create_docs(user_pdf_list):
|
42 |
+
"""Iterate over files in that user uploaded PDF files, one by one"""
|
43 |
+
print("Started creating docs")
|
44 |
+
df = pd.DataFrame({"Invoice no.": pd.Series(dtype='str'),
|
45 |
+
"Description": pd.Series(dtype='str'),
|
46 |
+
"Quantinty": pd.Series(dtype='str'),
|
47 |
+
"Date": pd.Series(dtype='str'),
|
48 |
+
"Unit price": pd.Series(dtype='str'),
|
49 |
+
"Amount": pd.Series(dtype='int'),
|
50 |
+
"Total": pd.Series(dtype='str'),
|
51 |
+
"Email": pd.Series(dtype='str'),
|
52 |
+
"Phone number": pd.Series(dtype='str'),
|
53 |
+
"Address": pd.Series(dtype='str')})
|
54 |
+
|
55 |
+
for filename in user_pdf_list:
|
56 |
+
print('filename is {}'.format(filename))
|
57 |
+
raw_data = get_pdf_text(filename)
|
58 |
+
print('raw_data is {}'.format(raw_data))
|
59 |
+
|
60 |
+
llm_extracted_data = extracted_data(raw_data)
|
61 |
+
print('llm_extracted_data is {}'.format(llm_extracted_data))
|
62 |
+
|
63 |
+
# Capturing just the required data and excluding andy unwanted test from the LLM response
|
64 |
+
pattern = r'{(.+)}'
|
65 |
+
match = re.search(pattern, llm_extracted_data, re.DOTALL)
|
66 |
+
|
67 |
+
if match:
|
68 |
+
extracted_text = match.group(1)
|
69 |
+
# Converting the extracted text to a dictionary
|
70 |
+
data_dict = eval('{' + extracted_text + '}')
|
71 |
+
print('data_dict is {}'.format(data_dict))
|
72 |
+
else:
|
73 |
+
print("No match found.")
|
74 |
+
new_row = pd.DataFrame([data_dict]) # Creating a DataFrame from the dictionary
|
75 |
+
df = pd.concat([df, new_row], ignore_index=True) # Concatenating the new row
|
76 |
+
print('*' * 10 + 'DONE' + '*' * 10)
|
77 |
+
|
78 |
+
df.head()
|
79 |
+
return df
|