Upload 29 files

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/invoice_extractor.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="llm_env" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/material_theme_project_new.xml

@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="MaterialThemeProjectNewConfig">
+    <option name="metadata">
+      <MTProjectMetadataState>
+        <option name="migrated" value="true" />
+        <option name="pristineConfig" value="false" />
+        <option name="userId" value="13c809ac:18ec6bfab90:-7ffe" />
+      </MTProjectMetadataState>
+    </option>
+  </component>
+</project>

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="C:\Users\kosti\anaconda3" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="llm_env" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/invoice_extractor.iml" filepath="$PROJECT_DIR$/.idea/invoice_extractor.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/pdfkit-invoice" vcs="Git" />
+  </component>
+</project>

.idea/workspace.xml

@@ -0,0 +1,164 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="AutoImportSettings">
+    <option name="autoReloadType" value="SELECTIVE" />
+  </component>
+  <component name="ChangeListManager">
+    <list default="true" id="5d5b8dc8-e7fe-43d9-8802-74478f493adb" name="Changes" comment="" />
+    <option name="SHOW_DIALOG" value="false" />
+    <option name="HIGHLIGHT_CONFLICTS" value="true" />
+    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+    <option name="LAST_RESOLUTION" value="IGNORE" />
+  </component>
+  <component name="FileTemplateManagerImpl">
+    <option name="RECENT_TEMPLATES">
+      <list>
+        <option value="Python Script" />
+      </list>
+    </option>
+  </component>
+  <component name="Git.Settings">
+    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$/pdfkit-invoice" />
+  </component>
+  <component name="ProjectColorInfo">{
+  &quot;associatedIndex&quot;: 4
+}</component>
+  <component name="ProjectId" id="2f2dqHtOqUJDiIt51G8oHM6rauH" />
+  <component name="ProjectViewState">
+    <option name="hideEmptyMiddlePackages" value="true" />
+    <option name="showLibraryContents" value="true" />
+  </component>
+  <component name="PropertiesComponent">{
+  &quot;keyToString&quot;: {
+    &quot;Python.pdf_reader.executor&quot;: &quot;Run&quot;,
+    &quot;Python.real_promises_only.executor&quot;: &quot;Run&quot;,
+    &quot;RunOnceActivity.ShowReadmeOnStart&quot;: &quot;true&quot;,
+    &quot;git-widget-placeholder&quot;: &quot;master&quot;,
+    &quot;node.js.detected.package.eslint&quot;: &quot;true&quot;,
+    &quot;node.js.detected.package.tslint&quot;: &quot;true&quot;,
+    &quot;node.js.selected.package.eslint&quot;: &quot;(autodetect)&quot;,
+    &quot;node.js.selected.package.tslint&quot;: &quot;(autodetect)&quot;,
+    &quot;nodejs_package_manager_path&quot;: &quot;npm&quot;,
+    &quot;vue.rearranger.settings.migration&quot;: &quot;true&quot;
+  }
+}</component>
+  <component name="RunManager" selected="Python.real_promises_only">
+    <configuration name="app" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true">
+      <module name="invoice_extractor" />
+      <option name="ENV_FILES" value="" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+      </envs>
+      <option name="SDK_HOME" value="" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
+      <option name="IS_MODULE_SDK" value="true" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
+      <option name="SCRIPT_NAME" value="C:\Users\kosti\PycharmProjects\invoice_extractor\app.py" />
+      <option name="PARAMETERS" value="" />
+      <option name="SHOW_COMMAND_LINE" value="false" />
+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
+    </configuration>
+    <configuration name="pdf_reader" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+      <module name="invoice_extractor" />
+      <option name="ENV_FILES" value="" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+      </envs>
+      <option name="SDK_HOME" value="" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
+      <option name="IS_MODULE_SDK" value="true" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/pdf_reader.py" />
+      <option name="PARAMETERS" value="" />
+      <option name="SHOW_COMMAND_LINE" value="false" />
+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
+    </configuration>
+    <configuration name="real_promises_only" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+      <module name="invoice_extractor" />
+      <option name="ENV_FILES" value="" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+      </envs>
+      <option name="SDK_HOME" value="" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
+      <option name="IS_MODULE_SDK" value="true" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/real_promises_only.py" />
+      <option name="PARAMETERS" value="" />
+      <option name="SHOW_COMMAND_LINE" value="false" />
+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
+    </configuration>
+    <recent_temporary>
+      <list>
+        <item itemvalue="Python.real_promises_only" />
+        <item itemvalue="Python.pdf_reader" />
+      </list>
+    </recent_temporary>
+  </component>
+  <component name="SharedIndexes">
+    <attachedChunks>
+      <set>
+        <option value="bundled-js-predefined-1d06a55b98c1-74d2a5396914-JavaScript-PY-241.14494.241" />
+        <option value="bundled-python-sdk-0509580d9d50-28c9f5db9ffe-com.jetbrains.pycharm.pro.sharedIndexes.bundled-PY-241.14494.241" />
+      </set>
+    </attachedChunks>
+  </component>
+  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
+  <component name="TaskManager">
+    <task active="true" id="Default" summary="Default task">
+      <changelist id="5d5b8dc8-e7fe-43d9-8802-74478f493adb" name="Changes" comment="" />
+      <created>1713003295790</created>
+      <option name="number" value="Default" />
+      <option name="presentableId" value="Default" />
+      <updated>1713003295790</updated>
+      <workItem from="1713003297318" duration="7246000" />
+      <workItem from="1713108901124" duration="72000" />
+      <workItem from="1713108981255" duration="5119000" />
+      <workItem from="1713203072228" duration="313000" />
+      <workItem from="1713303909981" duration="380000" />
+    </task>
+    <servers />
+  </component>
+  <component name="TypeScriptGeneratedFilesManager">
+    <option name="version" value="3" />
+  </component>
+  <component name="XDebuggerManager">
+    <breakpoint-manager>
+      <breakpoints>
+        <line-breakpoint enabled="true" suspend="THREAD" type="python-line">
+          <url>file://$PROJECT_DIR$/app.py</url>
+          <line>8</line>
+          <option name="timeStamp" value="1" />
+        </line-breakpoint>
+      </breakpoints>
+    </breakpoint-manager>
+  </component>
+  <component name="com.intellij.coverage.CoverageDataManagerImpl">
+    <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$" />
+    <SUITE FILE_PATH="coverage/invoice_extractor$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="$PROJECT_DIR$" />
+  </component>
+</project>

Extracted_Invoice.csv

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+0
+"
+
+{'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'}"

Extracted_Invoice1.csv

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+0
+"
+
+- 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: $160.00
+- Email: info@gptwidgets.com
+- Phone number: (123) 456-7890
+- Address: 123 AI Lane, Model Town, OpenAI"

Extracted_Invoice_001.csv

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+0
+" {'email': 'info@gptwidgets.com', 'phone number': '(123) 456-7890', 'address': '123 AI Lane, Model Town, OpenAI'}
+
+{'Invoice no.': '001', 'Description': 'Widget A', 'Quantity': '2', 'Date': '2024-04-13', 'Unit price': '30.00', 'Amount': '60.00', 'Total': '160.00'} 
+{'Invoice no.': '001', 'Description': 'Widget B', 'Quantity': '5', 'Date': '2024-04-13', 'Unit price': '20.00', 'Amount': '100.00', 'Total': '160.00'}
+{'email': 'info@gptwidgets.com', 'phone number': '(123) 456-7890', 'address': '123 AI Lane, Model Town, OpenAI'}"

Political_Promises_Fine_Tuning.csv

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+text,label
+"We will build 10,000 kilometres of subway lines in the next ten years.",promise
+We expect the Japanese to increase tariffs next year.,not promise
+We will work towards making Canada a better place.,not promise
+AI will cause the loss of a million jobs next year.,not promise
+The government will provide free college education to all students by 2025.,promise
+Interest rates are expected to rise by next quarter.,not promise
+"We will eliminate the income tax for individuals earning less than $30,000 by next year.",promise
+The economy is predicted to grow by 4% in the coming fiscal year.,not promise
+Our administration will ensure the construction of 500 new schools in rural areas within five years.,promise
+Climate change will likely increase temperatures globally.,not promise

app.py

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+import streamlit as st
+from dotenv import load_dotenv
+
+from utils import *
+
+
+def main():
+    load_dotenv()
+
+    st.set_page_config(page_title="Invoice Extractor")
+    st.title("Invoice Extractor Bot...")
+    st.subheader("I can help you in extracting invoice data")
+
+    # Upload the Invoices (pdf files)
+    pdf = st.file_uploader("Upload invoices here", type=["pdf"], accept_multiple_files=True)
+
+    submit = st.button("Extract Data")
+
+    if submit:
+        with st.spinner("Loading invoices..."):
+            df = create_docs(pdf)
+            # st.write('managed to extract data from pdf')
+            # st.write(df)
+            st.write(df.head())
+
+            # Saving the dataframe as CSV file
+            data_as_csv = df.to_csv(index=False).encode('utf-8')
+            st.download_button(
+                "Download CSV",
+                data_as_csv,
+                "invoice_extracted_data.csv",
+                "text/csv",
+                key="download-tools-csv",
+            )
+
+        st.success("Hope I was able to save your time <3")
+
+
+if __name__ == '__main__':
+    main()

extracted_data.csv

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+

pdf_reader.py

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+# import pandas as pd
+# import json
+#
+#
+# # Function to parse the LLM output and create a DataFrame
+# def process_llm_data(llm_data):
+#     # Assuming llm_data is a string that exactly matches the dictionary format
+#     # Safely convert string representation of dictionary to a dictionary
+#     data_dict = eval(llm_data.strip())
+#
+#     # Convert the dictionary to a DataFrame
+#     # Since our example has comma-separated values in strings for some fields, let's handle that
+#     for key, value in data_dict.items():
+#         if ',' in value:
+#             data_dict[key] = [v.strip() for v in value.split(',')]
+#         else:
+#             data_dict[key] = [value]
+#
+#     df = pd.DataFrame(data_dict)
+#
+#     # Saving the DataFrame to a CSV file
+#     csv_file_path = "Invoice_Data.csv"
+#     df.to_csv(csv_file_path, index=False)
+#
+#     return csv_file_path
+#
+#
+# # Example usage
+# llm_extracted_data = """
+# {
+#     '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': '$160.00',
+#     'Email': 'info@gptwidgets.com',
+#     'Phone number': '(123) 456-7890',
+#     'Address': '123 AI Lane, Model Town, OpenAI'
+# }
+# """
+#
+# # Call the function to process the data and create a CSV
+# csv_path = process_llm_data(llm_extracted_data)
+# print(f"Data saved to CSV at: {csv_path}")
+
+import pandas as pd
+from utils import get_pdf_text, extracted_data
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+def save_data_to_csv(data, filename):
+    # Convert the dictionary to a DataFrame
+    df = pd.DataFrame([data])
+    print(df)
+    # Save the DataFrame to a CSV file
+    csv_path = f"{filename}.csv"
+    df.to_csv(csv_path, index=False)
+    print(f"Data saved to {csv_path}")
+
+
+# Example usage
+print('filename is {}'.format("Invoice_001.pdf"))
+raw_data = get_pdf_text("Invoice_001.pdf")
+print('raw_data is {}'.format(raw_data))
+
+llm_extracted_data = extracted_data(raw_data)
+print('llm_extracted_data is {}'.format(llm_extracted_data))
+
+# Assuming llm_extracted_data is a dictionary containing the extracted fields
+save_data_to_csv(llm_extracted_data, "Extracted_Invoice1")

pdfkit-invoice/.gitignore

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+node_modules
+invoice.pdf

pdfkit-invoice/README.md

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+# PDFKit Invoices
+
+<center>
+  <a href="https://pspdfkit.com/web">
+    <img src="./screenshot.png" alt="Screenshot of an invoice PDF in the PSPDFKit for Web reader." width="1009">
+  </a>
+</center>
+
+## Prerequisites
+
+- [Node.js](http://nodejs.org/) (with npm or Yarn)
+
+## Getting Started
+
+This repository is an example of how to make PDF invoices with PDFKit.
+
+There are two important fields in this this repository:
+
+- [`index.js`](index.js) is the main entry point. It defines the data structure used to create the invoices.
+- [`createInvoice.js`](createInvoice.js) exports a function that can be used to create invoice PDFs.
+
+To get started, use the following commands:
+
+```bash
+git clone https://github.com/PSPDFKit-labs/pdfkit-invoice.git
+
+npm install  # Install dependencies
+
+npm start  # This will create an invoice.pdf file in the root of the project.
+```
+
+To learn more about this project, make sure to read the accompanying [blog post](https://pspdfkit.com/blog/2019/generate-invoices-pdfkit-node)
+
+## Contributing
+
+Please ensure
+[you have signed our CLA](https://pspdfkit.com/guides/web/current/miscellaneous/contributing/) so that we can
+accept your contributions.

pdfkit-invoice/createInvoice.js

@@ -0,0 +1,193 @@
+const fs = require("fs");
+const PDFDocument = require("pdfkit");
+
+function createInvoice(invoice, path) {
+  let doc = new PDFDocument({ size: "A4", margin: 50 });
+
+  generateHeader(doc);
+  generateCustomerInformation(doc, invoice);
+  generateInvoiceTable(doc, invoice);
+  generateFooter(doc);
+
+  doc.end();
+  doc.pipe(fs.createWriteStream(path));
+}
+
+function generateHeader(doc) {
+  doc
+    .image("logo.png", 50, 45, { width: 50 })
+    .fillColor("#444444")
+    .fontSize(20)
+    .text("ACME Inc.", 110, 57)
+    .fontSize(10)
+    .text("ACME Inc.", 200, 50, { align: "right" })
+    .text("123 Main Street", 200, 65, { align: "right" })
+    .text("New York, NY, 10025", 200, 80, { align: "right" })
+    .moveDown();
+}
+
+function generateCustomerInformation(doc, invoice) {
+  doc
+    .fillColor("#444444")
+    .fontSize(20)
+    .text("Invoice", 50, 160);
+
+  generateHr(doc, 185);
+
+  const customerInformationTop = 200;
+
+  doc
+    .fontSize(10)
+    .text("Invoice Number:", 50, customerInformationTop)
+    .font("Helvetica-Bold")
+    .text(invoice.invoice_nr, 150, customerInformationTop)
+    .font("Helvetica")
+    .text("Invoice Date:", 50, customerInformationTop + 15)
+    .text(formatDate(new Date()), 150, customerInformationTop + 15)
+    .text("Balance Due:", 50, customerInformationTop + 30)
+    .text(
+      formatCurrency(invoice.subtotal - invoice.paid),
+      150,
+      customerInformationTop + 30
+    )
+
+    .font("Helvetica-Bold")
+    .text(invoice.shipping.name, 300, customerInformationTop)
+    .font("Helvetica")
+    .text(invoice.shipping.address, 300, customerInformationTop + 15)
+    .text(
+      invoice.shipping.city +
+        ", " +
+        invoice.shipping.state +
+        ", " +
+        invoice.shipping.country,
+      300,
+      customerInformationTop + 30
+    )
+    .moveDown();
+
+  generateHr(doc, 252);
+}
+
+function generateInvoiceTable(doc, invoice) {
+  let i;
+  const invoiceTableTop = 330;
+
+  doc.font("Helvetica-Bold");
+  generateTableRow(
+    doc,
+    invoiceTableTop,
+    "Item",
+    "Description",
+    "Unit Cost",
+    "Quantity",
+    "Line Total"
+  );
+  generateHr(doc, invoiceTableTop + 20);
+  doc.font("Helvetica");
+
+  for (i = 0; i < invoice.items.length; i++) {
+    const item = invoice.items[i];
+    const position = invoiceTableTop + (i + 1) * 30;
+    generateTableRow(
+      doc,
+      position,
+      item.item,
+      item.description,
+      formatCurrency(item.amount / item.quantity),
+      item.quantity,
+      formatCurrency(item.amount)
+    );
+
+    generateHr(doc, position + 20);
+  }
+
+  const subtotalPosition = invoiceTableTop + (i + 1) * 30;
+  generateTableRow(
+    doc,
+    subtotalPosition,
+    "",
+    "",
+    "Subtotal",
+    "",
+    formatCurrency(invoice.subtotal)
+  );
+
+  const paidToDatePosition = subtotalPosition + 20;
+  generateTableRow(
+    doc,
+    paidToDatePosition,
+    "",
+    "",
+    "Paid To Date",
+    "",
+    formatCurrency(invoice.paid)
+  );
+
+  const duePosition = paidToDatePosition + 25;
+  doc.font("Helvetica-Bold");
+  generateTableRow(
+    doc,
+    duePosition,
+    "",
+    "",
+    "Balance Due",
+    "",
+    formatCurrency(invoice.subtotal - invoice.paid)
+  );
+  doc.font("Helvetica");
+}
+
+function generateFooter(doc) {
+  doc
+    .fontSize(10)
+    .text(
+      "Payment is due within 15 days. Thank you for your business.",
+      50,
+      780,
+      { align: "center", width: 500 }
+    );
+}
+
+function generateTableRow(
+  doc,
+  y,
+  item,
+  description,
+  unitCost,
+  quantity,
+  lineTotal
+) {
+  doc
+    .fontSize(10)
+    .text(item, 50, y)
+    .text(description, 150, y)
+    .text(unitCost, 280, y, { width: 90, align: "right" })
+    .text(quantity, 370, y, { width: 90, align: "right" })
+    .text(lineTotal, 0, y, { align: "right" });
+}
+
+function generateHr(doc, y) {
+  doc
+    .strokeColor("#aaaaaa")
+    .lineWidth(1)
+    .moveTo(50, y)
+    .lineTo(550, y)
+    .stroke();
+}
+
+function formatCurrency(cents) {
+  return "$" + (cents / 100).toFixed(2);
+}
+
+function formatDate(date) {
+  const day = date.getDate();
+  const month = date.getMonth() + 1;
+  const year = date.getFullYear();
+
+  return year + "/" + month + "/" + day;
+}
+
+module.exports = {
+  createInvoice
+};

pdfkit-invoice/index.js

@@ -0,0 +1,31 @@
+const { createInvoice } = require("./createInvoice.js");
+
+const invoice = {
+  shipping: {
+    name: "John Doe",
+    address: "1234 Main Street",
+    city: "San Francisco",
+    state: "CA",
+    country: "US",
+    postal_code: 94111
+  },
+  items: [
+    {
+      item: "TC 100",
+      description: "Toner Cartridge",
+      quantity: 2,
+      amount: 6000
+    },
+    {
+      item: "USB_EXT",
+      description: "USB Cable Extender",
+      quantity: 1,
+      amount: 2000
+    }
+  ],
+  subtotal: 8000,
+  paid: 0,
+  invoice_nr: 1234
+};
+
+createInvoice(invoice, "invoice.pdf");

pdfkit-invoice/package-lock.json

@@ -0,0 +1,1395 @@
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+}

pdfkit-invoice/package.json

@@ -0,0 +1,16 @@
+{
+  "name": "pdfkit-invoice",
+  "version": "1.0.0",
+  "description": "",
+  "main": "index.js",
+  "scripts": {
+    "start": "node index.js",
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "keywords": [],
+  "author": "",
+  "license": "ISC",
+  "dependencies": {
+    "pdfkit": "^0.13.0"
+  }
+}

real_promises_only.py

@@ -0,0 +1,114 @@
+# promises detector fine tuning for llama2-7B
+from datasets import load_dataset
+from accelerate import find_executable_batch_size  # 8 is the recommended from the book
+from transformers import TrainingArguments, BitsAndBytesConfig
+from transformers import LlamaForCausalLM, LlamaTokenizer
+from peft import PeftModel, LoraConfig
+from trl import SFTTrainer
+
+
+tune_data = load_dataset("csv", data_files='Political_Promises_Fine_Tuning.csv')
+
+optim = "paged_adamw_32bit"
+learning_rate = 3e-4
+weight_decay = .01
+lr_scheduler_type = 'cosine'
+warmup_ratio = .03  # The proportion of training steps
+
+# on a 16GB GPU the params are:
+gradient_accumulation_steps = 4
+bf16 = True
+gradient_checkpointing = True
+
+# Label smoothing
+# Label 0 will be transformed to label_smoothing_factor/num_labels
+# Label 1 will be transformed to 1 + label_smoothing_factor(-1 + 1/ num_labels)
+
+label_smoothing_factor = .1
+# neftune stands for Noise Embedding Instruction Fine-Tuning (noise ~U(-1,1))
+neftune_noise_alpha = 5
+
+# batch-size (the bigger, the faster and more memory heavy and prone to local minima/overfitting)
+# Tip: Reduce max_seq_len to support larger batch_size
+per_device_train_batch_size = find_executable_batch_size()
+per_device_eval_batch_size = find_executable_batch_size()
+
+max_grad_norm = 2
+group_by_length = True
+max_train_epochs = 3
+
+# peft = parameter efficient fine tuning
+# LORA:
+r = 64
+lora_alpha = 8
+lora_dropout = 0.1
+
+# Working with reduced precision with bitsandbytes
+use_4bit = True
+bnb_4bit_compute_dtype = 'float16'
+bnb_4bit_quant_type = 'nf4'
+use_nested_quant = False
+
+# trl = Transformer Reinforcement Learning, for supervised fine-tuning
+max_seq_length = 128
+# Packing is used to place multiple instructions in the same input sequence
+
+packing = True
+
+train_params = TrainingArguments(
+    optim=optim,
+    learning_rate=learning_rate,
+    weight_decay=weight_decay,
+    warmup_ratio=warmup_ratio,
+    gradient_accumulation_steps=gradient_accumulation_steps,
+    bf16=bf16,
+    gradient_checkpointing=gradient_checkpointing,
+    label_smoothing_factor=label_smoothing_factor,
+    neftune_noise_alpha=neftune_noise_alpha,
+    per_device_train_batch_size=per_device_train_batch_size,
+    per_device_eval_batch_size=per_device_eval_batch_size,
+    max_grad_norm=max_grad_norm,
+    group_by_length=group_by_length,
+    num_train_epochs=max_train_epochs,
+    output_dir='./model_outputs',
+    save_steps=50,
+    logging_steps=10
+)
+
+quantize_params = BitsAndBytesConfig(
+    use_4bit=use_4bit,
+    bnb_4bit_compute_dtype=bnb_4bit_compute_dtype,
+    bnb_4bit_quant_type=bnb_4bit_quant_type,
+    use_nested_quant=use_nested_quant,
+)
+
+
+lora_params = LoraConfig (
+    r = 64,
+    lora_alpha = 8,
+    lora_dropout = 0.1
+    )
+
+model = LlamaForCausalLM.from_pretrained(
+    pretrained_model_name_or_path = 'meta-llama/Llama-2-7b',
+    quantization_config=quantize_params,
+    device_map='auto'
+    )
+
+tokenizer = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-7b')
+
+tune_data = load_dataset("csv", data_files='/path/to/finetune_data.csv')
+
+sft = SFTTrainer (
+    model = model,
+    args = train_params,
+    train_dataset = tune_data,
+    tokenizer = tokenizer
+    peft_config = lora_params,
+    max_seq_length = 128,
+    dataset_text_field = 'text',
+    packing = True
+    )
+
+sft.train()
+sft.model.save_pretrained('/path/to/llama-2-it.csv')

utils.py

@@ -0,0 +1,79 @@
+from langchain_openai import OpenAI
+from pypdf import PdfReader
+import pandas as pd
+import re
+import replicate
+from langchain.prompts import PromptTemplate
+
+
+def get_pdf_text(pdf_doc):
+    text = ''
+    pdf_reader = PdfReader(pdf_doc)
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+
+def extracted_data(pages_data):
+    template = """Extract all the following values : invoice no. Description, Quantinty, data, Unit price, Amount, Total
+    email, phone number and address from this data: {pages}
+    
+    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}}"""
+
+    prompt_template = PromptTemplate(input_variables=["pages"], template=template)
+
+    llm = OpenAI(temperature=.7)
+    full_response = llm(prompt_template.format(pages=pages_data))
+
+    # input_prompt = {
+    #     "top_p": .9,
+    #     "prompt": prompt_template.format(pages=pages_data),
+    #     "temperature": .1,
+    #     "max_new_tokens": 500
+    # }
+    # full_response = ''
+    # for event in replicate.stream("meta/llama-2-70b-chat", input=input_prompt):
+    #     full_response += event
+
+    return full_response
+
+
+def create_docs(user_pdf_list):
+    """Iterate over files in that user uploaded PDF files, one by one"""
+    print("Started creating docs")
+    df = pd.DataFrame({"Invoice no.": pd.Series(dtype='str'),
+                       "Description": pd.Series(dtype='str'),
+                       "Quantinty": pd.Series(dtype='str'),
+                       "Date": pd.Series(dtype='str'),
+                       "Unit price": pd.Series(dtype='str'),
+                       "Amount": pd.Series(dtype='int'),
+                       "Total": pd.Series(dtype='str'),
+                       "Email": pd.Series(dtype='str'),
+                       "Phone number": pd.Series(dtype='str'),
+                       "Address": pd.Series(dtype='str')})
+
+    for filename in user_pdf_list:
+        print('filename is {}'.format(filename))
+        raw_data = get_pdf_text(filename)
+        print('raw_data is {}'.format(raw_data))
+
+        llm_extracted_data = extracted_data(raw_data)
+        print('llm_extracted_data is {}'.format(llm_extracted_data))
+
+        # Capturing just the required data and excluding andy unwanted test from the LLM response
+        pattern = r'{(.+)}'
+        match = re.search(pattern, llm_extracted_data, re.DOTALL)
+
+        if match:
+            extracted_text = match.group(1)
+            # Converting the extracted text to a dictionary
+            data_dict = eval('{' + extracted_text + '}')
+            print('data_dict is {}'.format(data_dict))
+        else:
+            print("No match found.")
+        new_row = pd.DataFrame([data_dict])  # Creating a DataFrame from the dictionary
+        df = pd.concat([df, new_row], ignore_index=True)  # Concatenating the new row
+        print('*' * 10 + 'DONE' + '*' * 10)
+
+    df.head()
+    return df