How I Built an AI Document Analyzer with Python and Streamlit

Table of Contents

• Introduction
• Extracting Key Data from Invoices
• Categorizing Documents by Type
• Testing the AI Document Analyzer
• Conclusion and Next Steps

Introduction to the AI Document Analyzer Project

The AI Document Analyzer is an exciting new project that leverages artificial intelligence to automatically categorize documents and extract key data from them. This advanced technology can help streamline business processes and unlock valuable insights from documents.

In this post, we'll provide an overview of the AI Document Analyzer, including the technologies used to build it. We'll also demonstrate how it can be applied to real-world use cases like extracting details from invoices and identifying different document types.

Overview of the AI Document Analyzer Project

The AI Document Analyzer is a web application prototype built by Fabio, an experienced project manager. It allows users to upload a PDF document, which is then processed by AI models to categorize the document type and extract relevant data. For example, an invoice uploaded to the app would be identified as an invoice. The AI would further analyze the invoice to pull out key details like the invoice number, date, seller, and total amount due.

Technologies Used to Build the AI Document Analyzer

The AI Document Analyzer uses several key technologies: Streamlit - This Python framework was used to create the web interface. LangChain - An AI system that can understand and generate natural language. Python - The core language used to develop the backend logic and integrate the AI models. Anthropic's open-source Mistal 7B model - A large language model trained by Anthropic to understand and generate text.

Extracting Key Data from Invoices

One of the most useful applications of the AI Document Analyzer is extracting key details from invoices. The tool makes this process fast and efficient.

We'll walk through the steps of uploading an invoice, previewing it, and using AI to identify and pull out the most important invoice information.

Uploading and Previewing the Invoice

The first step is simply uploading a PDF invoice to the tool. Once uploaded, the app generates a preview of the invoice so the user can verify it is the correct document. The AI does not need an perfectly scanned or formatted invoice to work - it is robust enough to handle variances in layouts and image quality.

Using AI to Identify and Extract Invoice Details

After previewing the invoice, the user can trigger the AI to process the document. The Mistal model analyzes the text and layout to identify it as an invoice. Next, the AI locates key data fields like the invoice number, date, seller, and total amount due. It extracts and structures this information so it can be easily reviewed and exported.

Categorizing Documents by Type

Identifying the correct document type or category is another key capability of the AI Document Analyzer.

We'll go through examples of uploading different document types, and show how the tool's AI accurately assigns categories to each one.

Uploading Different Document Types

To demonstrate categorization, we'll upload a few different document types as examples: Insurance policy - A document outlining a person's car insurance policy. Invoice - A billing document from a vendor. Covid-19 test result - A medical report showing the results of a Covid-19 test.

Leveraging AI to Accurately Categorize Documents

Once these sample documents are uploaded, we can trigger the AI Document Analyzer's categorization capability. The Mistal model reads and evaluates the content of each document. Despite differences in formatting and length, it accurately identifies the insurance policy, invoice, and Covid test result.

Testing the AI Document Analyzer

Before deploying the AI Document Analyzer, it is critical to test it on additional documents to validate accuracy and reliability.

We can analyze categorization and data extraction results across many diverse documents to catch any errors and incrementally improve the AI's capabilities.

Validating Categorization and Data Extraction

The first step in testing is running the tool on a wide range of documents beyond our initial examples. These might include contracts, financial reports, medical forms, tax documents and more. Each output is reviewed to ensure categories are correctly identified, and key fields accurately extracted for documents like invoices.

Analyzing Additional Documents to Confirm Accuracy

If any errors in categorization or data extraction are found, the cause is determined. If it is a dataset issue, more training examples can be added. The AI models are retrained and tested again. This cycle repeats until the desired level of accuracy is reached across document types.

Conclusion and Next Steps

In summary, the AI Document Analyzer shows immense promise to save time and unlock value from business documents. Additional development could make the tool even more powerful.

Summary of Project and Key Takeaways

This project demonstrated how AI can automatically categorize documents and extract useful data from them. Key takeaways include the ability to identify document types based on content, and pull out details like invoice numbers and medical test results.

Potential Improvements and Future Development

While accurate, there are several ways the AI Document Analyzer could be improved. More training data would increase accuracy, especially for niche document types. Support for additional file types beyond PDF would make the tool more versatile. Integrating automated data export to databases or other systems would maximize value.

FAQ

Q: What is an AI document analyzer?
A: An AI document analyzer is a system that uses artificial intelligence to read, understand, categorize, and extract data from documents like PDFs.

Q: How was the AI document analyzer built?
A: The AI document analyzer prototype was built using Python, Streamlit, and the Mistal 7B large language model.

Q: What types of documents can it categorize?
A: It can categorize documents like invoices, insurance policies, contracts, reports, and more.

Q: What invoice data can it extract?
A: It can extract key invoice details like invoice number, date, seller, and total amount.

Q: How accurate is the AI document analyzer?
A: Testing showed it was highly accurate in categorizing and extracting data from documents.

Q: Can I get access to the code for this project?
A: Yes, the creator offered to share the Python code with anyone interested in the comments.

Q: What improvements could be made?
A: Possible improvements include supporting more document types, extracting more fields, and improving accuracy.

Q: Can this help automate business processes?
A: Yes, AI document analyzers like this could significantly automate document processing in many industries.

Q: Is this better than manual data entry?
A: Absolutely, an AI system like this reduces manual work and human errors involved in entering data.

Q: What skills are required to build something like this?
A: You need skills in Python, natural language processing, and frameworks like Streamlit to build an AI document analyzer.