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With the rise of AI and machine learning, image processing and Optical Character Recognition (OCR) have become incredibly popular. Tasks that once required hours of manual work—sorting, assessing, and storing information from countless images and documents—can now be completed in minutes.
Usually, these apps are built with programming languages like Python, often taking weeks to develop. However, with the advent of no-code tools, you can achieve the same results in less than a day.
In this blog post, we’ll guide you through building a web app that allows users to upload receipts and interact with them using natural language, powered by GPT. We’ll use v0 by Vercel for creating the user interface and BuildShip, a low-code visual backend builder, to develop the backend.
Before we start, you can clone the templates to follow along:
Demo Walkthrough
1. Upload receipts: The app allows users to upload single or multiple receipts, which are then stored in the backend.
2. Chat with receipts: Users can search for specific information within the uploaded receipts using natural language. For example, asking "Is there any mention of hardwood anywhere?" will prompt the app to search through the receipts and provide a relevant response.
3. GPT-powered responses: The app uses GPT Vision to perform Q&A on the uploaded receipts, enabling it to understand and respond to user queries accurately.
4. Receipt management: The app also includes basic CRUD (Create, Read, Update, Delete) functionality, allowing users to view, delete, and upload receipts as needed.
Building the User Interface with v0
1. Generating the UI: v0, a service offered by Vercel, was used to generate the user interface using AI. By providing a prompt describing the desired layout and features, v0 generated a fully functional UI component.
2. Customizing the generated UI: VZ allows for editing and refining the generated UI to better suit the app's requirements. In this case, the generated UI was modified to include a more prominent "Upload More Receipts" button and a chat list above the message input.
3. Integrating the UI with Next.js: The generated UI component can be easily integrated into a Next.js project using the provided command. Next.js, a frontend framework built by Vercel, seamlessly integrates with v0.
Implementing Functionality with Claude AI Assistance
1. Upload functionality: To implement the "Upload More Receipts" button functionality, the author used AI assistance from Claude. By providing the desired functionality and relevant code snippets, Calude generated the necessary code to handle single image uploads and post multipart form data to the appropriate endpoint.
2. Fetching and deleting receipts: Similar to the upload functionality, AI assistance was used to generate code for fetching saved receipts from the API when the page is loaded and deleting receipts when required.
3. Q&A functionality: The core functionality of the app, which involves posting a question to a BuildShip workflow to perform Q&A on the receipts using GPT, was also implemented with the help of AI-generated code.
Building the Backend with BuildShip
1. Workflow for uploading receipts: The BuildShip workflow for uploading receipts handles converting HEIC files to JPEG (if necessary), uploading the file to BuildShip storage, and saving the receipt details in the BuildShip database.
2. Workflow for listing receipts: The list receipts workflow retrieves all saved receipts from the database when the page is loaded for the first time.
3. Workflow for deleting receipts: The delete receipt workflow removes a specific receipt from the BuildShip database based on the provided receipt ID.
4. Workflow for chatting with receipts: The chat workflow retrieves all saved receipts, passes their URLs to GPT Vision for Q&A, and returns the response to the user's question.
Conclusion
By combining the power of AI-driven tools like v0 for UI generation and BuildShip for backend development, it's possible to quickly create a functional web app that allows users to upload receipts and chat with them using natural language. This receipt chatbot demonstrates the potential of AI in easing the development process and providing valuable functionality to users. With the ability to ask questions and receive accurate responses based on the content of the uploaded receipts, this app can greatly simplify the task of sorting through and finding specific information within a large collection of receipts.
With the rise of AI and machine learning, image processing and Optical Character Recognition (OCR) have become incredibly popular. Tasks that once required hours of manual work—sorting, assessing, and storing information from countless images and documents—can now be completed in minutes.
Usually, these apps are built with programming languages like Python, often taking weeks to develop. However, with the advent of no-code tools, you can achieve the same results in less than a day.
In this blog post, we’ll guide you through building a web app that allows users to upload receipts and interact with them using natural language, powered by GPT. We’ll use v0 by Vercel for creating the user interface and BuildShip, a low-code visual backend builder, to develop the backend.
Before we start, you can clone the templates to follow along:
Demo Walkthrough
1. Upload receipts: The app allows users to upload single or multiple receipts, which are then stored in the backend.
2. Chat with receipts: Users can search for specific information within the uploaded receipts using natural language. For example, asking "Is there any mention of hardwood anywhere?" will prompt the app to search through the receipts and provide a relevant response.
3. GPT-powered responses: The app uses GPT Vision to perform Q&A on the uploaded receipts, enabling it to understand and respond to user queries accurately.
4. Receipt management: The app also includes basic CRUD (Create, Read, Update, Delete) functionality, allowing users to view, delete, and upload receipts as needed.
Building the User Interface with v0
1. Generating the UI: v0, a service offered by Vercel, was used to generate the user interface using AI. By providing a prompt describing the desired layout and features, v0 generated a fully functional UI component.
2. Customizing the generated UI: VZ allows for editing and refining the generated UI to better suit the app's requirements. In this case, the generated UI was modified to include a more prominent "Upload More Receipts" button and a chat list above the message input.
3. Integrating the UI with Next.js: The generated UI component can be easily integrated into a Next.js project using the provided command. Next.js, a frontend framework built by Vercel, seamlessly integrates with v0.
Implementing Functionality with Claude AI Assistance
1. Upload functionality: To implement the "Upload More Receipts" button functionality, the author used AI assistance from Claude. By providing the desired functionality and relevant code snippets, Calude generated the necessary code to handle single image uploads and post multipart form data to the appropriate endpoint.
2. Fetching and deleting receipts: Similar to the upload functionality, AI assistance was used to generate code for fetching saved receipts from the API when the page is loaded and deleting receipts when required.
3. Q&A functionality: The core functionality of the app, which involves posting a question to a BuildShip workflow to perform Q&A on the receipts using GPT, was also implemented with the help of AI-generated code.
Building the Backend with BuildShip
1. Workflow for uploading receipts: The BuildShip workflow for uploading receipts handles converting HEIC files to JPEG (if necessary), uploading the file to BuildShip storage, and saving the receipt details in the BuildShip database.
2. Workflow for listing receipts: The list receipts workflow retrieves all saved receipts from the database when the page is loaded for the first time.
3. Workflow for deleting receipts: The delete receipt workflow removes a specific receipt from the BuildShip database based on the provided receipt ID.
4. Workflow for chatting with receipts: The chat workflow retrieves all saved receipts, passes their URLs to GPT Vision for Q&A, and returns the response to the user's question.
Conclusion
By combining the power of AI-driven tools like v0 for UI generation and BuildShip for backend development, it's possible to quickly create a functional web app that allows users to upload receipts and chat with them using natural language. This receipt chatbot demonstrates the potential of AI in easing the development process and providing valuable functionality to users. With the ability to ask questions and receive accurate responses based on the content of the uploaded receipts, this app can greatly simplify the task of sorting through and finding specific information within a large collection of receipts.
With the rise of AI and machine learning, image processing and Optical Character Recognition (OCR) have become incredibly popular. Tasks that once required hours of manual work—sorting, assessing, and storing information from countless images and documents—can now be completed in minutes.
Usually, these apps are built with programming languages like Python, often taking weeks to develop. However, with the advent of no-code tools, you can achieve the same results in less than a day.
In this blog post, we’ll guide you through building a web app that allows users to upload receipts and interact with them using natural language, powered by GPT. We’ll use v0 by Vercel for creating the user interface and BuildShip, a low-code visual backend builder, to develop the backend.
Before we start, you can clone the templates to follow along:
Demo Walkthrough
1. Upload receipts: The app allows users to upload single or multiple receipts, which are then stored in the backend.
2. Chat with receipts: Users can search for specific information within the uploaded receipts using natural language. For example, asking "Is there any mention of hardwood anywhere?" will prompt the app to search through the receipts and provide a relevant response.
3. GPT-powered responses: The app uses GPT Vision to perform Q&A on the uploaded receipts, enabling it to understand and respond to user queries accurately.
4. Receipt management: The app also includes basic CRUD (Create, Read, Update, Delete) functionality, allowing users to view, delete, and upload receipts as needed.
Building the User Interface with v0
1. Generating the UI: v0, a service offered by Vercel, was used to generate the user interface using AI. By providing a prompt describing the desired layout and features, v0 generated a fully functional UI component.
2. Customizing the generated UI: VZ allows for editing and refining the generated UI to better suit the app's requirements. In this case, the generated UI was modified to include a more prominent "Upload More Receipts" button and a chat list above the message input.
3. Integrating the UI with Next.js: The generated UI component can be easily integrated into a Next.js project using the provided command. Next.js, a frontend framework built by Vercel, seamlessly integrates with v0.
Implementing Functionality with Claude AI Assistance
1. Upload functionality: To implement the "Upload More Receipts" button functionality, the author used AI assistance from Claude. By providing the desired functionality and relevant code snippets, Calude generated the necessary code to handle single image uploads and post multipart form data to the appropriate endpoint.
2. Fetching and deleting receipts: Similar to the upload functionality, AI assistance was used to generate code for fetching saved receipts from the API when the page is loaded and deleting receipts when required.
3. Q&A functionality: The core functionality of the app, which involves posting a question to a BuildShip workflow to perform Q&A on the receipts using GPT, was also implemented with the help of AI-generated code.
Building the Backend with BuildShip
1. Workflow for uploading receipts: The BuildShip workflow for uploading receipts handles converting HEIC files to JPEG (if necessary), uploading the file to BuildShip storage, and saving the receipt details in the BuildShip database.
2. Workflow for listing receipts: The list receipts workflow retrieves all saved receipts from the database when the page is loaded for the first time.
3. Workflow for deleting receipts: The delete receipt workflow removes a specific receipt from the BuildShip database based on the provided receipt ID.
4. Workflow for chatting with receipts: The chat workflow retrieves all saved receipts, passes their URLs to GPT Vision for Q&A, and returns the response to the user's question.
Conclusion
By combining the power of AI-driven tools like v0 for UI generation and BuildShip for backend development, it's possible to quickly create a functional web app that allows users to upload receipts and chat with them using natural language. This receipt chatbot demonstrates the potential of AI in easing the development process and providing valuable functionality to users. With the ability to ask questions and receive accurate responses based on the content of the uploaded receipts, this app can greatly simplify the task of sorting through and finding specific information within a large collection of receipts.
