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PDF Analyzer

An intelligent PDF analysis tool that leverages AI to extract, analyze, and provide insights from PDF documents.

Project Structure

pdf-analyzer/
├── api/              # Serverless API endpoints
│   └── health.js     # Health check endpoint
├── public/           # Static files
│   ├── css/          # Stylesheets
│   │   └── styles.css # Main stylesheet
│   └── index.html    # Landing page
├── index.js          # Express server configuration
├── package.json      # Project dependencies
├── vercel.json       # Vercel deployment configuration
└── README.md         # Project documentation

Local Development

  1. Install Dependencies

    npm install

  2. Environment Variables Create a .env file in the root directory with:

    PORT=3004
GEMINI_API_KEY=your_api_key_here
GEMINI_API_ENDPOINT=your_api_endpoint_here

  3. Start Development Server

    npm run dev

    The server will start on http://localhost:3004

  4. View Landing Page

    • Open your browser and navigate to http://localhost:3004
    • You should see the PDF Analyzer landing page
    • The page is responsive and will adapt to different screen sizes
    • When deployed, the landing page will be accessible at https://parsepdf.io

Deployment to Vercel (parsepdf.io)

  1. Install Vercel CLI

    npm install -g vercel

  2. Configure Domain

    • Log into your Vercel account
    • Add parsepdf.io as a custom domain
    • Configure DNS settings as provided by Vercel

  3. Environment Variables Set the following environment variables in your Vercel project settings:

    • GEMINI_API_KEY: Your Google Gemini API key
    • GEMINI_API_ENDPOINT: Your Google Gemini API endpoint

  4. Deploy

    vercel

    For production deployment:

    vercel --prod

Environment Variables

Required Variables

  • PORT: Server port (default: 3004)
  • GEMINI_API_KEY: Your Gemini API key (get it from Google AI Studio)
  • NODE_ENV: Environment setting (development/production)

Local Setup

Create a .env file in the root directory:

# Server Configuration
PORT=3004

# Gemini API Configuration
GEMINI_API_KEY=your-api-key

Vercel Deployment

  1. Go to your project settings in the Vercel dashboard
  2. Navigate to the Environment Variables section
  3. Add the required variables:
    • GEMINI_API_KEY
    • GEMINI_API_ENDPOINT
  4. Deploy your project to apply the changes

Features

File Upload

The application supports PDF file upload through:

  • Drag and drop interface
  • Traditional file picker

When a file is selected:

  1. The file name and size are displayed
  2. Only PDF files are accepted
  3. An "Analyze PDF" button appears

Pricing

  • Base fee: $1.00 per document
  • Per record: $0.10

Testing File Upload

  1. Local Testing

    • Start the development server: npm run dev
    • Open http://localhost:3004 in your browser
    • Try uploading a PDF file by: a. Dragging and dropping a PDF onto the upload zone b. Clicking the upload zone and selecting a PDF
    • Verify that:
      • The file name and size are displayed
      • Non-PDF files are rejected
      • The "Analyze PDF" button appears after selection

  2. Production Testing

    • Visit the deployed site
    • Perform the same upload tests as above
    • Verify the upload interface is responsive on mobile devices

API Endpoints

Health Check

  • GET /api/health: Health check endpoint
    • Returns: { status: 'healthy', timestamp: '...' }

PDF Analysis

  • POST /api/analyze: Upload and analyze a PDF file
    • Content-Type: multipart/form-data
    • Body:
      • pdf: PDF file (required)
    • Size Limit: 10MB
    • Returns: 
      {
  "success": true,
  "message": "File successfully processed",
  "data": {
    "sessionId": "uuid-v4",
    "filename": "example.pdf",
    "size": 1234567,
    "mimeType": "application/pdf",
    "base64Preview": "...",
    "base64": "full-base64-string"
  }
}

Testing the API

Using the Web Interface

  1. Visit the landing page
  2. Upload a PDF file using drag-and-drop or file picker
  3. Click "Analyze PDF"
  4. Check the response in the UI

Using Postman

  1. Create a new POST request to /api/analyze
  2. Set the request type to multipart/form-data
  3. Add a field named pdf and select a PDF file
  4. Send the request
  5. Verify the JSON response includes:
    • Success status
    • Session ID
    • Summary of the PDF content
    • Structured data extracted from the PDF

Using cURL

curl -X POST \
  -F "pdf=@/path/to/your/file.pdf" \
  http://localhost:3004/api/analyze

Error Responses

  • File too large (>10MB): 
    {
  "success": false,
  "message": "File too large"
}
  • Invalid file type: 
    {
  "success": false,
  "message": "Only PDF files are allowed"
}