AI-Assisted-Smart-Data-Entry-PoC

AI-Assisted Smart Data Entry Backend

This project provides a FastAPI-based backend for extracting structured data from medical receipts using OpenAI's GPT-4o model with structured outputs.

Features

• FastAPI: Modern, fast web framework for building APIs.
• Structured Extraction: Uses OpenAI's beta.chat.completions.parse to ensure extracted data adheres to a Pydantic schema.
• CORS Enabled: Configured to allow access from frontend applications (e.g., Angular).
• Health Check: Simple endpoint to verify server status.

Setup

Backend (FastAPI)

1. Initialize Virtual Environment: ```bash

   Create environment

   python -m venv venv

# Activate environment (Windows) .\venv\Scripts\activate

# Activate environment (Mac/Linux) # source venv/bin/activate

2. **Install Dependencies**:
   ```bash
   pip install -r requirements.txt

1. Configure Environment: Create a .env file in the root directory and add your OpenAI API key:

   OPENAI_API_KEY=your_api_key_here
   

   1. Run the Server: bash uvicorn backend.main:app --reload

Frontend (Angular)

1. Install Dependencies:

   cd ai-data-entry-ui
   npm install --legacy-peer-deps
   

   1. Run the UI: bash npm start

API Endpoints

GET /health

Returns the status of the server.

POST /api/v1/extract

Extracts data from a medical receipt image.

Request Body:

• file: Multipart image file.
• user_name (optional): Name of the employee.
• department (optional): Department of the employee.

Response: Returns a structured JSON matching the ExtractionResponse schema.

Project Structure

• backend/
  • main.py: Entry point and API endpoints.
  • schemas.py: Pydantic models for data validation and structured output.
  • services/:
  • openai_service.py: Logic for interacting with OpenAI API.
• ai-data-entry-ui/: Angular frontend.
• requirements.txt: Python dependencies.
• .env: Environment variables (not tracked by git).