README.md

Retfound Model API

This repository contains the Retfound model fine-tuned on the IDRID dataset and an API built with Flask to host and interact with the model.

Install Environment

1. Create a Python environment with conda:

   conda create -n retfound python=3.7.5 -y
   conda activate retfound

2. Install dependencies:

   git clone https://github.com/krishnaadithya/retfound_api.git
   cd retfound_api
   pip install -r requirements.txt

Steps to Use the API

1. Download Model File

• Find the model .pth file here.
• Download the file and place it in the finetune_IDRID folder in the root directory.

2. Running the API

• The API is built using Flask.

• Host the model by running:

  python retfound_api.py

3. Accessing the API

• For online tunneling, the current setup uses ngrok.

• Currently, the model is hosted locally, and you can use the following endpoint to push your image: https://623c-2001-8f8-166b-2a54-b5d7-fefa-f504-a420.ngrok-free.app/predict.

• Api file format: {'image': (image_path, image_data)}

• Use the provided Python code to call the API:

  import requests
  from PIL import Image
  import io
  import time
  
  # Replace 'your_image.jpg' with the path to your image file
  image_path = 'your_image.jpg'
  
  # Open the image file
  with open(image_path, 'rb') as f:
      image_data = f.read()
  
  # Create a dictionary containing the image file
  files = {'image': (image_path, image_data)}
  
  # Make a POST request to the API endpoint
  url = "https://623c-2001-8f8-166b-2a54-b5d7-fefa-f504-a420.ngrok-free.app/predict"  # Replace this link with your endpoint
  response = requests.post(url, files=files)
  
  
  # Check if the request was successful (status code 200)
  if response.status_code == 200:
      result = response.json()
      print(f'Predicted category: {result["predictions"]}')    
  else:
      print(f'Error: {response.text}')

Please replace your_image.jpg with the path to your image file before running the code.