README.md

metadata

title: Tuberculosis Detection ViT
emoji: 🩺
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.36.1
app_file: app.py
pinned: false
short_description: Classify chest X-ray images as Normal or Tuberculosis
tags:
  - medical-imaging
  - tuberculosis-detection
  - vision-transformer
  - pytorch
  - gradio

🩺 Tuberculosis Detection with Vision Transformer

Classify chest X-ray images as Normal or Tuberculosis using a Vision Transformer (ViT) model.

How to Use (Web Interface)

• Upload a chest X-ray image (JPEG/PNG).
• Click "Predict".
• View the prediction, confidence score, and probabilities.

How to Use (API)

Standard Endpoint

URL: https://sukhmani1303-tuberculosis-vit-model.hf.space/api/predict/ Method: POST Content-Type: multipart/form-data Input: Image file (JPEG/PNG) Output: JSON response

import requests

url = "https://sukhmani1303-tuberculosis-vit-model.hf.space/api/predict/"
files = {"file": open("chest_xray.jpg", "rb")}
response = requests.post(url, files=files)
print(response.json())

Raw Debug Endpoint

URL: https://sukhmani1303-tuberculosis-vit-model.hf.space/api/predict_raw/ Method: POST Content-Type: multipart/form-data Input: Image file (JPEG/PNG) Output: JSON response with raw debug information

import requests

url = "https://sukhmani1303-tuberculosis-vit-model.hf.space/api/predict_raw/"
files = {"file": open("chest_xray.jpg", "rb")}
response = requests.post(url, files=files)
print(response.json())

Expected Response Format

{
  "status": "success",
  "prediction": "Normal",
  "confidence": 0.8542,
  "probabilities": {
    "Normal": 0.8542,
    "Tuberculosis": 0.1458
  }
}

Medical Disclaimer

This tool is for educational and research purposes only. It is not intended for medical diagnosis. Always consult qualified healthcare professionals for medical advice and diagnosis.

Model Information

• Architecture: Vision Transformer (ViT)
• Task: Binary classification (Normal vs Tuberculosis)
• Input: Chest X-ray images
• Image Size: 224x224 pixels