app.py

import gradio as gr
import tensorflow as tf
import keras 
import numpy as np
import sklearn
from sklearnex import patch_sklearn, unpatch_sklearn
patch_sklearn()
import xgboost as xgb

xgb_params = {
    'objective':                    'binary:logistic',
    'predictor':                    'cpu_predictor',
    'disable_default_eval_metric':  'true',
}
model_xgb= xgb.XGBClassifier(**xgb_params)
model_xgb.load_model('xgb.json')

base_cnn = keras.applications.resnet50.ResNet50(
    include_top=True,
    weights='imagenet',
)
base_cnn.load_weights('model.keras')
def fn(image):
  if len(image.shape)==2:
    img = np.stack([image,image,image],axis=2)
    img = np.resize(img,(224,224,3))
  elif len(image.shape)==3 and image.shape[2]==1:
    img = np.stack([image[:,:,0],image[:,:,0],image[:,:,0]],axis=2)
    img = np.resize(img,(224,224,3))
  else:
    img = np.resize(image,(224,224,3))
  img = np.expand_dims(img,axis=0)
  feats = base_cnn.predict(img)
  pred = model_xgb.predict(feats)

  
  if pred==0:
    return 'autism'
  else:
    return 'control'
    

demo = gr.Interface(
    fn,['image'],"text",
    
)

if __name__ == "__main__":
    demo.launch()