Upload 4 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,49 @@
+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()

model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:43705b7f89313067909d08b52cdcd9540b2be9d29654d85bcf2a6bb433921e5e
+size 103147728

requirements.txt

@@ -0,0 +1,4 @@
+numpy
+tensorflow
+scikit-learn-intelex
+keras

xgb.json

@@ -0,0 +1 @@
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