updated

app.py

@@ -2,18 +2,34 @@ import streamlit as st
 import tensorflow as tf
 import cv2
 import numpy as np
-from huggingface_hub import from_pretrained_keras
 from lime import lime_image
 from skimage.segmentation import mark_boundaries
 import matplotlib.pyplot as plt
+from vit import CNN_ViT
 
+hp = {}
+hp['image_size'] = 512
+hp['num_channels'] = 3
+hp['patch_size'] = 64
+hp['num_patches'] = (hp['image_size']**2) // (hp["patch_size"]**2)
+hp["flat_patches_shape"] = (hp["num_patches"], hp['patch_size']*hp['patch_size']*hp["num_channels"])
+hp['batch_size'] = 32
+hp['lr'] = 1e-4
+hp["num_epochs"] = 30
+hp['num_classes'] = 2
+hp["num_layers"] = 12
+hp["hidden_dim"] = 512
+hp["mlp_dim"] = 3072
+hp['num_heads'] = 12
+hp['dropout_rate'] = 0.1
+hp['class_names'] = ["breast_benign", "breast_malignant"]
 
+model = CNN_ViT(hp)
+model.load_weights("model/ResViT_for_breast_cancer_classification(1).keras")
 #model = tf.keras.models.load_model("model/resnet_for_breast_cancer-v1.h5")
-model = from_pretrained_keras("ErnestBeckham/BreastResViT-II")
+#model = from_pretrained_keras("ErnestBeckham/BreastResViT-II")
 explainer = lime_image.LimeImageExplainer()
 
-hp = {}
-hp['class_names'] = ["breast_benign", "breast_malignant"]
 
 def main():
     st.title("Breast Cancer Classification")