created app

app.py

@@ -0,0 +1,98 @@
+import streamlit as st
+import tensorflow as tf
+import cv2
+import numpy as np
+from patchify import patchify
+from huggingface_hub import from_pretrained_keras
+
+model = from_pretrained_keras('ErnestBeckham/MulticancerViT')
+
+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['class_names'] = ['cervix_koc',
+                      'cervix_dyk',
+                      'cervix_pab',
+                      'cervix_sfi',
+                      'cervix_mep',
+                      'colon_bnt',
+                      'colon_aca',
+                      'lung_aca',
+                      'lung_bnt',
+                      'lung_scc',
+                      'oral_scc',
+                      'oral_normal',
+                      'kidney_tumor',
+                      'kidney_normal',
+                      'breast_benign',
+                      'breast_malignant',
+                      'lymph_fl',
+                      'lymph_cll',
+                      'lymph_mcl',
+                      'brain_tumor',
+                      'brain_glioma',
+                      'brain_menin']
+
+def main():
+    st.title("Multi-Cancer Classification")
+
+    # Upload image through drag and drop
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Convert the uploaded file to OpenCV format
+        image = convert_to_opencv(uploaded_file)
+        
+        # Display the uploaded image
+        st.image(image, channels="BGR", caption="Uploaded Image", use_column_width=True)
+
+        # Display the image shape
+        image_class = predict_single_image(image, model, hp)
+        st.write(f"Image Class: {image_class}")
+
+def convert_to_opencv(uploaded_file):
+    # Read the uploaded file using OpenCV
+    image_bytes = uploaded_file.read()
+    np_arr = np.frombuffer(image_bytes, np.uint8)
+    image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+    return image
+
+def detect_image_shape(image):
+    # Get the image shape
+    return image.shape
+
+def preprocess_image(image, hp):
+    # Resize the image to the expected input size
+    image = cv2.resize(image, (hp['image_size'], hp['image_size']))
+    # Normalize pixel values to be in the range [0, 1]
+    image = image / 255.0
+    # Extract patches using the same patching mechanism as during training
+    patch_shape = (hp['patch_size'], hp['patch_size'], hp['num_channels'])
+    patches = patchify(image, patch_shape, hp['patch_size'])
+    # Flatten the patches
+    patches = np.reshape(patches, hp['flat_patches_shape'])
+    # Convert the flattened patches into a format suitable for prediction
+    patches = patches.astype(np.float32)
+    
+    return patches
+
+def predict_single_image(image, model, hp):
+    # Preprocess the image
+    preprocessed_image = preprocess_image(image, hp)
+    # Convert the preprocessed image to a TensorFlow tensor if needed
+    preprocessed_image = tf.convert_to_tensor(preprocessed_image)
+    # Add an extra batch dimension (required for model.predict)
+    preprocessed_image = tf.expand_dims(preprocessed_image, axis=0)
+    # Make the prediction
+    predictions = model.predict(preprocessed_image)
+
+    np.around(predictions)
+    y_pred_classes = np.argmax(predictions, axis=1)
+    class_name = hp['class_names'][y_pred_classes[0]]
+    return class_name
+    
+if __name__ == "__main__":
+    main()