Initial commit

.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
+model2.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,31 @@
+import os
+import keras
+from tensorflow.keras.models import load_model # type: ignore
+import streamlit as st 
+import tensorflow as tf
+import numpy as np
+
+st.header('Dental Classification CNN Model')
+#class_names = ['daisy', 'dandelion', 'rose', 'sunflower', 'tulip']
+class_names = ['Calculus', 'Dental Caries', 'Gingivitis', 'Hypodontia', 'Tooth discoloration']
+
+model = load_model('model2.keras')
+
+def classify_images(image_path):
+    input_image = tf.keras.utils.load_img(image_path, target_size=(256,256))
+    input_image_array = tf.keras.utils.img_to_array(input_image)
+    input_image_exp_dim = tf.expand_dims(input_image_array,0)
+
+    predictions = model.predict(input_image_exp_dim)
+    result = tf.nn.softmax(predictions[0])
+    outcome = 'The Image belongs to ' + class_names[np.argmax(result)] + ' with a score of '+ str(np.max(result)*100)
+    return outcome
+
+uploaded_file = st.file_uploader('Upload an Image')
+if uploaded_file is not None:
+    with open(os.path.join('upload', uploaded_file.name), 'wb') as f:
+        f.write(uploaded_file.getbuffer())
+    
+    st.image(uploaded_file, width = 200)
+
+    st.markdown(classify_images(uploaded_file))

model2.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ecc1ed3cbe12c5ad66f892f301100e2264bb767cbb9394f4803182cdc91c7c70
+size 32383065