Upload 3 files

app.py

@@ -0,0 +1,40 @@
+import streamlit as st
+import pandas as pd 
+import numpy as np
+from tensorflow import keras
+import tensorflow as tf
+from keras.models import load_model
+import matplotlib.pyplot as plt
+from PIL import Image
+
+st.title('Dental Type Segmentation')
+
+# Load the saved model outside the prediction function
+loaded_model = load_model('dental_model.h5')
+
+def prediction(file):
+    img = tf.keras.utils.load_img(file, target_size=(224, 224))
+    x = tf.keras.utils.img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+
+    # Predict the class probabilities
+    classes = loaded_model.predict(x)
+
+    # Get the predicted class label
+    classes = np.ravel(classes) # convert to 1D array
+    idx = np.argmax(classes)
+    clas = ['Segmentation1', 'Segmentation2'][idx]
+
+    return clas
+
+uploaded_file = st.file_uploader("Choose X-ray file")
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    image = image.resize((224, 224))
+    image = tf.keras.preprocessing.image.img_to_array(image)
+    image = image / 255.0
+    image = tf.expand_dims(image, axis=0)
+  
+if st.button('Predict'): 
+    result = prediction(uploaded_file)
+    st.write('Prediction is {}'.format(result))

dental_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:481f2e7105c2543e787aff73624b50a9dc8fd31abaf08c8da8b834312c7a678c
+size 31251320

requirements.txt

@@ -0,0 +1,6 @@
+pandas
+numpy
+scikit-learn==1.2.2
+tensorflow==2.12.0
+keras
+matplotlib