Delete app.py

app.py

@@ -1,100 +0,0 @@
-import streamlit as st 
-
-import tensorflow as tf
-from PIL import Image
-import numpy as np
-import cv2
-from huggingface_hub import from_pretrained_keras
-
-
-try:
-    model=from_pretrained_keras("SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net")
-except:
-    model=tf.keras.models.load_model("dental_xray_seg.h5")
-    pass
-    
-st.header("Segmentation of Teeth in Panoramic X-ray Image Using UNet")
-
-examples=["1.jbg","2.jbg","3.jbg"]
-link='Check Out Our Github Repo ! [link](https://github.com/SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net)'
-st.markdown(link,unsafe_allow_html=True)
-
-
-def load_image(image_file):
-	img = Image.open(image_file)
-	return img
-
-def convert_one_channel(img):
-    #some images have 3 channels , although they are grayscale image
-    if len(img.shape)>2:
-        img= cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        return img
-    else:
-        return img
-    
-def convert_rgb(img):
-    #some images have 3 channels , although they are grayscale image
-    if len(img.shape)==2:
-        img= cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)  
-        return img
-    else:
-        return img
-    
-    
-st.subheader("Upload Dental Panoramic X-ray Image Image")
-image_file = st.file_uploader("Upload Images", type=["png","jpg","jpeg"])
-
-
-col1, col2, col3 = st.columns(3)
-with col1:
-    ex=load_image(examples[0])
-    st.image(ex,width=200)
-    if st.button('Example 1'):
-        image_file=examples[0]
-
-with col2:
-    ex1=load_image(examples[1])
-    st.image(ex1,width=200)
-    if st.button('Example 2'):
-        image_file=examples[1]
-
-
-with col3:
-    ex2=load_image(examples[2])
-    st.image(ex2,width=200)
-    if st.button('Example 3'):
-        image_file=examples[2]
-    
-    
-if image_file is not None:
-
-      img=load_image(image_file)
-      
-      st.text("Making A Prediction ....")
-      st.image(img,width=850)
-      
-      img=np.asarray(img)
-  
-      img_cv=convert_one_channel(img)
-      img_cv=cv2.resize(img_cv,(512,512), interpolation=cv2.INTER_LANCZOS4)
-      img_cv=np.float32(img_cv/255)
-      
-      img_cv=np.reshape(img_cv,(1,512,512,1))
-      prediction=model.predict(img_cv)
-      predicted=prediction[0]
-      predicted = cv2.resize(predicted, (img.shape[1],img.shape[0]), interpolation=cv2.INTER_LANCZOS4)
-      mask=np.uint8(predicted*255)# 
-      _, mask = cv2.threshold(mask, thresh=0, maxval=255, type=cv2.THRESH_BINARY+cv2.THRESH_OTSU)
-      kernel =( np.ones((5,5), dtype=np.float32))
-      mask=cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel,iterations=1 )  
-      mask=cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel,iterations=1 )
-      cnts,hieararch=cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
-      output = cv2.drawContours(convert_rgb(img), cnts, -1, (255, 0, 0) , 3)
-
-
-      if output is not None :      
-          st.subheader("Predicted Image")  
-          st.write(output.shape)
-          st.image(output,width=850)
-
-      st.text("DONE ! ....")