Update app.py

app.py

@@ -1,70 +1,37 @@
 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
 
+st.header("Segmentación de dientes con rayos X")
 
-# 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
-
-model=from_pretrained_keras("SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net")
-
-st.header("Segmentation of Teeth in Panoramic X-ray Image Using UNet")
-
-# examples=["107.png","108.png","109.png"]
+st.markdown('''
 
-# st.markdown(link,unsafe_allow_html=True)
+Hola estudiantes de Platzi 🚀. Este modelo usan UNet para segmentar imágenes
+de dientos en rayos X. Se utila un modelo de Keras importado con la función
+`huggingface_hub.from_pretrained_keras`. Recuerda que el Hub de Hugging Face está integrado
+con muchas librerías como Keras, scikit-learn, fastai y otras.
 
+El modelo fue creado por [SerdarHelli](https://huggingface.co/SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net).
 
+''')
 
+model_id = "SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net"
+model=from_pretrained_keras(model_id)
 
+## Si una imagen tiene más de un canal entonces se convierte a escala de grises (1 canal) 
 def convert_one_channel(img):
-    #some images have 3 channels , although they are grayscale image
-    if len(img.shape)>2:
+    if len(img.shape)>1:
         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:
 
@@ -89,7 +56,7 @@ if image_file is not None:
       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)
+      output = cv2.drawContours(convert_one_channel(img), cnts, -1, (255, 0, 0) , 3)
 
 
       if output is not None :