adding app and req files

app.py

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+import streamlit as st
+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")
+st.subheader("Iteration to improve demo")
+st.markdown(
+    """
+    Demo for testing image segmentation
+"""
+)
+
+
+'''
+Technical Overview
+Architecture: It utilizes the U-Net architecture, a popular "encoder-decoder" convolutional neural network (CNN) specifically optimized for biomedical image segmentation where pixel-level accuracy is critical.
+Performance: In the accompanying research, the model achieved a Dice overlap score of 95.4% for overall teeth segmentation.
+Post-Processing: A key highlight of this specific implementation is the use of grayscale morphological filtering and operations applied to the sigmoid output. This reduces tooth counting errors significantly (from 26.8% down to roughly 6.2%).Dataset: The model was trained on a relatively small but highly curated dataset (approximately 105–116 panoramic images) based on work by Abdi et al. (2015).Key ApplicationsClinical Diagnosis: Assists dentists in identifying the boundaries of individual teeth to detect caries, lesions, or bone loss.
+Forensics and Identification: Automates the process of identifying dental patterns for human remains or age/gender determination.
+Treatment Planning: Provides a baseline for orthodontic therapy workups by isolating dental structures from the surrounding mandible and maxilla.
+'''
+
+
+## Select and load the model
+model_id = "SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net"
+model = from_pretrained_keras(model_id)
+
+## Allow the user to upload an image
+archivo_imagen = st.file_uploader("Sube aquí tu imagen.", type=["png", "jpg", "jpeg"])
+
+## If an image has more than one channel, it is converted to grayscale (1 channel)
+def convertir_one_channel(img):
+    if len(img.shape) > 2:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        return img
+    else:
+        return img
+
+def convertir_rgb(img):
+    if len(img.shape) == 2:
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
+        return img
+    else:
+        return img
+
+## We will manipulate the interface so we can use example images
+## If the user clicks on an example, the model will run with the following:
+ejemplos = ["dientes_1.png", "dientes_2.png", "dientes_3.png"]
+
+## Create three columns; an example image will be in each one
+col1, col2, col3 = st.columns(3)
+
+with col1:
+    ## The image is loaded and displayed in the interface
+    ex = Image.open(ejemplos[0])
+    st.image(ex, width=200)
+    ## If the button is pressed, we will use this example in the model
+    if st.button("Corre este ejemplo 1"):
+        archivo_imagen = ejemplos[0]
+
+with col2:
+    ex1 = Image.open(ejemplos[1])
+    st.image(ex1, width=200)
+    if st.button("Corre este ejemplo 2"):
+        archivo_imagen = ejemplos[1]
+
+with col3:
+    ex2 = Image.open(ejemplos[2])
+    st.image(ex2, width=200)
+    if st.button("Corre este ejemplo 3"):
+        archivo_imagen = ejemplos[2]
+
+## If we have an image to input into the model, 
+## we process it and feed it to the model
+if archivo_imagen is not None:
+    ## Load the image with PIL, display it, and convert it to a NumPy array
+    img = Image.open(archivo_imagen)
+    st.image(img, width=850)
+    img = np.asarray(img)
+
+    ## Process the image for model input
+    img_cv = convertir_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))
+
+    ## Feed the NumPy array into the model
+    predicted = model.predict(img_cv)
+    predicted = predicted[0]
+
+    ## Resize the image back to its original shape and add the segmentation masks
+    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(convertir_one_channel(img), cnts, -1, (255, 0, 0), 3)
+
+    ## If we successfully obtained a result, display it in the interface
+    if output is not None:
+        st.subheader("Segmentación:")
+        st.write(output.shape)
+        st.image(output, width=850)
+        
+    st.markdown("Thanks for using our demo!")

requirements.txt

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+numpy
+Pillow
+scipy
+opencv-python
+tensorflow