Update app.py

app.py

@@ -1,26 +1,12 @@
-import gradio as gr
+import streamlit as st
+from streamlit_back_camera_input import back_camera_input
+
 import matplotlib.pyplot as plt
 import tensorflow as tf
 
 loaded_model = tf.saved_model.load("model/")
 loaded_model = loaded_model.signatures["serving_default"]
 
-def js_to_prefere_the_back_camera_of_mobilephones():
-    custom_html = """
-    <script>
-    const originalGetUserMedia = navigator.mediaDevices.getUserMedia.bind(navigator.mediaDevices);
-    
-    navigator.mediaDevices.getUserMedia = (constraints) => {
-      if (!constraints.video.facingMode) {
-        constraints.video.facingMode = {ideal: "environment"};
-      }
-      constraints.video.transform = [{flipX: true}];
-      return originalGetUserMedia(constraints);
-    };
-    </script>
-    """
-    return custom_html
-
 def get_target_shape(original_shape):
     original_aspect_ratio = original_shape[0] / original_shape[1]
 
@@ -111,13 +97,7 @@ def compute_saliency(input_image, alpha=0.65):
         return blended_image
 
 
-examples = [
-    "examples/kirsten-frank-o1sXiz_LU1A-unsplash.jpg",
-    "examples/oscar-fickel-F5ze5FkEu1g-unsplash.jpg",
-    "examples/ting-tian-_79ZJS8pV70-unsplash.jpg",
-    "examples/gina-domenique-LmrAUrHinqk-unsplash.jpg",
-    "examples/robby-mccullough-r05GkQBcaPM-unsplash.jpg",
-]
+
 
 with gr.Blocks(head=js_to_prefere_the_back_camera_of_mobilephones()) as demo:
     with gr.Row():
@@ -127,8 +107,5 @@ with gr.Blocks(head=js_to_prefere_the_back_camera_of_mobilephones()) as demo:
         btn.click(fn=compute_saliency, inputs=input_image, outputs=output_image, api_name="compute_saliency")
 
 if __name__ == "__main__":
-    #demo.title("Visual Saliency Prediction")
-    #demo.description("A demo to predict where humans fixate on an image using a deep learning model trained on eye movement data. Upload an image file, take a snapshot from your webcam, or paste an image from the clipboard to compute the saliency map.")
-    #demo.article("For more information on the model, check out [GitHub](https://github.com/alexanderkroner/saliency) and the corresponding [paper](https://doi.org/10.1016/j.neunet.2020.05.004).")
-    #demo.allow_flagging("never")
+
     demo.launch()