Update app.py

app.py

@@ -1,15 +1,5 @@
 import gradio as gr
 from predictor import predict, key_already_generated, pre_process_encrypt_send_purchase, decrypt_prediction
-import base64
-
-import os
-zama_path = os.path.join(os.path.dirname(__file__), "Img", "zama.png")
-epita_path = os.path.join(os.path.dirname(__file__), "Img", "Epita.png")
-
-def encode_image_to_base64(image_path):
-    with open(image_path, "rb") as image_file:
-        return base64.b64encode(image_file.read()).decode("utf-8")
-
 
 def key_generated():
     """
@@ -28,23 +18,19 @@ def key_generated():
         print("Keys have been generated ✅")
         return {gen_key_btn: gr.update(value="Keys have been generated ✅")}
     
+
 demo = gr.Blocks(css=".markdown-body { font-size: 18px; }")
 
 with demo:
-    zama_base64 = encode_image_to_base64(zama_path)
-    epita_base64 = encode_image_to_base64(epita_path)
-
     gr.Markdown(
-        f"""
+        """
         <div style="display: flex; justify-content: center; align-items: center;">
-            <img style="margin-right: 50px;" width=200 src="data:image/png;base64,{zama_base64}">
-            <img width=200 src="data:image/png;base64,{epita_base64}">
+            <img style="margin-right: 50px;" width=200 src="/file=Img/zama.png">
+            <img width=200 src="/file=Img/Epita.png">
         </div>
         """
     )
 
-
-
     gr.Markdown(
         """
         <h1 style="text-align: center;">Fraud Detection with FHE Model</h1>
@@ -107,11 +93,10 @@ with demo:
         """
     )
 
-    schema_base64 = encode_image_to_base64("Img/schema.png")
     gr.Markdown(
-        f"""
+        """
         <p align="center">
-            <img width="75%" height="30%" src="data:image/png;base64,{schema_base64}">
+            <img width="75%" height="30%" src="/file=Img/schema.png">
         </p>
         """
     )
@@ -135,7 +120,7 @@ with demo:
         key_generated,
         inputs=[],
         outputs=[gen_key_btn],
-    )#547
+    )
 
     gr.Markdown("<hr />")
 
@@ -268,7 +253,6 @@ with demo:
         label="Total FHE execution time (in seconds):", max_lines=1, interactive=False
     )
 
-    # Button to send the encodings to the server using post method
     execute_fhe_button.click(predict, outputs=[fhe_execution_time, execute_fhe_button])
 
     gr.Markdown("<hr />")
@@ -305,4 +289,4 @@ with demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()