Update app.py

app.py

@@ -1,5 +1,11 @@
 import gradio as gr
 from predictor import predict, key_already_generated, pre_process_encrypt_send_purchase, decrypt_prediction
+import base64
+
+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():
     """
@@ -21,15 +27,20 @@ def key_generated():
 demo = gr.Blocks(css=".markdown-body { font-size: 18px; }")
 
 with demo:
+    zama_base64 = encode_image_to_base64("./Img/zama.png")
+    epita_base64 = encode_image_to_base64("./Img/Epita.png")
+
     gr.Markdown(
-        """
+        f"""
         <div style="display: flex; justify-content: center; align-items: center;">
-            <img style="margin-right: 50px;" width=200 src="https://user-images.githubusercontent.com/5758427/197816413-d9cddad3-ba38-4793-847d-120975e1da11.png">
-            <img width=200 src="https://upload.wikimedia.org/wikipedia/fr/d/d8/Epita.png">
+            <img style="margin-right: 50px;" width=200 src="data:image/png;base64,{zama_base64}">
+            <img width=200 src="data:image/png;base64,{epita_base64}">
         </div>
         """
     )
 
+
+
     gr.Markdown(
         """
         <h1 style="text-align: center;">Fraud Detection with FHE Model</h1>
@@ -92,10 +103,11 @@ with demo:
         """
     )
 
+    schema_base64 = encode_image_to_base64("./Img/schema.png")
     gr.Markdown(
-        """
+        f"""
         <p align="center">
-            <img width="75%" height="30%" src="https://raw.githubusercontent.com/kcelia/Img/main/fhe_anonymization_banner.png">
+            <img width="75%" height="30%" src="data:image/png;base64,{schema_base64}">
         </p>
         """
     )
@@ -127,10 +139,23 @@ with demo:
 
     gr.Markdown(
         "## Step 2: Make your purchase\n\n"
-        """ 🛍️ It's time to shop! To simulate your latest purchase, please provide the details of your most recent transaction.
+        """ 
+        🛍️ It's time to shop! To simulate your latest purchase, please provide the details of your most recent transaction.
+        
+        If you don't have an idea, you can pre-fill with an example of fraud or non-fraud.
         """
     )
 
+    def prefill_fraud():
+        return 34, 50, 3, False, False, False, True
+    
+    def prefill_no_fraud():
+        return 12, 2, 0.7, True, False, True, False
+
+    with gr.Row():
+        prefill_button = gr.Button("Exemple Fraud")
+        prefill_button_no = gr.Button("Exemple No-Fraud")
+
     with gr.Row():
         with gr.Column():
             distance_home = gr.Slider(
@@ -174,6 +199,35 @@ with demo:
                 info="Check if you made your purchase online"
             )
 
+        
+    prefill_button.click(
+        fn=prefill_fraud,
+        inputs=[],
+        outputs=[
+            distance_home, 
+            distance_last, 
+            ratio, 
+            repeat_retailer, 
+            used_chip, 
+            used_pin_number, 
+            online
+        ]
+    )
+
+    prefill_button_no.click(
+        fn=prefill_no_fraud,
+        inputs=[],
+        outputs=[
+            distance_home, 
+            distance_last, 
+            ratio, 
+            repeat_retailer, 
+            used_chip, 
+            used_pin_number, 
+            online
+        ]
+    )
+
     with gr.Row():
         with gr.Column(scale=2):
             encrypt_button_applicant = gr.Button("Encrypt the inputs and send to server.")