minor updates

app.py

@@ -16,6 +16,7 @@ EXAMPLE_IMAGES_AUTH = ['./VGGFace2/n000001/0013_01.jpg', './VGGFace2/n000149/001
 
 # --- Global Variables ---
 face_app = None
+DB_SUBJECT_COUNT = 0
 
 # --- Helper Functions ---
 def initialize_face_app():
@@ -37,7 +38,13 @@ def run_binary(bin_path, *args):
     command = [bin_path] + list(args)
     print(f"Running command: {' '.join(command)}")
     
-    cwd = "."
+    # Determine working directory based on binary path
+    if "biometric-recognition-FHE" in bin_path:
+        cwd = "./biometric-recognition-FHE"
+    elif "biometric-search-FHE" in bin_path:
+        cwd = "./biometric-search-FHE"
+    else:
+        cwd = "."
     
     try:
         os.chmod(bin_path, 0o755)
@@ -157,14 +164,19 @@ body, .gradio-container { background: var(--background); color: var(--foreground
 with gr.Blocks(css=css) as demo:
     # --- Header ---
     gr.HTML("""
-        <div class="main-header">
-            <img src="./static/logo.png" alt="Suraksh.AI Logo" style="height: 60px; margin-bottom: 1rem;">
+        <div class="main-header">            
             <h1>Suraksh AI</h1>
             <p style="color: #EEEEEC; opacity: 0.9;">The Future of Secure Biometrics</p>
             <a href="https://suraksh.ai/" target="_blank">🌐 Visit Our Website</a>
         </div>
     """)
 
+    # --- Key Generation on Load ---
+    # Generate keys once for each demo when the app starts up
+    demo.load(lambda: run_binary("./bin/genKeys.bin", "128", "genkeys"), None, None)
+    demo.load(lambda: run_binary("./bin/search.bin", "128", "genkeys"), None, None)
+
+
     # --- Main Tabs for Demo Mode ---
     with gr.Tabs() as mode_tabs:
         # --- 1:1 Recognition Demo ---
@@ -177,15 +189,50 @@ with gr.Blocks(css=css) as demo:
                     with gr.Group(elem_classes="narrative-section"):
                         gr.HTML('<h2 class="narrative-header">The Problem: How Your Face Can Be Stolen</h2>')
                         gr.HTML("""<div class="warning-card"><h3>⚠️ Your Biometric Data is Exposed!</h3><p>Most systems handle biometric data in plaintext. This means your facial embedding—a digital map of your face—can be stolen and used to reconstruct your image, creating a major privacy risk.</p></div>""")
-                        with gr.Row():
-                            with gr.Column():
-                                vuln_image_in = create_image_selection_ui("1. Select an Image", EXAMPLE_IMAGES_ENROLL)
-                            with gr.Column():
-                                gr.HTML('<h3 class="step-header">2. Reconstruct the Face</h3><p>Click below to simulate an attacker stealing the embedding and recreating the face.</p>')
-                                reconstruct_btn = gr.Button("😱 Reconstruct My Face!", variant="stop")
-                                vuln_image_out = gr.Image(type="filepath", label="Reconstructed Face", interactive=False)
                         
-                        reconstruct_btn.click(lambda x: "./static/reconstructed.png" if x else None, inputs=vuln_image_in, outputs=vuln_image_out)
+                        with gr.Column():
+                            gr.HTML('<h3 class="step-header">1. Original Image</h3>')
+                            gr.Image(value=EXAMPLE_IMAGES_ENROLL[2], label="Original Face", interactive=False, show_label=False, container=False)
+
+                            gr.HTML('<h3 class="step-header" style="margin-top: 2rem;">2. Simulate Attack: Steal Data</h3><p>An attacker breaches the system and steals the stored facial embedding. Click the button to simulate this theft.</p>')
+                            extract_btn = gr.Button("😱 Steal Biometric Data", variant="primary")
+
+                            with gr.Group(visible=False) as stolen_data_group:
+                                feature_output = gr.JSON(label="Stolen Feature Vector (Face Embedding)")
+                                gr.HTML('<h3 class="step-header" style="margin-top: 2rem;">3. Simulate Attack: Reconstruct Face</h3><p>Now, the attacker uses the stolen features to create a reconstruction of the face, completely compromising the user\'s privacy.</p>')
+                                reconstruct_btn = gr.Button("🎭 Reconstruct Face from Stolen Data", variant="stop")
+
+                            with gr.Group(visible=False) as reconstructed_image_group:
+                                reconstructed_output = gr.Image(label="Reconstructed Face", interactive=False, show_label=False)
+
+                        def extract_and_reveal(image_path):
+                            embedding = extract_embedding(image_path)
+                            feature_json = {"embedding": embedding}
+                            return {
+                                feature_output: feature_json,
+                                stolen_data_group: gr.update(visible=True),
+                                extract_btn: gr.update(value="Data Stolen!", interactive=False)
+                            }
+
+                        def show_reconstruction():
+                            reconstructed_image_path = "./static/reconstructed.png"
+                            return {
+                                reconstructed_output: reconstructed_image_path,
+                                reconstructed_image_group: gr.update(visible=True),
+                                reconstruct_btn: gr.update(interactive=False)
+                            }
+
+                        extract_btn.click(
+                            fn=extract_and_reveal,
+                            inputs=gr.State(EXAMPLE_IMAGES_ENROLL[0]),
+                            outputs=[feature_output, stolen_data_group, extract_btn]
+                        )
+
+                        reconstruct_btn.click(
+                            fn=show_reconstruction,
+                            inputs=None,
+                            outputs=[reconstructed_output, reconstructed_image_group, reconstruct_btn]
+                        )
 
                 # --- Secure System Tab ---
                 with gr.TabItem("✅ The Suraksh.AI Solution"):
@@ -196,8 +243,14 @@ with gr.Blocks(css=css) as demo:
                         with gr.Row():
                             with gr.Column():
                                 rec_ref_img = create_image_selection_ui("1. Provide Reference Image", EXAMPLE_IMAGES_ENROLL)
+                                with gr.Group(visible=False) as rec_ref_features_group:
+                                    rec_ref_raw_features = gr.JSON(label="Raw Features (Plaintext)")
+                                    rec_ref_encrypted_features = gr.Textbox(label="Encrypted Features (Ciphertext)", interactive=False, lines=5)
                             with gr.Column():
                                 rec_probe_img = create_image_selection_ui("2. Provide Probe Image", EXAMPLE_IMAGES_AUTH)
+                                with gr.Group(visible=False) as rec_probe_features_group:
+                                    rec_probe_raw_features = gr.JSON(label="Raw Features (Plaintext)")
+                                    rec_probe_encrypted_features = gr.Textbox(label="Encrypted Features (Ciphertext)", interactive=False, lines=5)
 
                         with gr.Accordion("Advanced Settings", open=False):
                             rec_threshold = gr.Slider(-512*5, 512*5, value=133, label="Match Strictness", info="A higher value means a stricter match is required.")
@@ -209,29 +262,43 @@ with gr.Blocks(css=css) as demo:
                         rec_result = gr.HTML()
 
                         def secure_recognition_flow(ref_img, probe_img, threshold, sec_level):
-                            yield "Initializing...", ""
-                            bin_path = "./bin/genKeys.bin"
-                            run_binary(bin_path, sec_level, "genkeys")
-                            yield "✅ Keys Generated... Encrypting Reference...", ""
-                            _, _ = extract_embedding(ref_img, "enroll")
+                            # Reset UI
+                            yield "Initializing...", "", gr.update(visible=False), None, None, gr.update(visible=False), None, None
+
+                            # Process Reference Image
+                            yield "Extracting reference features...", "", gr.update(visible=False), None, None, gr.update(visible=False), None, None
+                            ref_emb, _ = extract_embedding(ref_img, "enroll")
+                            
+                            yield "Encrypting reference features...", "", gr.update(visible=True), {"embedding": ref_emb}, None, gr.update(visible=False), None, None
                             run_binary("./bin/encReference.bin", sec_level, "encrypt")
-                            yield "✅ Reference Encrypted... Encrypting Probe...", ""
-                            _, _ = extract_embedding(probe_img, "auth")
+                            ref_ciphertext, _ = run_binary("./bin/encReference.bin", sec_level, "print")
+
+
+                            # Process Probe Image
+                            yield "✅ Reference Encrypted. Extracting probe features...", "", gr.update(visible=True), {"embedding": ref_emb}, ref_ciphertext, gr.update(visible=False), None, None
+                            probe_emb, _ = extract_embedding(probe_img, "auth")
+
+                            yield "Encrypting probe features...", "", gr.update(visible=True), {"embedding": ref_emb}, ref_ciphertext, gr.update(visible=True), {"embedding": probe_emb}, None
                             run_binary("./bin/encProbe.bin", sec_level, "encrypt")
-                            yield "✅ Probe Encrypted... Performing Secure Match...", ""
+                            probe_ciphertext, _ = run_binary("./bin/encProbe.bin", sec_level, "print")
+
+                            # Perform Match
+                            yield "✅ Probe Encrypted. Performing Secure Match...", "", gr.update(visible=True), {"embedding": ref_emb}, ref_ciphertext, gr.update(visible=True), {"embedding": probe_emb}, probe_ciphertext
                             run_binary("./bin/recDecision.bin", sec_level, "decision", str(threshold))
-                            yield "✅ Match Computed... Decrypting Result...", ""
+                            
+                            yield "✅ Match Computed. Decrypting Result...", "", gr.update(visible=True), {"embedding": ref_emb}, ref_ciphertext, gr.update(visible=True), {"embedding": probe_emb}, probe_ciphertext
                             output, _ = run_binary("./bin/decDecision.bin", sec_level, "decision")
+                            
                             if "match" in output.lower():
                                 result_html = """<div class="result-container match-verified"><h2>✅ MATCH VERIFIED</h2><p>Identity successfully confirmed under FHE.</p></div>"""
                             else:
                                 result_html = """<div class="result-container no-match"><h2>❌ NO MATCH</h2><p>Identity verification failed.</p></div>"""
-                            yield "Done!", result_html
+                            yield "Done!", result_html, gr.update(visible=True), {"embedding": ref_emb}, ref_ciphertext, gr.update(visible=True), {"embedding": probe_emb}, probe_ciphertext
 
                         rec_run_btn.click(
                             fn=secure_recognition_flow,
                             inputs=[rec_ref_img, rec_probe_img, rec_threshold, rec_sec_level],
-                            outputs=[rec_status, rec_result]
+                            outputs=[rec_status, rec_result, rec_ref_features_group, rec_ref_raw_features, rec_ref_encrypted_features, rec_probe_features_group, rec_probe_raw_features, rec_probe_encrypted_features]
                         )
 
         # --- 1:N Search Demo ---
@@ -247,13 +314,21 @@ with gr.Blocks(css=css) as demo:
 
                         with gr.Row():
                             with gr.Column():
-                                search_enroll_img = create_image_selection_ui("1. Enroll Subjects into DB", EXAMPLE_IMAGES_ENROLL)
-                                search_offset = gr.Number(label="Subject Offset ID", info="A unique ID for this person in the DB.", precision=0)
+                                gr.HTML('<h3 class="step-header">1. Enroll Subjects into DB</h3>')
+                                search_enroll_img = create_image_selection_ui("Select Image to Enroll", EXAMPLE_IMAGES_ENROLL)
                                 search_enroll_btn = gr.Button("➕ Encrypt & Add to Database", variant="secondary")
+                                with gr.Group(visible=False) as enroll_features_group:
+                                    enroll_raw_features = gr.JSON(label="Raw Features (Plaintext)")
+                                    enroll_encrypted_features = gr.Textbox(label="Encrypted Features (Ciphertext)", interactive=False, lines=5)
                                 search_enroll_status = gr.HTML()
+
                             with gr.Column():
-                                search_probe_img = create_image_selection_ui("2. Search for a Subject", EXAMPLE_IMAGES_AUTH)
+                                gr.HTML('<h3 class="step-header">2. Search for a Subject</h3>')
+                                search_probe_img = create_image_selection_ui("Select Image to Search", EXAMPLE_IMAGES_AUTH)
                                 search_run_btn = gr.Button("🚀 Perform Secure 1:N Search", variant="primary", size="lg")
+                                with gr.Group(visible=False) as search_features_group:
+                                    search_raw_features = gr.JSON(label="Raw Features (Plaintext)")
+                                    search_encrypted_features = gr.Textbox(label="Encrypted Features (Ciphertext)", interactive=False, lines=5)
                                 search_status = gr.HTML(elem_classes="status-text")
                         
                         search_result = gr.HTML()
@@ -262,41 +337,57 @@ with gr.Blocks(css=css) as demo:
                             search_threshold = gr.Slider(-512*5, 512*5, value=133, label="Match Strictness")
                             search_sec_level = gr.Dropdown(SECURITYLEVELS, value="128", label="Security Level")
                         
-                        demo.load(lambda: run_binary("./bin/search.bin", "128", "genkeys"), None, None)
-
-                        def secure_enroll_flow(image, offset, sec_level):
-                            if image is None or offset is None: raise gr.Error("Please provide an image and a unique offset ID.")
-                            yield "Encrypting and adding subject..."
-                            _, emb_path = extract_embedding(image, "search_enroll")
-                            run_binary("./bin/search.bin", sec_level, "encRef", emb_path, str(int(offset)))
+                        def secure_enroll_flow(image, sec_level):
+                            global DB_SUBJECT_COUNT
+                            if image is None: raise gr.Error("Please provide an image to enroll.")
+                            
+                            current_id = DB_SUBJECT_COUNT
+                            
+                            yield "Extracting features...", gr.update(visible=False), None, None
+                            embedding, emb_path = extract_embedding(image, "search_enroll")
+                            
+                            yield "Encrypting features...", gr.update(visible=True), {"embedding": embedding}, None
+                            run_binary("./bin/search.bin", sec_level, "encRef", emb_path, str(current_id))
+                            ciphertext, _ = run_binary("./bin/search.bin", sec_level, "printRef")
+                            
+                            yield "Adding to secure database...", gr.update(visible=True), {"embedding": embedding}, ciphertext
                             run_binary("./bin/search.bin", sec_level, "addRef")
-                            yield f"✅ Subject with ID {int(offset)} added to the secure database."
+                            
+                            DB_SUBJECT_COUNT += 1
+                            yield f"✅ Subject with ID {current_id} added. Total subjects: {DB_SUBJECT_COUNT}.", gr.update(visible=True), {"embedding": embedding}, ciphertext
 
                         def secure_search_flow(image, threshold, sec_level):
                             if image is None: raise gr.Error("Please provide an image to search.")
-                            yield "Initializing secure search...", ""
-                            _, emb_path = extract_embedding(image, "search_auth")
+                            
+                            yield "Extracting probe features...", "", gr.update(visible=False), None, None
+                            embedding, emb_path = extract_embedding(image, "search_auth")
+
+                            yield "Encrypting probe features...", "", gr.update(visible=True), {"embedding": embedding}, None
                             run_binary("./bin/search.bin", sec_level, "encProbe", emb_path)
-                            yield "✅ Probe encrypted... Searching database...", ""
+                            ciphertext, _ = run_binary("./bin/search.bin", sec_level, "printProbe")
+
+                            yield "✅ Probe encrypted. Searching database...", "", gr.update(visible=True), {"embedding": embedding}, ciphertext
                             run_binary("./bin/search.bin", sec_level, "search")
-                            yield "✅ Search complete... Decrypting results...", ""
+                            
+                            yield "✅ Search complete. Decrypting results...", "", gr.update(visible=True), {"embedding": embedding}, ciphertext
                             output, _ = run_binary("./bin/search.bin", sec_level, "decDecisionClear", str(threshold))
+                            
                             if "found" in output.lower():
                                 result_html = """<div class="result-container match-verified"><h2>✅ SUBJECT FOUND</h2><p>The subject was successfully found in the database.</p></div>"""
                             else:
                                 result_html = """<div class="result-container no-match"><h2>❌ NOT FOUND</h2><p>The subject was not found in the database.</p></div>"""
-                            yield "Done!", result_html
+                            yield "Done!", result_html, gr.update(visible=True), {"embedding": embedding}, ciphertext
 
                         search_enroll_btn.click(
                             fn=secure_enroll_flow,
-                            inputs=[search_enroll_img, search_offset, search_sec_level],
-                            outputs=[search_enroll_status]
+                            inputs=[search_enroll_img, search_sec_level],
+                            outputs=[search_enroll_status, enroll_features_group, enroll_raw_features, enroll_encrypted_features]
                         )
                         
                         search_run_btn.click(
                             fn=secure_search_flow,
                             inputs=[search_probe_img, search_threshold, search_sec_level],
-                            outputs=[search_status, search_result]
+                            outputs=[search_status, search_result, search_features_group, search_raw_features, search_encrypted_features]
                         )
 
 # --- Launch the Application ---