Update app.py

app.py

@@ -73,56 +73,56 @@ with app_tab:
     picture = st.camera_input("Take a picture", disabled=not enable)
 
     if picture is not None:
-    with st.spinner("Analyzing face..."):
-        image_pil = Image.open(picture)
-        np_webcam = np.array(image_pil)
-        cv2_webcam = cv2.cvtColor(np_webcam, cv2.COLOR_RGB2BGR)
-
-        webcam_faces = app.get(cv2_webcam, max_num=1)
-
-        if not webcam_faces:
-            st.warning("No face detected in the captured image.")
-        else:
-            webcam_emb = torch.tensor(webcam_faces[0].embedding, dtype=torch.float32)
-
-            similarity_scores = []
-            for path in image_paths:
-                img = cv2.imread(path)
-                faces = app.get(img, max_num=1)
-                if not faces:
-                    similarity_scores.append(torch.tensor(-1.0))
-                    continue
-
-                face_emb = torch.tensor(faces[0].embedding, dtype=torch.float32)
-                score = F.cosine_similarity(face_emb, webcam_emb, dim=0)
-                similarity_scores.append(score)
-
-            similarity_scores = torch.stack(similarity_scores)
-            match_idx = torch.argmax(similarity_scores)
-            matched_score = similarity_scores[match_idx].item()
-
-            # Draw bounding box and name
-            (x1, y1, x2, y2) = map(int, webcam_faces[0].bbox)
-            cv2.rectangle(cv2_webcam, (x1, y1), (x2, y2), (0, 255, 0), 2)
-
-            if matched_score >= 0.6:
-                matched_name = os.path.basename(image_paths[match_idx]).split('.')[0]
-                cv2.putText(cv2_webcam, matched_name, (x1, y1 - 10), 
-                            cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        with st.spinner("Analyzing face..."):
+            image_pil = Image.open(picture)
+            np_webcam = np.array(image_pil)
+            cv2_webcam = cv2.cvtColor(np_webcam, cv2.COLOR_RGB2BGR)
+    
+            webcam_faces = app.get(cv2_webcam, max_num=1)
 
-                st.success(f"✅ Welcome: {matched_name}")
+            if not webcam_faces:
+                st.warning("No face detected in the captured image.")
+            else:
+                webcam_emb = torch.tensor(webcam_faces[0].embedding, dtype=torch.float32)
+
+                similarity_scores = []
+                for path in image_paths:
+                    img = cv2.imread(path)
+                    faces = app.get(img, max_num=1)
+                    if not faces:
+                        similarity_scores.append(torch.tensor(-1.0))
+                        continue
+    
+                    face_emb = torch.tensor(faces[0].embedding, dtype=torch.float32)
+                    score = F.cosine_similarity(face_emb, webcam_emb, dim=0)
+                    similarity_scores.append(score)
+    
+                similarity_scores = torch.stack(similarity_scores)
+                match_idx = torch.argmax(similarity_scores)
+                matched_score = similarity_scores[match_idx].item()
+    
+                # Draw bounding box and name
+                (x1, y1, x2, y2) = map(int, webcam_faces[0].bbox)
+                cv2.rectangle(cv2_webcam, (x1, y1), (x2, y2), (0, 255, 0), 2)
+
+                if matched_score >= 0.6:
+                    matched_name = os.path.basename(image_paths[match_idx]).split('.')[0]
+                    cv2.putText(cv2_webcam, matched_name, (x1, y1 - 10), 
+                                cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+    
+                    st.success(f"✅ Welcome: {matched_name}")
 
                 # Send attendance via POST
-                url = "https://aiml2025.glitch.me/attend"
-                data = {'rollno': 15, 'Name': matched_name, 'Class': 7}
-                try:
-                    response = requests.post(url, data=data)
-                    if response.status_code == 200:
-                        st.success("Attendance marked successfully.")
-                    else:
-                        st.warning("Failed to update attendance.")
-                except Exception as e:
-                    st.error(f"Request failed: {e}")
+                    url = "https://aiml2025.glitch.me/attend"
+                    data = {'rollno': 15, 'Name': matched_name, 'Class': 7}
+                    try:
+                        response = requests.post(url, data=data)
+                        if response.status_code == 200:
+                            st.success("Attendance marked successfully.")
+                        else:
+                            st.warning("Failed to update attendance.")
+                    except Exception as e:
+                        st.error(f"Request failed: {e}")
             else:
                 st.error("❌ Match not found. Try again.")