Update app.py

app.py

@@ -73,44 +73,60 @@ 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)
-            prediction_scores, match_idx = prod_function(app, image_paths, image_pil)
-
-            if prediction_scores is None:
-                st.warning("No face detected in the captured image.")
+    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)
+
+                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}")
             else:
-                st.write("Similarity Scores:", prediction_scores)
-                matched_score = prediction_scores[match_idx].item()
-                image=image_pil
-                def detect_faces(image):
-                    # Convert image to grayscale
-                    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
-                
-                    # Detect faces
-                    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30))
-                
-                    # Draw rectangles around faces
-                    for (x, y, w, h) in faces:
-                        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
-
-                    return image,
-
-                if matched_score >= 0.6:
-                    matched_name = os.path.basename(image_paths[match_idx]).split('.')[0]
-                    
-                    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}")
-                else:
-                    st.error("❌ Match not found. Try again.")
+                st.error("❌ Match not found. Try again.")
+
+            # Convert back to RGB for displaying in Streamlit
+            final_img = cv2.cvtColor(cv2_webcam, cv2.COLOR_BGR2RGB)
+            st.image(final_img, caption="Detected Face")
+