Update app.py

app.py

@@ -33,26 +33,31 @@ def prod_function(app, prod_path, webcam_img_pil):
     np_webcam = np.array(webcam_img_pil)
     cv2_webcam = cv2.cvtColor(np_webcam, cv2.COLOR_RGB2BGR)
     
-    webcam_faces = app.get(cv2_webcam, max_num=1)
+    webcam_faces = app.get(cv2_webcam)
     if not webcam_faces:
-        return None, None
+        return []
     
-    webcam_emb = torch.tensor(webcam_faces[0].embedding, dtype=torch.float32)
-
-    similarity_scores = []
-    for path in prod_path:
-        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)
-    return similarity_scores, torch.argmax(similarity_scores)
+    results = []
+    for webcam_face in webcam_faces:
+        webcam_emb = torch.tensor(webcam_face.embedding, dtype=torch.float32)
+        similarity_scores = []
+        for path in prod_path:
+            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)
+        best_match_idx = torch.argmax(similarity_scores)
+        best_score = similarity_scores[best_match_idx].item()
+        results.append((best_score, best_match_idx))
+    
+    return results
 
 # Streamlit tabs
 about_tab, app_tab = st.tabs(["About the app", "Face Recognition"])
@@ -75,28 +80,32 @@ with app_tab:
     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)
+            matches = prod_function(app, image_paths, image_pil)
 
-            if prediction_scores is None:
+            if not matches:
                 st.warning("No face detected in the captured image.")
             else:
-                st.write("Similarity Scores:", prediction_scores)
-                matched_score = prediction_scores[match_idx].item()
-
-                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://nielit-attendance.glitch.me/adds"
-                    data = {'rno': 15, 'sname': matched_name, 'sclass': 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.write("Similarity Scores:", prediction_scores)
+                recognized = False
+                for score, idx in matches:
+                    if matched_score >= 0.6:
+                        matched_name = os.path.basename(image_paths[match_idx]).split('.')[0]
+                        st.success(f"✅ Welcome: {matched_name}")
+                        recognizes=True
+                        
+                        # Send attendance via POST
+                        url = "https://nielit-attendance.glitch.me/adds"
+                        data = {'rno': 15, 'sname': matched_name, 'sclass': 7}
+                        try:
+                            response = requests.post(url, data=data)
+                            if response.status_code == 200:
+                                st.success(f"Attendance marked successfully for {matched_name}.")
+                            else:
+                                st.warning(f"Failed to update attendance for {matched_name}.")
+                        except Exception as e:
+                            st.error(f"Request failed: {e}")
+                    else:
+                        st.warning("❌ Face match not found or too low confidence.")
+                
+                if not recognized:
+                    st.error("❌ No matches above threshold. Try again.")