Update app.py

app.py

@@ -20,7 +20,6 @@ import numpy as np
 from PIL import Image
 from glob import glob
 import requests
-from urllib import request
 
 # Set the device (GPU or CPU)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -55,21 +54,21 @@ def prod_function(app, prod_path, webcam_path):
 
     webcam_emb = app.get(cv2_webcam, max_num=1)
     if not webcam_emb:
-        return torch.tensor([])
+        return torch.tensor([]), None, None
 
-    webcam_emb = torch.from_numpy(np.array(webcam_emb[0].embedding))
+    webcam_emb_tensor = torch.from_numpy(np.array(webcam_emb[0].embedding))
 
     similarity_score = []
     for path in prod_path:
         img = cv2.imread(path)
         face_embedding = app.get(img, max_num=1)
         if not face_embedding:
-            similarity_score.append(torch.tensor(-1.0))  # No face detected
+            similarity_score.append(torch.tensor(-1.0))
             continue
-        face_embedding = torch.from_numpy(np.array(face_embedding[0].embedding))
-        similarity_score.append(F.cosine_similarity(face_embedding, webcam_emb, dim=0))
+        face_tensor = torch.from_numpy(np.array(face_embedding[0].embedding))
+        similarity_score.append(F.cosine_similarity(face_tensor, webcam_emb_tensor, dim=0))
 
-    return torch.stack(similarity_score)
+    return torch.stack(similarity_score), cv2_webcam, webcam_emb[0].bbox.astype(int)
 
 # Streamlit Tabs
 about_tab, app_tab = st.tabs(["About the app", "Face Recognition"])
@@ -83,7 +82,8 @@ with about_tab:
         
         This is a facial recognition-based attendance system enhanced with image classification using a Vision Transformer (ViT).  
         Just capture your image to mark your attendance automatically!
-        """)
+        """
+    )
 
 # Application Tab
 with app_tab:
@@ -97,37 +97,31 @@ with app_tab:
             image.save(webcam_path)
 
             # --- Face Recognition ---
-            prediction = prod_function(app, image_paths, webcam_path)
+            prediction, cv2_webcam, face_bbox = prod_function(app, image_paths, webcam_path)
 
-            if len(prediction) == 0:
+            if prediction is None or len(prediction) == 0:
                 st.warning("No face detected in the image.")
             else:
                 match_idx = torch.argmax(prediction)
-                st.subheader("Face Recognition Results")
-                # st.write("Similarity Scores:", prediction.tolist())
-    if prediction[match_idx] >= 0.6:
-        name = image_paths[match_idx].split('/')[-1].split('.')[0].upper()
-
-    # Extract face location from InsightFace result
-    face_info = app.get(cv2_webcam, max_num=1)[0]
-    faceLoc = face_info.bbox.astype(int)  # returns [x1, y1, x2, y2]
-    
-    x1, y1, x2, y2 = faceLoc
-    cv2.rectangle(cv2_webcam, (x1, y1), (x2, y2), (0, 255, 0), 2)
-    cv2.rectangle(cv2_webcam, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
-    cv2.putText(cv2_webcam, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
 
-    # Convert BGR to RGB before displaying in Streamlit
-    st.image(cv2.cvtColor(cv2_webcam, cv2.COLOR_BGR2RGB), caption=f"Matched: {name}", use_column_width=True)
+                if prediction[match_idx] >= 0.6:
+                    name = image_paths[match_idx].split('/')[-1].split('.')[0].upper()
 
+                    # Draw bounding box and name
+                    x1, y1, x2, y2 = face_bbox
+                    cv2.rectangle(cv2_webcam, (x1, y1), (x2, y2), (0, 255, 0), 2)
+                    cv2.rectangle(cv2_webcam, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
+                    cv2.putText(cv2_webcam, name, (x1 + 6, y2 - 6),
+                                cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
 
-                if prediction[match_idx] >= 0.6:
-                    pname = image_paths[match_idx].split('/')[-1].split('.')[0]
-                    st.success(f'✅ Welcome, {pname}!')
+                    # Show result
+                    st.image(cv2.cvtColor(cv2_webcam, cv2.COLOR_BGR2RGB),
+                             caption=f"✅ Matched: {name}", use_column_width=True)
+                    st.success(f"Welcome, {name}!")
 
-                    # --- Send data to external API ---
+                    # --- Send Attendance ---
                     url = "https://skattendancesystem25.glitch.me/adds"
-                    data = {'rno': 15, 'sname': pname, 'sclass': 7}
+                    data = {'rno': 15, 'sname': name, 'sclass': 7}
                     try:
                         response = requests.post(url, data=data)
                         if response.status_code == 200:
@@ -138,20 +132,3 @@ with app_tab:
                         st.error(f"Request failed: {e}")
                 else:
                     st.error("❌ Match not found. Try again.")
-                #     try:
-                        
-                #         response = requests.post(url, data=data)
-                #         if response.status_code == 200:
-                #             st.success("✔️ Attendance data updated on remote server")
-                #         else:
-                #             st.warning("⚠️ Failed to update data")
-                #     except Exception as e:
-                #         st.error(f"❌ Error updating server: {e}")
-                # else:
-                #     st.warning("❌ Face match not found.")
-
-            # --- Image Classification ---
-            # st.subheader("Image Classification (Vision Transformer)")
-            # vit_results = vit_pipe(image)
-            # for result in vit_results:
-            #     st.write(f"🔹 Label: **{result['label']}**, Score: {result['score']:.4f}")