Updated app.py

app.py

@@ -8,22 +8,8 @@ import time
 
 st.set_page_config(layout="wide")
 
-
-
-
-
 cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
 
-import tempfile
-import os
-
-weights_paths = {
-    'age': '/home/appuser/.deepface/weights/age_model_weights.h5',
-    'gender': '/home/appuser/.deepface/weights/gender_model_weights.h5',
-    'race': '/home/appuser/.deepface/weights/race_model_single_batch.h5',
-    'emotion': '/home/appuser/.deepface/weights/facial_expression_model_weights.h5'
-}
-
 def upload():
     image=None
     initial_image = st.camera_input('Take a picture')
@@ -39,46 +25,34 @@ def upload():
     
 
 def main(options):
-    col1,col2=st.columns(2)
-    image=None
-    original_image=None
-    
     
+   
     
     
-    with col1:
-       
+   
+    if st.checkbox('Take a picture for prediction'):
         
-        if st.checkbox('Take a picture for prediction'):
-           
-           
-            image, original_image= upload()
-                
-                    
-            
-    with col2:
-        if original_image is not None and original_image is not None and st.button('Detect'): 
-                    
-                   
+        image, original_image= upload()
+        if original_image is not None and original_image is not None and st.button('Prediction'):  # Check if original_image is not None
             st.warning('Wait for few seconds!!')
             progress_bar = st.progress(0.0)
             status_text = st.empty()
-                
+            
             result = DeepFace.analyze(image,detector_backend=options,actions=['age','gender','emotion','race'])
-                
+            
             for i in range(100):
                 progress_bar.progress((i + 1) / 100)
                 status_text.text(f"Processing {i+1}%")
                 time.sleep(0.01)
-                
+            
             progress_bar.empty()
             gray_frame = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
             faces = cascade.detectMultiScale(gray_frame, 1.1, 3)
-                
+            
 
             for x,y,w,h in faces:
-                    
-                    
+                
+                
                 cv2.rectangle(image, (x, y), (x+w, y+h), (4, 29, 255), 2, cv2.LINE_4)
                 user_selected_items = list(result[0].keys())
                 if 'age' in user_selected_items:
@@ -87,15 +61,15 @@ def main(options):
                 if 'dominant_gender' in user_selected_items:
                     gender_label='Gender: '+str(result[0]['dominant_gender'])
                     cv2.putText(image, gender_label, (x, y+h+70), cv2.FONT_ITALIC,1, (0,255,255), 2)
-                    
+                
                 if 'dominant_emotion' in user_selected_items:
                     emotion_label='Emotion: '+str(result[0]['dominant_emotion']).title()
                     cv2.putText(image, emotion_label, (x, y+h+110), cv2.FONT_ITALIC,1 ,(255,0,255), 2)
 
                 if 'dominant_race' in user_selected_items:
                     emotion_label='Race: '+str(result[0]['dominant_race']).title()
-                    cv2.putText(image, emotion_label, (x, y+h+150), cv2.FONT_ITALIC,1 ,(51,102,0), 2)
-
+                    cv2.putText(image, emotion_label, (x, y+h+150), cv2.FONT_ITALIC,1 ,(0,0,102), 2)
+            
             st.image(image, channels='BGR')