Updated app.py

app.py

@@ -50,57 +50,53 @@ def main(options):
        
         
         if st.checkbox('Take a picture for prediction'):
-            if 'c02' not in st.session_state:
-                st.session_state.c02=False
-            c02_state=st.session_state.c02
-            c02_state=st.sidebar.checkbox('Detect',value=c02_state)
            
             with col1:
                 image, original_image= upload()
-                if original_image is not None and original_image is not None and c02_state: 
+                
                     
-                    # Check if original_image is not None
-                    pass
-                    st.session_state.c02=c02_state
             
     with col2:
-        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:
+        if original_image is not None and original_image is not None and st.button('Detect'): 
+                    
+                   
+            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)
                 
-            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:
-                age_label='Age: '+str(result[0]['age'])
-                cv2.putText(image, age_label, (x ,y+h+30), cv2.FONT_ITALIC,1 ,(255,255,0), 2)
-            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)
+            progress_bar.empty()
+            gray_frame = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+            faces = cascade.detectMultiScale(gray_frame, 1.1, 3)
                 
-            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)
+            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:
+                    age_label='Age: '+str(result[0]['age'])
+                    cv2.putText(image, age_label, (x ,y+h+30), cv2.FONT_ITALIC,1 ,(255,255,0), 2)
+                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)
 
-        st.image(image, channels='BGR')
+            st.image(image, channels='BGR')