Update app.py

app.py

@@ -62,9 +62,9 @@ img_file_buffer=st.camera_input("Take a picture")
 if img_file_buffer is not None:
      
     test_image = Image.open(img_file_buffer)
+    image1 = Image.open(img_file_buffer)
     st.image(test_image, use_column_width=True)
-    image = cv2.resize(test_image,(48,48))
-    image = extract_features(image)
+    image = np.asarray(test_image)
     
     
     #########################
@@ -85,16 +85,22 @@ if img_file_buffer is not None:
             y1, x2, y2, x1 = faceLoc
             y1, x2, y2, x1 = y1*4,x2*4,y2*4,x1*4
             cv2.rectangle(image,(x1,y1),(x2,y2),(0,255,0),2)
-            st.write("reshape")
-            pred = model.predict(image)
-            prediction_label = labels[pred.argmax()]
-            st.write("predict")
             cv2.rectangle(image,(x1,y2-35),(x2,y2),(0,255,0),cv2.FILLED)
             cv2.putText(image,name,(x1+6,y2-6),cv2.FONT_HERSHEY_COMPLEX,1,(255, 255, 255),2)
-            st.write("Predicted Output:", prediction_label)
-            cv2.putText(image, '% s' %(prediction_label), (x1+6,y2-6),cv2.FONT_HERSHEY_COMPLEX_SMALL,2, (0,0,255))
-            st.write("success")
-             
+
+            faces=face_cascade.detectMultiScale(image1,1.3,5)
+            try: 
+                for (p,q,r,s) in faces:
+                    image = gray[q:q+s,p:p+r]
+                    cv2.rectangle(image1,(p,q),(p+r,q+s),(255,0,0),2)
+                    image = cv2.resize(image,(48,48))
+                    img = extract_features(image)
+                    pred = model.predict(img)
+                    prediction_label = labels[pred.argmax()]
+                    st.write("Predicted Output:", prediction_label)
+                    # cv2.putText(im,prediction_label)
+                    img = cv2.putText(image1, '% s' %(prediction_label), (p-10, q-10),cv2.FONT_HERSHEY_COMPLEX_SMALL,2, (0,0,255))
+                    st.image(img, use_column_width=True)
         else:
             st.write("fail")