Update app.py

app.py

@@ -54,77 +54,77 @@ def findEncodings(Images):
 
 encodeListknown = findEncodings(Images)
 st.write('Encoding Complete')
-while True:
-    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 = np.asarray(test_image)
-        
-        img = np.asarray(image1)
-        img = cv2.resize(img,(0,0),None,0.25,0.25)
-    
-        #image gray
-        img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        faces = face_cascade.detectMultiScale(
-            image=img_gray, scaleFactor=1.3, minNeighbors=5)
-        try:
-            for (x, y, w, h) in faces:
-                cv2.rectangle(img=img, pt1=(x, y), pt2=(
-                    x + w, y + h), color=(255, 0, 0), thickness=2)
-                roi_gray = img_gray[y:y + h, x:x + w]
-                roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)
-                if np.sum([roi_gray]) != 0:
-                    roi = roi_gray.astype('float') / 255.0
-                    roi = img_to_array(roi)
-                    roi = np.expand_dims(roi, axis=0)
-                    prediction = classifier.predict(roi)[0]
-                    maxindex = int(np.argmax(prediction))
-                    finalout = emotion_dict[maxindex]
-                    output = str(finalout)
-                    st.write(output)
-                label_position = (x, y)
-                img = cv2.putText(img, output, label_position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-                st.image(img, use_column_width=True)
-        except:
-            st.write("face is not clear")
+
+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 = np.asarray(test_image)
     
-        #########################
-        imgS = cv2.resize(image,(0,0),None,0.25,0.25)
-        imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
-        facesCurFrame = face_recognition.face_locations(imgS)
-        encodesCurFrame = face_recognition.face_encodings(imgS,facesCurFrame)
-        for encodeFace,faceLoc in zip(encodesCurFrame,facesCurFrame):
-            matches = face_recognition.compare_faces(encodeListknown,encodeFace)
-            faceDis = face_recognition.face_distance(encodeListknown,encodeFace)
-            matchIndex = np.argmin(faceDis)
-            if matches[matchIndex]:
-                name = classnames[matchIndex]
-                st.write(name)
-                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)
-                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)
-                ##############
-                if name:
-                    if output=='happy':
-                        url = "https://kiwi-whispering-plier.glitch.me/update" 
-                        
-                        data = {
-                            'name': name,
-                        }
-                        response = requests.get(url, params=data)
-                         
-                        if response.status_code == 200 : 
-                            st.write(" data updated on : https://kiwi-whispering-plier.glitch.me" )
-                            st.image(image)
-                        else :
-                            st.write("data not updated ")
+    img = np.asarray(image1)
+    img = cv2.resize(img,(0,0),None,0.25,0.25)
+
+    #image gray
+    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(
+        image=img_gray, scaleFactor=1.3, minNeighbors=5)
+    try:
+        for (x, y, w, h) in faces:
+            cv2.rectangle(img=img, pt1=(x, y), pt2=(
+                x + w, y + h), color=(255, 0, 0), thickness=2)
+            roi_gray = img_gray[y:y + h, x:x + w]
+            roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)
+            if np.sum([roi_gray]) != 0:
+                roi = roi_gray.astype('float') / 255.0
+                roi = img_to_array(roi)
+                roi = np.expand_dims(roi, axis=0)
+                prediction = classifier.predict(roi)[0]
+                maxindex = int(np.argmax(prediction))
+                finalout = emotion_dict[maxindex]
+                output = str(finalout)
+                st.write(output)
+            label_position = (x, y)
+            img = cv2.putText(img, output, label_position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+            st.image(img, use_column_width=True)
+    except:
+        st.write("face is not clear")
+
+    #########################
+    imgS = cv2.resize(image,(0,0),None,0.25,0.25)
+    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
+    facesCurFrame = face_recognition.face_locations(imgS)
+    encodesCurFrame = face_recognition.face_encodings(imgS,facesCurFrame)
+    for encodeFace,faceLoc in zip(encodesCurFrame,facesCurFrame):
+        matches = face_recognition.compare_faces(encodeListknown,encodeFace)
+        faceDis = face_recognition.face_distance(encodeListknown,encodeFace)
+        matchIndex = np.argmin(faceDis)
+        if matches[matchIndex]:
+            name = classnames[matchIndex]
+            st.write(name)
+            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)
+            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)
+            ##############
+            if name:
+                if output=='happy':
+                    url = "https://kiwi-whispering-plier.glitch.me/update" 
+                    
+                    data = {
+                        'name': name,
+                    }
+                    response = requests.get(url, params=data)
                      
-                        ##############################
+                    if response.status_code == 200 : 
+                        st.write(" data updated on : https://kiwi-whispering-plier.glitch.me" )
+                        st.image(image)
+                    else :
+                        st.write("data not updated ")
+                 
+                    ##############################
                        
     
                     else: