attention_span_fix

web-app/backend/requirements.txt

 absl-py==1.2.0

web-app/backend/server.py

@@ -21,45 +21,50 @@ from controler import captureImage
 
 from keras.models import load_model
 from sklearn.preprocessing import OneHotEncoder
+from datetime import datetime
 
 # Attention classes
 attClass = {0: 'Low Attention', 1: 'Mid Attention', 2: 'High Attention'}
-
-UPLOAD_FOLDER = 'C:/SLIIT/Final Research implementation/Backend/new model 2.0/Main Project code/uploads'
 ALLOWED_EXTENSIONS = set(
     ['mkv', 'webm', 'mp4', 'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'])
-
-x = datetime.datetime.now()
-
 # Initializing flask app
 app = Flask(__name__)
 cors = CORS(app)
 app.config['CORS_HEADERS'] = 'Content-Type'
-app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
 
 # for prediction
 
 
 @app.route('/upload', methods=['POST'])
+@cross_origin()
 def predictAttention():
     print(request)
-    target = os.path.join(UPLOAD_FOLDER, 'test_vids')
-    if not os.path.isdir(target):
-        os.mkdir(target)
+    directory = os.getcwd()
+    dataJson = []
     file = request.files['file']
     testNo = request.form.get('questionIndex')
-    filename = secure_filename("Testing_vid")
-    destination = "/".join([target, filename])
-    file.save(destination)
-    session['uploadFilePath'] = destination
+    filename = request.form.get('filename')
+    if file is None or filename == "":
+            return jsonify({'error: no file'})
+    file.save("./uploads/"+filename)
+    destination =  directory + r"\uploads\att.mp4"
     scoreLst = captureImage(destination, 10)
+    dataDict = {
+                'low_attention': scoreLst[0],
+                'mid_attention': scoreLst[1],
+                'high_attention': scoreLst[2],
+            }
+    dataJson.append(dataDict)
+    dt_string = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
     db.db['attentionlevelScore'].insert_one({
                 "activityName": "Manual_Attention_Test",
                 "questionIndex": testNo,
-                "attentionLevelScore": scoreLst,
+                "attentionLevelScore": dataDict,
+                "createdAt":dt_string,
             })
-    print("Prediction", scoreLst)
-    return scoreLst
+    print("Prediction02", scoreLst)
+    print("Prediction", dataDict)
+    return jsonify(dataJson)
 
 
 @app.route('/parents/', methods=['POST', 'GET'])

web-app/frontend/src/pages/attention_demo/attentionspan.jsx

@@ -70,6 +70,7 @@ const AttentionSpan_screen = () => {
     console.log("testing", blob);
     const data = new FormData();
     data.append("file", blob);
+    data.append("filename", "att.mp4")
     data.append("questionIndex", id);
     await API.post("upload", data, {
       headers: {
@@ -77,7 +78,8 @@ const AttentionSpan_screen = () => {
       },
     })
       .then((res) => {
-        setattention(res.data);
+        console.log(res.data[0])
+        setattention(res.data[0]);
         setisClicked(true);
       })
       .catch((err) => {
@@ -221,15 +223,15 @@ const AttentionSpan_screen = () => {
                           <>
                             <p>
                               Low level attention :{" "}
-                              {attention[0] * 100}
+                              {Math.round(attention.low_attention* 100)/100}
                             </p>
                             <p>
                               Mid level attention :{" "}
-                              {attention[1] * 100}
+                              {attention.mid_attention * 100}
                             </p>
                             <p>
                               High level attention :{" "}
-                              {attention[2] * 100}
+                              {attention.high_attention * 100}
                             </p>
                           </>
                         )}

web-app/package-lock.json

-{
-  "lockfileVersion": 1
-}