small fixes + non infinte loading on error

Dockerfile

@@ -1,6 +1,8 @@
 FROM python
 
-COPY ./ /app
+COPY ./requirements.txt /app/requirements.txt
 RUN pip install -r /app/requirements.txt
 
+COPY ./ /app
+
 CMD python /app/run.py

application/static/main.js

@@ -17,7 +17,11 @@ function loadData() {
             renderRecipeList(data)
                 
         },
-        loadData()
+        function (error, data) {
+            console.log(error)
+            rl.innerHTML = "<p>Es gab einen Fehler, bitte suchen Sie erneut.</p>"
+                
+        }
     );
 }
 

getImages.py

@@ -42,7 +42,7 @@ def getImages():
             if defaultImg == recipe.img:
                 url = recipe.url
                 string1 = '//*[@id="recipe-image-carousel"]/div/div[1]/div[10]/div/a/amp-img'
-
+//*[@id="recipe-image-carousel"]/div/div[1]/div/div/a/amp-img
                 driver.get(url)
                 element = WebDriverWait(driver, 30).until(
                             ec.presence_of_element_located((
@@ -63,11 +63,11 @@ def getImages():
                 counter +=1
                 print(counter/maxC)
                 sleep(5)
-        except:
+        except Exception as e:
             errorUrls.append(recipe.url)
             print(recipe.url)
-            print("error")
-
+            print(e)
+    print(errorUrls)
         
 
 getImages()

migrate.py

@@ -6,16 +6,21 @@ from nltk.corpus import stopwords
 from application.db import Session, Recipe, Ingredient, Trunk
 
 def stemWord(word):
-    arr = []
-    stopset = set(stopwords.words('german'))
-    stopset |= set("(),")
-    snowball = nltk.SnowballStemmer(language='german')
-    for token in nltk.word_tokenize(word): 
-        if token in stopset or len(token) < 4:
-            continue
-        stemmed = snowball.stem(token)
-        arr.append(stemmed)
-    return arr
+    try:
+        arr = []
+        stopset = set(stopwords.words('german'))
+        stopset |= set("(),")
+        snowball = nltk.SnowballStemmer(language='german')
+        for token in nltk.word_tokenize(word): 
+            if token in stopset or len(token) < 4:
+                continue
+            stemmed = snowball.stem(token)
+            arr.append(stemmed)
+        if len(arr) == 0:
+            arr.append("")
+        return arr
+    except:
+        return [""]
 
 def migrate(path):
     recs = ""