test source eval model

app.py

@@ -3,54 +3,55 @@ import requests
 from bs4 import BeautifulSoup
 from bs4.element import Comment
 from transformers import pipeline
-
-def tag_visible(element):
-    if element.parent.name in ['style', 'script', 'head', 'title', 'meta', '[document]']:
-        return False
-    if isinstance(element, Comment):
-        return False
-    return True
-
-
-def getTalkPage(wiki_page):
-  wiki_page = "https://en.wikipedia.org/"
-  if "wikipedia.org" in wiki_page:
-      response = requests.get(wiki_page)
-      soup = BeautifulSoup(response.content, 'html.parser')
-
-      talk_url = soup.find_all("a", {"rel": "discussion"})
-      if len(talk_url) > 0:
-        talk_url = talk_url[0]["href"]
-        try:
-          talk_response = requests.get("https://en.wikipedia.org" + talk_url)
-          talk_soup = BeautifulSoup(talk_response.content, 'html.parser')
-          talk_texts = talk_soup.findAll(text=True)
-          visible_texts = filter(tag_visible, talk_texts)
-          return u" ".join(t.strip() for t in visible_texts)
-
-        except Exception as error:
-          print('Error occured: {}'.format(error))
-
-classifier = pipeline(model="amitkayal/bert-finetuned-sem_eval-english", top_k=None)
-
-def tone_talkpage(url):
-  talk_content = getTalkPage(url)
-  tone_labels = {'anger': 0, 'anticipation': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'love': 0, 'optimism': 0, 'pessimism': 0, 'sadness': 0, 'surprise': 0, 'trust': 0}
-  if talk_content:
-    breakdown = talk_content.split()
-    n = 200 #because the max amount of sequence length is 512
-    breakdown_lst = [' '.join(breakdown[i:i+n]) for i in range(0,len(talk_content),n)]
-    for ele in breakdown_lst:
-      res = classifier(ele)[0]
-      for tone_res in res:
-        tone_labels[tone_res["label"]] += tone_res["score"]
+from source_eval_model.source_eval_model import check_source_quality
+
+# def tag_visible(element):
+#     if element.parent.name in ['style', 'script', 'head', 'title', 'meta', '[document]']:
+#         return False
+#     if isinstance(element, Comment):
+#         return False
+#     return True
+
+
+# def getTalkPage(wiki_page):
+#   wiki_page = "https://en.wikipedia.org/"
+#   if "wikipedia.org" in wiki_page:
+#       response = requests.get(wiki_page)
+#       soup = BeautifulSoup(response.content, 'html.parser')
+
+#       talk_url = soup.find_all("a", {"rel": "discussion"})
+#       if len(talk_url) > 0:
+#         talk_url = talk_url[0]["href"]
+#         try:
+#           talk_response = requests.get("https://en.wikipedia.org" + talk_url)
+#           talk_soup = BeautifulSoup(talk_response.content, 'html.parser')
+#           talk_texts = talk_soup.findAll(text=True)
+#           visible_texts = filter(tag_visible, talk_texts)
+#           return u" ".join(t.strip() for t in visible_texts)
+
+#         except Exception as error:
+#           print('Error occured: {}'.format(error))
+
+# classifier = pipeline(model="amitkayal/bert-finetuned-sem_eval-english", top_k=None)
+
+# def tone_talkpage(url):
+#   talk_content = getTalkPage(url)
+#   tone_labels = {'anger': 0, 'anticipation': 0, 'disgust': 0, 'fear': 0, 'joy': 0, 'love': 0, 'optimism': 0, 'pessimism': 0, 'sadness': 0, 'surprise': 0, 'trust': 0}
+#   if talk_content:
+#     breakdown = talk_content.split()
+#     n = 200 #because the max amount of sequence length is 512
+#     breakdown_lst = [' '.join(breakdown[i:i+n]) for i in range(0,len(talk_content),n)]
+#     for ele in breakdown_lst:
+#       res = classifier(ele)[0]
+#       for tone_res in res:
+#         tone_labels[tone_res["label"]] += tone_res["score"]
     
-    lst_len = len(breakdown_lst)
-    for key, val in tone_labels.items():
-      tone_labels[key] = val/lst_len
+#     lst_len = len(breakdown_lst)
+#     for key, val in tone_labels.items():
+#       tone_labels[key] = val/lst_len
   
-  return tone_labels
+#   return tone_labels
 
 
-iface = gr.Interface(fn=tone_talkpage, inputs="text", outputs="text")
+iface = gr.Interface(fn=check_source_quality, inputs="text", outputs="text")
 iface.launch()