Update app.py

app.py

@@ -9,58 +9,9 @@ import torch.nn.functional as F
 import gradio as gr
 import torch
 
-def check_by_url(txt_url):
-  #txt_url = "https://www.c-sharpcorner.com/article/how-to-add-multimedia-content-with-html/default.txt" 
-  parsed_url = urlparse(txt_url)
-  url = f"{parsed_url.scheme}://{parsed_url.netloc}{parsed_url.path.rsplit('/', 1)[0]}/"
-  print(url)
-
-  new_data =[]
-  page = urlopen(url=url).read().decode("utf-8")
-  soup = BeautifulSoup(page, 'html.parser')
-  title = soup.find('title').get_text()
-
-  css_class_to_remove = "dp-highlighter"  # Replace with the CSS class you want to remove
-    #Find <div> tags with the specified CSS class and remove their content
-  div_tags = soup.find_all(['code', 'pre'])
-  for div_tag in div_tags:
-    div_tag.clear()
-
-  div_tags = soup.find_all('div', class_=css_class_to_remove)
-  for div_tag in div_tags:
-    div_tag.clear()
-
-  # Fetch content of remaining tags
-  content_with_style = ""
-  p_tags_with_style = soup.find_all('p', style=True)
-  for p_tag in p_tags_with_style:
-      p_content = re.sub(r'\n', '', p_tag.get_text())
-      content_with_style += p_content
-
-  # Fetch content of <p> tags without style
-  content_without_style = ""
-  p_tags_without_style = soup.find_all('p', style=False)
-  for p_tag in p_tags_without_style:
-      p_content = re.sub(r'\n', '', p_tag.get_text())
-      content_without_style += p_content
-
-  # Replace Unicode characters in the content and remove duplicates
-  normalized_content_with_style = re.sub(r'\s+', ' ', content_with_style)  # Remove extra spaces
-  normalized_content_with_style = normalized_content_with_style.replace('\r', '')  # Replace '\r' characters
-  normalized_content_with_style = unicodedata.normalize('NFKD', normalized_content_with_style)
-  normalized_content_with_style = unidecode.unidecode(normalized_content_with_style)
-
-  normalized_content_without_style = re.sub(r'\s+', ' ', content_without_style)  # Remove extra spaces
-  normalized_content_without_style = normalized_content_without_style.replace('\r', '')  # Replace '\r' characters
-  normalized_content_without_style = unicodedata.normalize('NFKD', normalized_content_without_style)
-  normalized_content_without_style = unidecode.unidecode(normalized_content_without_style)
-  
-  normalized_content_with_style += normalized_content_without_style
-  new_data = {"title": title, "content": normalized_content_with_style}
-
+def check_by_title(title):
   model = DistilBertForSequenceClassification.from_pretrained(".")
   tokenizer = DistilBertTokenizer.from_pretrained(".")
-
   test_encodings = tokenizer.encode_plus(
     title,    
     truncation=True,
@@ -83,8 +34,9 @@ def check_by_url(txt_url):
     predicted_labels = torch.argmax(outputs.logits, dim=1)
   label_mapping = {1: "SFW", 0: "NSFW"} # 1:True 0:false  
   predicted_label_title = label_mapping[predicted_labels.item()]
-  
+  return predicted_label_title, confidence_score_title
 
+def check_by_content(normalized_content_with_style):
   test_encodings = tokenizer.encode_plus(
       normalized_content_with_style,    
       truncation=True,
@@ -107,25 +59,26 @@ def check_by_url(txt_url):
   label_mapping = {1: "SFW", 0: "NSFW"} # 1:True 0:false  
   predicted_label_content = label_mapping[predicted_labels.item()] 
 
-  return predicted_label_title, confidence_score_title, predicted_label_content, confidence_scores_content, new_data
+  return predicted_label_content, confidence_scores_content
 
-def predict_2( url):
-    predicted_label_title, confidence_score_title,predicted_label_content, confidence_scores_content, new_data = check_by_url(url)
-    return predicted_label_title, confidence_score_title, predicted_label_content, confidence_scores_content, new_data
+def predict_2(title, normalized_content_with_style):
+    predicted_label_title, confidence_score_title = check_by_title(title)
+    predicted_label_content, confidence_scores_content = check_by_content(normalized_content_with_style)
+    return predicted_label_title, confidence_score_title, predicted_label_content, confidence_scores_content
 
 demo = gr.Interface(
   fn=predict_2, 
-  inputs= [
-      gr.inputs.Textbox(label="Enter URL"),
-  
+  inputs=[
+      gr.inputs.Textbox(label="Title", placeholder="Enter title"),
+      gr.inputs.Textbox(label="Content", placeholder="enter Content"),
       ],
-  outputs= [
-      
+  outputs= [      
       gr.outputs.Textbox(label="Title_prediction"),
       gr.outputs.Textbox(label="Title_confidence_score"), 
       gr.outputs.Textbox(label="Content_prediction"),
       gr.outputs.Textbox(label="content_confidence_score"),
-      gr.outputs.Textbox(label="new_data").style(show_copy_button=True)
+      #gr.outputs.Textbox(label="Description").style(show_copy_button=True)
       ],
+  
 )
 demo.launch()