Create app.py

app.py

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+from transformers.pipelines.image_segmentation import Predictions
+import unidecode, re, unicodedata
+from bs4 import BeautifulSoup
+from urllib.request import urlopen
+from urllib.parse import urlparse
+from sklearn.metrics import confusion_matrix, accuracy_score
+import torch.nn.functional as F
+import gradio as gr
+
+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}
+
+  model = DistilBertForSequenceClassification.from_pretrained(Save_model)
+  tokenizer = DistilBertTokenizer.from_pretrained(Save_model)
+
+  test_encodings = tokenizer.encode_plus(
+    title,    
+    truncation=True,
+  padding=True,
+  max_length=512,
+  return_tensors="pt"
+   )
+  model1=[]
+  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+  test_input_ids = test_encodings["input_ids"].to(device)
+  test_attention_mask = test_encodings["attention_mask"].to(device)
+  with torch.no_grad():
+    model1= model.to(device)
+    model1.eval()
+    outputs= model1( test_input_ids, attention_mask=test_attention_mask)
+    logits = outputs.logits
+    predicted_labels = torch.argmax(logits, dim=1)    
+    probabilities = F.softmax(logits, dim=1)
+    confidence_score_title = torch.max(probabilities, dim=1).values.tolist()
+    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()]
+  
+
+  test_encodings = tokenizer.encode_plus(
+      normalized_content_with_style,    
+      truncation=True,
+      padding=True,
+      max_length=512,
+      return_tensors="pt"
+   )
+  model1=[]
+  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+  test_input_ids = test_encodings["input_ids"].to(device)
+  test_attention_mask = test_encodings["attention_mask"].to(device)
+  with torch.no_grad():
+    model1= model.to(device)
+    model1.eval()
+    outputs= model1( test_input_ids, attention_mask=test_attention_mask)
+    logits = outputs.logits
+    predicted_labels = torch.argmax(logits, dim=1)    
+    probabilities = F.softmax(logits, dim=1)
+    confidence_scores_content = torch.max(probabilities, dim=1).values.tolist()
+  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
+
+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
+
+demo = gr.Interface(
+  fn=predict_2, 
+  inputs= [
+      gr.inputs.Textbox(label="Enter URL"),
+  
+      ],
+  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)
+      ],
+)
+demo.launch()