Update app.py

app.py

@@ -65,8 +65,8 @@ def check_by_url(txt_url):
     new_data = {"title": title, "content": normalized_content_with_style}
 #     return new_data
 
-    model = DistilBertForSequenceClassification.from_pretrained(".")
-    tokenizer = DistilBertTokenizer.from_pretrained(".")
+    model = DistilBertForSequenceClassification.from_pretrained("/content/LoadModel")
+    tokenizer = DistilBertTokenizer.from_pretrained("/content/LoadModel")
 
     label_mapping = {1: "SFW", 0: "NSFW"} 
     test_encodings = tokenizer.encode_plus(
@@ -125,7 +125,9 @@ def predict_2(txt_url, normalized_content_with_style):
         confidence_scores_content,
         new_data, 
     ) = (None, None, None, None, None)
+
   predicted_label_text, confidence_score_text = None, None
+
   if txt_url.startswith("http://") or txt_url.startswith("https://"):
     (
             predicted_label_title,
@@ -135,8 +137,8 @@ def predict_2(txt_url, normalized_content_with_style):
             new_data,
       ) = check_by_url(txt_url)
   elif txt_url.startswith(""):
-    model = DistilBertForSequenceClassification.from_pretrained(".")
-    tokenizer = DistilBertTokenizer.from_pretrained(".")
+    model = DistilBertForSequenceClassification.from_pretrained("/content/LoadModel")
+    tokenizer = DistilBertTokenizer.from_pretrained("/content/LoadModel")
 
     test_encodings = tokenizer.encode_plus(
             normalized_content_with_style,
@@ -145,6 +147,7 @@ def predict_2(txt_url, normalized_content_with_style):
             max_length=512,
             return_tensors="pt",
         )
+
     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)
@@ -169,6 +172,7 @@ def predict_2(txt_url, normalized_content_with_style):
             confidence_score_text,
             #new,
             )
+
 def word_by_word(txt_url, normalized_content_with_style):
   if txt_url.startswith("http://") or txt_url.startswith("https://") or txt_url.startswith(""):
     (
@@ -181,8 +185,8 @@ def word_by_word(txt_url, normalized_content_with_style):
             confidence_score_text,
       ) = predict_2(txt_url, normalized_content_with_style)
 
-  model = DistilBertForSequenceClassification.from_pretrained("")
-  tokenizer = DistilBertTokenizer.from_pretrained(".")      
+  model = DistilBertForSequenceClassification.from_pretrained("/content/LoadModel")
+  tokenizer = DistilBertTokenizer.from_pretrained("/content/LoadModel")      
   device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
   model = model.to(device)
   model.eval()
@@ -231,6 +235,8 @@ def word_by_word(txt_url, normalized_content_with_style):
       confidence_score_text,
       new_word,
   )
+
+
 demo = gr.Interface(
     fn=word_by_word,
     inputs=[
@@ -248,4 +254,5 @@ demo = gr.Interface(
         gr.outputs.Textbox(label="word-by-word").style(show_copy_button=True),
     ],
 ) 
-demo.launch()
+
+demo.launch(debug=True, share= True)