reverting to the parsbert

app.py

@@ -107,16 +107,13 @@ config = BertConfig.from_pretrained(
     id2label=id2label,
     label2id=label2id)
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-path = 'HooshvareLab-bert-fa-base-uncased-3class.bin'
-downloadedModelDict = hf_hub_download(repo_id="zArabi/Persian-Sentiment-Analysis", filename=path)
-loaded_model = SentimentModel(config=config).to(device)
-loaded_model.load_state_dict(torch.load(downloadedModelDict, map_location="cpu"))
+downloadedModelFile = hf_hub_download(repo_id="zArabi/Persian-Sentiment-Analysis", filename="persianModel")
+loaded_model = torch.load(downloadedModelFile,map_location="cpu")
 
 
 tokenizer = BertTokenizer.from_pretrained(modelName)
 max_len=512
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 def predict(text):
   text = cleaning(text)
@@ -147,5 +144,5 @@ gr.Interface(
     predict,
     inputs=gr.Textbox(label="Explore your sentence!",lines=2, placeholder="Type Here..."),
     outputs=gr.outputs.Label(num_top_classes=3),
-    title="How are feeling?!!",
+    title="How are feeling?!",
 ).launch()