Update app.py

app.py

@@ -114,11 +114,11 @@ class AspectSentimentPipeline(Pipeline):
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-aspect_extraction_model = BertForTokenClassification.from_pretrained("./aspect_extraction_model")
-aspect_extraction_tokenizer = BertTokenizerFast.from_pretrained("./aspect_extraction_tokenizer")
+aspect_extraction_model = BertForTokenClassification.from_pretrained("thealper2/aspect_extraction_model")
+aspect_extraction_tokenizer = BertTokenizerFast.from_pretrained("thealper2/aspect_extraction_tokenizer")
 
-aspect_sentiment_model = BertForSequenceClassification.from_pretrained("./aspect_sentiment_model")
-aspect_sentiment_tokenizer = BertTokenizer.from_pretrained("./aspect_sentiment_tokenizer")
+aspect_sentiment_model = BertForSequenceClassification.from_pretrained("thealper2/aspect_sentiment_model")
+aspect_sentiment_tokenizer = BertTokenizer.from_pretrained("thealper2/aspect_sentiment_tokenizer")
 
 pipeline = AspectSentimentPipeline(
     aspect_extraction_model=aspect_extraction_model,
@@ -144,4 +144,4 @@ async def predict(item: Item):
 
 
 if __name__=="__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    uvicorn.run(app, host="0.0.0.0", port=7860)