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@@ -22,6 +22,14 @@ WORKDIR $HOME/app
  
 # Copy the current directory contents into the container at $HOME/app setting the owner to the user
 COPY --chown=user . $HOME/app
+
+# Copy additional directories from your local machine into the container
+COPY --chown=user ./aspect_extraction_model $HOME/app/aspect_extraction_model
+COPY --chown=user ./aspect_extraction_tokenizer $HOME/app/aspect_extraction_tokenizer
  
+COPY --chown=user ./aspect_sentiment_model $HOME/app/aspect_sentiment_model
+COPY --chown=user ./aspect_sentiment_tokenizer $HOME/app/aspect_sentiment_tokenizer
+
+
 # Start the FastAPI app on port 7860, the default port expected by Spaces
 CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

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("thealper2/aspect-extraction-model")
-aspect_extraction_tokenizer = BertTokenizerFast.from_pretrained("thealper2/aspect-extraction-model")
+aspect_extraction_model = BertForTokenClassification.from_pretrained("./aspect_extraction_model")
+aspect_extraction_tokenizer = BertTokenizerFast.from_pretrained("./aspect_extraction_tokenizer")
 
-aspect_sentiment_model = BertForSequenceClassification.from_pretrained("thealper2/aspect-sentiment-model")
-aspect_sentiment_tokenizer = BertTokenizer.from_pretrained("thealper2/aspect-sentiment-model")
+aspect_sentiment_model = BertForSequenceClassification.from_pretrained("./aspect_sentiment_model")
+aspect_sentiment_tokenizer = BertTokenizer.from_pretrained("./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=8000)