Update app.py

app.py

@@ -1,12 +1,12 @@
 import gradio as gr
-import torch
+
 
 from transformers import pipeline, AutoTokenizer, AutoModel
 
-#pipe = pipeline("text-generation", model="furquan/opt_2_7_b_prompt_tuned_sentiment_analysis", trust_remote_code=True, cache_dir="/local/home/furquanh/myProjects/week12/").to('cuda')
+pipe = pipeline("text-generation", model="furquan/opt_2_7_b_prompt_tuned_sentiment_analysis", trust_remote_code=True)
 
-tokenizer = AutoTokenizer.from_pretrained("furquan/opt_2_7_b_prompt_tuned_sentiment_analysis", trust_remote_code=True)
-model = AutoModel.from_pretrained("furquan/opt_2_7_b_prompt_tuned_sentiment_analysis",trust_remote_code=True)
+# tokenizer = AutoTokenizer.from_pretrained("furquan/opt_2_7_b_prompt_tuned_sentiment_analysis", trust_remote_code=True)
+# model = AutoModel.from_pretrained("furquan/opt_2_7_b_prompt_tuned_sentiment_analysis",trust_remote_code=True)
 
 title = "OPT-2.7B"
 description = "This demo uses meta's opt-2.7b model prompt tuned on the Stanford Sentiment Treebank-5 way dataset to only output the sentiment of a given text."
@@ -14,12 +14,7 @@ article = "<p style='text-align: center'><a href='https://arxiv.org/pdf/2104.086
 
 
 def sentiment(text):
-    tokenized = tokenizer(text, return_tensors='pt')
-    with torch.no_grad():
-        outputs = model.generate(
-        input_ids=tokenized["input_ids"], attention_mask=tokenized["attention_mask"]
-        )
-    return f"text: {text} Sentiment: {tokenizer.decode(outputs[0][-3:], skip_special_tokens=True)}"
+    return pipe(text)
 
 iface = gr.Interface(fn=sentiment, inputs="text", outputs="text", title=title,
     description=description, article=article)