Update app.py

app.py

@@ -1,10 +1,12 @@
 import gradio as gr
-import pipeline
+import torch
 
-pipe = pipeline("text-generation", model="furquan/opt_2_7_b_prompt_tuned_sentiment_analysis", trust_remote_code=True)
+from transformers import pipeline, AutoTokenizer, AutoModel
 
-# 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)
+#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')
+
+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."
@@ -12,8 +14,14 @@ article = "<p style='text-align: center'><a href='https://arxiv.org/pdf/2104.086
 
 
 def sentiment(text):
-    return pipe(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)}"
 
 iface = gr.Interface(fn=sentiment, inputs="text", outputs="text", title=title,
     description=description, article=article)
 iface.launch()
+