Update app.py

app.py

@@ -1,16 +1,18 @@
 import gradio as gr
 import torch
 
-from transformers import pipeline, AutoTokenizer, AutoModel
+from transformers import pipeline, AutoTokenizer, AutoModel, LlamaForCausalLM
 
 #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-1-3b-prompt-tuned-sentiment-analysis", trust_remote_code=True)
-model = AutoModel.from_pretrained("furquan/opt-1-3b-prompt-tuned-sentiment-analysis", trust_remote_code=True)
+# tokenizer = AutoTokenizer.from_pretrained("furquan/opt-1-3b-prompt-tuned-sentiment-analysis", trust_remote_code=True)
+# model = AutoModel.from_pretrained("furquan/opt-1-3b-prompt-tuned-sentiment-analysis", trust_remote_code=True)
+model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
 
 
 title = "OPT-1.3B"
-description = "This demo uses meta's opt-1.3b Causal LM as base model that was prompt tuned on the Stanford Sentiment Treebank-5 way dataset to only output the sentiment of a given text."
+description = "This demo uses meta's LLama-2-7b Causal LM as base model that was prompt tuned on the Stanford Sentiment Treebank-5 way dataset to only output the sentiment of a given text."
 article = "<p style='text-align: center'><a href='https://arxiv.org/pdf/2104.08691.pdf' target='_blank'>The Power of Scale for Parameter-Efficient Prompt Tuning</a></p>"
 
 
@@ -20,9 +22,9 @@ 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"]
+        input_ids=tokenized["input_ids"], attention_mask=tokenized["attention_mask"], max_new_tokens=1
         )
-    return f"text: {text} Sentiment: {tokenizer.decode(outputs[0][-3:], skip_special_tokens=True)}"
+    return f"text: {text} Sentiment: {tokenizer.decode(outputs[0], skip_special_tokens=True).split(' ')[-1]}"
 
 iface = gr.Interface(fn=sentiment, inputs="text", outputs="text", title=title,
     description=description, article=article)