Update app.py

app.py

@@ -8,7 +8,7 @@ import torch
 
 hf_token = os.environ.get('hf_token')
 
-model_path='microsoft/Phi-4-mini-instruct'
+model_path= 'microsoft/Phi-4-mini-instruct-onnx' #'microsoft/Phi-4-mini-instruct'
 
 model = AutoModelForCausalLM.from_pretrained(
     model_path,
@@ -18,21 +18,6 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 
-Examples_to_teach_model="""
-Text: I hate apples
-Sentiment analysis:
-Sentiments: Negative
-PPrint Key words: hate, aples
-Text: I enjoy watching it
-Sentiment analysis:
-Sentiments: Positive
-PPrint Key words: enjoy
-Text: I'm tired of this long process
-Sentiment analysis:
-Sentiments: Negative
-PPrint Key words: tired, long process
-"""
-
 def make_prompt(sentence):
     prompt = ("""
     Given the below sentence(s) can you extract the sentiment and keywords for each sentence:
@@ -46,24 +31,12 @@ def split_conj(text):
     return re.sub('(but|yet|although|however|nevertheless|on the other hand|still|though)', "|", text).split('|')
 
 def get_sentiment_from_llm(review_text):
-    #sentences = review_text.lower().split(".")
-    """
-    segments=[]
-    for sen in sentences:
-        segments=segments+split_conj(sen)
-    ls_outputs=[]
-
-    segments= [x for x in segments if len(x)>=5]
-    print(segments)
-    """
 
     pipe = pipeline(
         "text-generation",
         model=model,
         tokenizer=tokenizer,
     )
-
-    
  
     generation_args = {
         "max_new_tokens": 500,
@@ -79,7 +52,7 @@ def get_sentiment_from_llm(review_text):
     ]
     output = pipe(messages, **generation_args)
     print(output)
-    return output
+    return output[0]['generated_text']
     
 demo = gr.Blocks()
 sentiment_extr = gr.Interface(