app.py

import os
os.system("pip install -q flash_attn==2.7.4.post1 transformers==4.49.0 accelerate>=0.26.0")
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import re
import os
import torch

hf_token = os.environ.get('hf_token')

model_path=  'microsoft/Phi-4-mini-instruct'

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    #device_map="auto",
    #torch_dtype="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_path)

def make_prompt(sentence):
    prompt = ("""
    Given the below sentence(s) can you extract the sentiment and keywords for each sentence:
    
    """ + sentence
    )

    return prompt

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):

    pipe = pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
    )
 
    generation_args = {
        "max_new_tokens": 500,
        "return_full_text": False,
        "temperature": 0.0,
        "do_sample": False,
    }
    
    question_and_background = make_prompt(review_text)
    messages = [
        {"role": "system", "content": "You are a helpful AI assistant who helps to extract sentiments and keywords from given sentences."},
        {"role": "user", "content": question_and_background}
    ]
    output = pipe(messages, **generation_args)
    print(output)
    return output[0]['generated_text']
    
demo = gr.Blocks()
sentiment_extr = gr.Interface(
    fn=get_sentiment_from_llm,
    inputs=gr.Textbox(label="Text input", type="text"),
    outputs=gr.Textbox(label="Sentiments", type="text"),
    title="Sentiment analysis and keywords extraction",
    description="""
    Enter one or two sentences in the Text Input and click "Submit" to see the sentiments extracted. <br>
    For longer input, please allow 2-3 minutes as the model is running on small CPU. <br>
    Base model: Phi-4-mini-instruct from Microsoft. <br>
    Prompt tuned by Thuyen Truong for sentiment extraction.
    """
)
with demo:
    gr.TabbedInterface([sentiment_extr], ["Sentiment text analysis"])
demo.launch()