app.py

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Imran1/sentimen_analysis_yelp",from_pt=True)

model = AutoModelForSequenceClassification.from_pretrained("Imran1/sentimen_analysis_yelp",from_pt=True)

from transformers import pipeline
Data= pipeline("text-classification", model=model, tokenizer=tokenizer,top_k=5)



Label=[]
Score=[]
def sentiment(text):
    data = Data(text)[0]
    for i in range (5):
        L=data[i]["label"]
        S=data[i]["score"]
        Label.append(L)
        Score.append(S)
    return dict(zip(Label,Score))

import gradio as gr
exmp=["the food is not good.","oh I really love this food "]

gr.Interface(fn=sentiment, inputs="text", outputs="label", examples=exmp,title= "Yelp reviews").launch()