app.py

import pandas as pd
dataset = pd.read_csv('a1_RestaurantReviews_HistoricDump.tsv', delimiter = '\t', quoting = 3)
import pickle
tokenizer=pickle.load(open("tokenizer.pkl","rb"))
from keras.utils import pad_sequences
model=pickle.load(open("model.pkl","rb"))
def calc(user_input):
  user_input=[user_input]
  seq=tokenizer.texts_to_sequences(user_input)
  inp=pad_sequences(seq,padding='post',maxlen=51)
  score=(model.predict(inp))[0][0]
  if score>0.6:
    return("positive")
  elif score <0.4:
    return("negative")
  else:
    return("neutral")
import gradio as gr
gr.Interface(fn=calc,inputs=gr.inputs.Textbox(placeholder="Input the feeedback to be reviewed"),outputs="textbox").launch();