app.py

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
sa= pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)

def adjust(x):
  if x<0:
    return 2*x+1
  return 2*x-1

def sa2(s):
  res= sa(s)
  return [adjust(-1*r['score']) if r['label']=='negative' else adjust(r['score']) for r in res ]
 
  
def get_examples():
  #return [e for e in  open("examplesTR.csv").readlines()]
  return ["Bu filmi beğenmedim\n bu filmi beğendim\n ceketin çok güzel\n bugün ne yesek"]

import pandas as pd

import matplotlib.pyplot as plt
def grfunc(comments):
  df=pd.DataFrame()
  c2=[s.strip() for s in comments.split("\n") if len(s.split())>2]
  df["scores"]= sa2(c2)
  df.plot(kind='hist')
  return plt.gcf()
 
import gradio as gr

iface = gr.Interface(
  fn=grfunc, 
  inputs=gr.inputs.Textbox(placeholder="put your sentences line by line", lines=5), 
  outputs="plot",
  examples=get_examples())
iface.launch()