| 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()] | |
| 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() | |