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from transformers import AutoTokenizer, AutoModelForSequenceClassification
# import torch
# device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
# device
download = False
save_model_locally= False
if download:
tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/feel-it-italian-sentiment", cache_dir="data/")
model = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/feel-it-italian-sentiment", cache_dir="data/")
model.eval()
tokenizer_emo = AutoTokenizer.from_pretrained("MilaNLProc/feel-it-italian-emotion", cache_dir="data/")
model_emo = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/feel-it-italian-emotion", cache_dir="data/")
model_emo.eval()
if save_model_locally:
model.save_pretrained('./local_models/sentiment_ITA')
tokenizer.save_pretrained('./local_models/sentiment_ITA')
model_emo.save_pretrained('./local_models/emotion_ITA')
tokenizer_emo.save_pretrained('./local_models/emotion_ITA')
else:
tokenizer = AutoTokenizer.from_pretrained("./local_models/sentiment_ITA/")
model = AutoModelForSequenceClassification.from_pretrained("./local_models/sentiment_ITA/", num_labels=2)
model.eval()
tokenizer_emo = AutoTokenizer.from_pretrained("./local_models/emotion_ITA/")
model_emo = AutoModelForSequenceClassification.from_pretrained("./local_models/emotion_ITA/", num_labels=4)
model_emo.eval()
#%%
from transformers import pipeline
import re
generator = pipeline(task="text-classification", model=model, tokenizer=tokenizer, return_all_scores =True)
generator_emo = pipeline(task="text-classification", model=model_emo, tokenizer=tokenizer_emo, return_all_scores =True)
def sentiment_emoji(input_abs):
if(input_abs ==""):
return "🤷♂️"
res = generator(input_abs)[0]
res = {res[x]["label"]: res[x]["score"] for x in range(len(res))}
res["🙂 positive"] = res.pop("positive")
res["🙁 negative"] = res.pop("negative")
return res
def emotion_emoji(input_abs):
if(input_abs ==""):
return "🤷♂️"
res = generator_emo(input_abs)[0]
res = {res[x]["label"]: res[x]["score"] for x in range(len(res))}
res["😃 joy"] = res.pop("joy")
res["😡 anger"] = res.pop("anger")
res["😨 fear"] = res.pop("fear")
res["😟 sadness"] = res.pop("sadness")
return res
#%%
import gradio as gr
demo = gr.Blocks()
with demo:
gr.Markdown("# Analisi sentimento/emozioni del testo italiano")
with gr.Row():
with gr.Column():
text_input = gr.Textbox(placeholder="Scrivi qui")
button_1 = gr.Button("Invia")
with gr.Column():
label_sem = gr.Label()
label_emo = gr.Label()
# gr.Interface(fn=emotion_emoji, inputs=text_input, outputs="label")
button_1.click(sentiment_emoji, inputs=text_input, outputs=label_sem, api_name="sentiment")
button_1.click(emotion_emoji, inputs=text_input, outputs=label_emo, api_name="emotion")
demo.launch(share=True)
print("Running is terminated") |