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import gradio as gr
from transformers import (
AutoTokenizer, AutoModelForTokenClassification,
AutoModelForSequenceClassification,
MarianMTModel, MarianTokenizer
)
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
# === Tokenizery i modele ABSA ===
aspect_tokenizer = AutoTokenizer.from_pretrained("EfektMotyla/bert-aspect-ner")
aspect_model = AutoModelForTokenClassification.from_pretrained("EfektMotyla/bert-aspect-ner").to(device)
sentiment_tokenizer = AutoTokenizer.from_pretrained("EfektMotyla/absa-roberta")
sentiment_model = AutoModelForSequenceClassification.from_pretrained("EfektMotyla/absa-roberta").to(device)
en_to_pl_tokenizer = MarianTokenizer.from_pretrained("gsarti/opus-mt-tc-en-pl")
en_to_pl_model = MarianMTModel.from_pretrained("gsarti/opus-mt-tc-en-pl").to(device)
pl_to_en_tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-pl-en")
pl_to_en_model = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-pl-en").to(device)
def translate(texts, tokenizer, model):
inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True).to(device)
translated = model.generate(**inputs)
return tokenizer.batch_decode(translated, skip_special_tokens=True)
def translate_pl_to_en(texts): return translate(texts, pl_to_en_tokenizer, pl_to_en_model)
def translate_en_to_pl(texts): return translate(texts, en_to_pl_tokenizer, en_to_pl_model)
def extract_aspects(text):
inputs = aspect_tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
with torch.no_grad():
outputs = aspect_model(**inputs)
preds = torch.argmax(outputs.logits, dim=2)[0].cpu().numpy()
tokens = aspect_tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
labels = [aspect_model.config.id2label[p] for p in preds]
aspects = []
current_tokens = []
for token, label in zip(tokens, labels):
if label == "B-ASP":
if current_tokens:
aspects.append(aspect_tokenizer.convert_tokens_to_string(current_tokens).strip())
current_tokens = []
current_tokens = [token]
elif label == "I-ASP" and current_tokens:
current_tokens.append(token)
else:
if current_tokens:
aspects.append(aspect_tokenizer.convert_tokens_to_string(current_tokens).strip())
current_tokens = []
if current_tokens:
aspects.append(aspect_tokenizer.convert_tokens_to_string(current_tokens).strip())
return list(set(aspects))
def analyze(text_pl):
try:
text_en = translate_pl_to_en([text_pl])[0]
aspects_en = extract_aspects(text_en)
if not aspects_en:
return "Nie wykryto żadnych aspektów."
results = []
for asp in aspects_en:
input_text = f"{text_en} [SEP] {asp}"
inputs = sentiment_tokenizer(input_text, return_tensors="pt", truncation=True, padding=True).to(device)
with torch.no_grad():
logits = sentiment_model(**inputs).logits
predicted_class_id = int(logits.argmax().cpu())
sentiment_label = {0: "negatywny", 1: "neutralny", 2: "pozytywny", 3: "konfliktowy"}[predicted_class_id]
asp_pl = translate_en_to_pl([asp])[0]
results.append(f"{asp_pl.capitalize()} -> **{sentiment_label}**")
return "\n".join(results)
except Exception as e:
return f"Błąd: {str(e)}"
# === Gradio UI ===
demo = gr.Interface(
fn=analyze,
inputs=gr.Textbox(label="Komentarz po polsku", placeholder="Np. Pizza była pyszna, ale kelner był nieuprzejmy."),
outputs=gr.Markdown(label="Wyniki analizy"),
title="ABSA – Analiza komentarzy restauracyjnych",
description="Wykrywa aspekty i przypisuje im sentymenty (pozytywny / negatywny / neutralny / konfliktowy)."
)
demo.launch()
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