Create app.py

app.py

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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()