Create app.py

app.py

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+from fastapi import FastAPI, Request
+from pydantic import BaseModel
+from typing import List
+from transformers import (
+    AutoTokenizer, AutoModelForTokenClassification,
+    AutoModelForSequenceClassification, pipeline
+)
+import torch
+
+app = FastAPI()
+
+# === Ładowanie modeli ===
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+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)
+
+pl_to_en = pipeline("translation", model="Helsinki-NLP/opus-mt-pl-en", device=0 if device == "cuda" else -1)
+en_to_pl = pipeline("translation", model="gsarti/opus-mt-tc-en-pl", device=0 if device == "cuda" else -1)
+
+# === Dane wejściowe i wyjściowe ===
+class Comment(BaseModel):
+    text: str
+
+class AspectSentiment(BaseModel):
+    aspect: str
+    sentiment: str
+
+class AnalysisResult(BaseModel):
+    results: List[AspectSentiment]
+
+# === Słownik aliasów aspektów EN→PL (taki sam jak wcześniej) ===
+aspect_aliases = {
+    "food": "jedzenie", "service": "obsługa", "price": "cena",
+    "taste": "smak", "waiter": "obsługa", "dish": "danie",
+    "portion": "porcja", "staff": "obsługa", "decor": "wystrój",
+    "menu": "menu", "drink": "napoje", "location": "lokalizacja",
+    "time": "czas oczekiwania", "cleanliness": "czystość", "smell": "zapach",
+    "value": "cena", "experience": "doświadczenie", "recommendation": "ogólna ocena",
+    "children": "dzieci", "family": "rodzina", "pet": "zwierzęta"
+    # dodaj więcej jak chcesz
+}
+
+# === Funkcje pomocnicze ===
+def translate_pl_to_en(texts):
+    return [res["translation_text"] for res in pl_to_en(texts)]
+
+def translate_en_to_pl(texts):
+    return [res["translation_text"] for res in en_to_pl(texts)]
+
+def extract_aspects(text_en):
+    inputs = aspect_tokenizer(text_en, 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 = [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([a.lower() for a in aspects]))
+
+# === Główna funkcja API ===
+@app.post("/analyze", response_model=AnalysisResult)
+def analyze_comment(comment: Comment):
+    text_pl = comment.text
+    text_en = translate_pl_to_en([text_pl])[0]
+    aspects = extract_aspects(text_en)
+
+    result = []
+    for asp in aspects:
+        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 = aspect_aliases.get(asp, translate_en_to_pl([asp])[0].lower())
+        result.append(AspectSentiment(aspect=asp_pl, sentiment=sentiment_label))
+
+    return {"results": result}