Create sentimental.py

sentimental.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# === Загрузка модели ===
+MODEL_NAME = "nlptown/bert-base-multilingual-uncased-sentiment"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+model.eval()
+
+# === FastAPI приложение ===
+app = FastAPI(title="Sentiment Analysis API")
+
+# === Схема запроса ===
+class TextRequest(BaseModel):
+    text: str
+
+# === Логика сентимента ===
+def analyze_sentiment(message: str) -> float:
+    inputs = tokenizer(message, return_tensors="pt", truncation=True)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    probs = torch.softmax(logits, dim=-1)
+    stars = torch.argmax(probs, dim=-1).item() + 1  # от 1 до 5
+    sentiment = (stars - 3) * 2.5  # нормируем -5..+5
+    return round(sentiment, 2)
+
+# === Эндпоинт API ===
+@app.post("/sentiment")
+def sentiment(request: TextRequest):
+    score = analyze_sentiment(request.text)
+    return {"sentiment_score": score}