app.py

from fastapi import FastAPI
from pydantic import BaseModel
from typing import List
import torch
from transformers import (
    AutoTokenizer,
    AutoModelForSequenceClassification,
    XLMRobertaForSequenceClassification,
)

app = FastAPI(title="Unified NLP API")

# =====================
# Agreement (MNLI)
# =====================
MNLI_MODEL = "facebook/bart-base-mnli"
mnli_tokenizer = None
mnli_model = None

def load_mnli():
    global mnli_tokenizer, mnli_model
    if mnli_model is None:
        mnli_tokenizer = AutoTokenizer.from_pretrained(MNLI_MODEL)
        mnli_model = AutoModelForSequenceClassification.from_pretrained(MNLI_MODEL)
        mnli_model.eval()

def check_agreement(msg1: str, msg2: str) -> float:
    load_mnli()
    inputs = mnli_tokenizer(msg1, msg2, return_tensors="pt", truncation=True)
    with torch.no_grad():
        logits = mnli_model(**inputs).logits
    probs = torch.softmax(logits, dim=-1)[0]
    return round((probs[2] - probs[0]).item(), 2)  # entailment - contradiction


# =====================
# Sentiment
# =====================
SENTIMENT_MODEL = "nlptown/bert-base-multilingual-uncased-sentiment"
sent_tokenizer = None
sent_model = None

def load_sentiment():
    global sent_tokenizer, sent_model
    if sent_model is None:
        sent_tokenizer = AutoTokenizer.from_pretrained(SENTIMENT_MODEL)
        sent_model = AutoModelForSequenceClassification.from_pretrained(SENTIMENT_MODEL)
        sent_model.eval()

def analyze_sentiment(text: str) -> float:
    load_sentiment()
    inputs = sent_tokenizer(text, return_tensors="pt", truncation=True)
    with torch.no_grad():
        logits = sent_model(**inputs).logits
    probs = torch.softmax(logits, dim=-1)
    stars = torch.argmax(probs, dim=-1).item() + 1
    return round((stars - 3) * 2.5, 2)  # -5 .. +5


# =====================
# Multilabel classifier
# =====================
CLASSIFIER_MODEL = "xlm-roberta-base"

CATEGORIES = [
    "politique", "woke", "racism", "crime",
    "police_abuse", "corruption", "hate_speech", "activism"
]

clf_tokenizer = None
clf_model = None

def load_classifier():
    global clf_tokenizer, clf_model
    if clf_model is None:
        clf_tokenizer = AutoTokenizer.from_pretrained(CLASSIFIER_MODEL)
        clf_model = XLMRobertaForSequenceClassification.from_pretrained(
            CLASSIFIER_MODEL,
            num_labels=len(CATEGORIES)
        )
        clf_model.eval()

def classify_message(text: str) -> List[str]:
    load_classifier()
    inputs = clf_tokenizer(text, return_tensors="pt", truncation=True)
    with torch.no_grad():
        logits = clf_model(**inputs).logits
    probs = torch.sigmoid(logits)[0]
    labels = [CATEGORIES[i] for i, p in enumerate(probs) if p > 0.5]
    return labels or ["neutral"]


# =====================
# API schemas
# =====================
class AgreementRequest(BaseModel):
    msg1: str
    msg2: str

class TextRequest(BaseModel):
    text: str


# =====================
# Endpoints
# =====================
@app.post("/agreement")
def agreement(req: AgreementRequest):
    return {"agreement_score": check_agreement(req.msg1, req.msg2)}

@app.post("/sentiment")
def sentiment(req: TextRequest):
    return {"sentiment_score": analyze_sentiment(req.text)}

@app.post("/classify")
def classify(req: TextRequest):
    return {"categories": classify_message(req.text)}

@app.get("/")
def root():
    return {
        "status": "ok",
        "endpoints": {
            "POST /sentiment": "sentiment analysis",
            "POST /agreement": "text agreement",
            "POST /classify": "multilabel classification",
            "GET /docs": "swagger UI"
        }
    }