app.py

from fastapi import FastAPI
from pydantic import BaseModel
from typing import List
import joblib
import pandas as pd
import numpy as np
from sentence_transformers import SentenceTransformer
from transformers import pipeline
import torch

app = FastAPI(title="Comment Validator API")

# =====================================
# 🔹 Chargement des modèles
# =====================================

if torch.cuda.is_available():
    device = "cuda"
elif torch.backends.mps.is_available():
    device = "mps"  # pour ton Mac local
else:
    device = "cpu"
print(f"🧠 Using device: {device}")

print("Loading model embedding")
text_model = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2", device=device)
print("Loading model classifier")
clf = joblib.load("models/classifier.joblib")
print("Loading model encoder")
encoder = joblib.load("models/encoder.joblib")
print("Loading model sentiment-analysis")
sentiment_analyzer = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment", device=device)
print("Loading model toxicity")
toxicity_analyzer = pipeline("text-classification", model="unitary/toxic-bert", return_all_scores=True, device=device)

def analyze_comment(comment: str, category: str, country: str) -> dict:
    reasons = []

    # --- Analyse du sentiment ---
    try:
        sentiment = sentiment_analyzer(comment[:512])[0]
        label = sentiment["label"]
        score = sentiment["score"]
    except Exception:
        label, score = "unknown", 0.0

    if "1" in label or "2" in label:
        sentiment_score = -1
        reasons.append("Le ton semble négatif ou insatisfait.")
    elif "4" in label or "5" in label:
        sentiment_score = 1
    else:
        sentiment_score = 0

    # --- Encodage du texte ---
    X_text = text_model.encode([comment])

    # --- Encodage catégorie/pays ---
    df_cat = pd.DataFrame([[category, country]], columns=["category", "country"])
    try:
        X_cat = encoder.transform(df_cat)
    except ValueError:
        reasons.append(f"Catégorie ou pays inconnus : {category}, {country}")
        n_features = sum(len(cats) for cats in encoder.categories_)
        X_cat = np.zeros((1, n_features))

    # --- Concaténation ---
    X = np.concatenate([X_text, X_cat], axis=1)

    # --- Prédiction validité ---
    proba = clf.predict_proba(X)[0][1]
    prediction = proba >= 0.5

    if len(comment.split()) < 3:
        reasons.append("Le commentaire est trop court.")
    if sentiment_score < 0:
        reasons.append("Le ton global est négatif.")
    if proba < 0.4:
        reasons.append("Le modèle estime une faible probabilité de validité.")

    # --- Analyse toxicité ---
    try:
        tox_scores = toxicity_analyzer(comment[:512])[0]  # tronquer pour sécurité
        tags = {f"tag_{item['label']}": round(item['score'], 3) for item in tox_scores}
    except Exception:
        tags = {f"tag_{label}": 0.0 for label in ["toxicity","severe_toxicity","obscene","identity_attack","insult","threat"]}

    # --- Résultat final ---
    result = {
        "is_valid": bool(prediction),
        "confidence": round(float(proba), 3),
        "sentiment": label,
        "sentiment_score": round(float(score), 3),
        "reasons": "; ".join(reasons) if reasons else "Aucune anomalie détectée."
    }

    result.update(tags)
    return result


# =====================================
# 🔸 Modèles de requête/réponse
# =====================================

class CommentRequest(BaseModel):
    comment: str
    category: str
    country: str

class BatchRequest(BaseModel):
    items: List[CommentRequest]

# =====================================
# 🔹 Routes
# =====================================

@app.post("/predict")
def predict(item: CommentRequest):
    """Analyse un seul commentaire"""
    result = analyze_comment(item.comment, item.category, item.country)
    return result


@app.post("/batch_predict")
def batch_predict(request: BatchRequest):
    """Analyse plusieurs commentaires à la fois"""
    results = []
    for item in request.items:
        results.append(analyze_comment(item.comment, item.category, item.country))
    return {"results": results}