6d62120443
- User 新增 credit_score 字段（0-200，默认100） - 信誉分影响检测阈值系数：高分降低敏感度，低分提高敏感度 - 发布成功+1分，被拦截-2分；申诉通过+10分，驳回-5分 - 新增手动调整和批量重算信誉分接口 - admin-users 页面显示信誉分进度条，支持编辑调整 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
344 lines
10 KiB
Python
344 lines
10 KiB
Python
from flask import Blueprint, current_app, request
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from flask_jwt_extended import jwt_required
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from app.extensions import db
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from app.ml.naive_bayes_classifier import NaiveBayesSpamClassifier
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from app.models import DetectionConfig, SpamPredictionLog, SpamTrainingSample
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from app.utils.auth import admin_required, current_user
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from app.utils.response import fail, ok
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spam_bp = Blueprint("spam", __name__)
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def _classifier() -> NaiveBayesSpamClassifier:
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return NaiveBayesSpamClassifier(current_app.config["NB_MODEL_PATH"])
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def _active_samples() -> list[dict]:
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rows = SpamTrainingSample.query.filter_by(is_active=True).order_by(SpamTrainingSample.id.asc()).all()
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return [{"text": row.text, "label": row.label} for row in rows]
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def _ensure_ready() -> NaiveBayesSpamClassifier:
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clf = _classifier()
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samples = _active_samples()
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clf.ensure_ready(samples)
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return clf
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def _threshold() -> float:
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row = DetectionConfig.query.order_by(DetectionConfig.id.asc()).first()
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return float(row.spam_threshold) if row else 0.75
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def _adjusted_threshold(user_credit: int = 100) -> float:
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"""根据用户信誉分调整阈值"""
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base_threshold = _threshold()
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# 系数范围：0.85 - 1.15
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credit_factor = 1.0 + (user_credit - 100) * 0.0015
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credit_factor = max(0.85, min(1.15, credit_factor))
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return base_threshold * credit_factor
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@spam_bp.post("/predict")
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@jwt_required()
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def predict_one():
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user = current_user()
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if not user:
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return fail("用户不存在", 404)
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payload = request.get_json(silent=True) or {}
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text = (payload.get("text") or "").strip()
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if len(text) < 2:
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return fail("请输入至少2个字符的待识别文本", 400)
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clf = _ensure_ready()
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result = clf.predict(text)
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threshold = _adjusted_threshold(user.credit_score or 100)
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blocked = float(result["spam_probability"]) >= threshold
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row = SpamPredictionLog(
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user_id=user.id,
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text=result["text"],
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prediction=result["prediction"],
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spam_probability=result["spam_probability"],
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ham_probability=result["ham_probability"],
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confidence=result["confidence"],
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reason_tokens=result["reason_tokens"],
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model_version=result.get("model_version", ""),
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)
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db.session.add(row)
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db.session.commit()
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return ok({**result, "log_id": row.id, "threshold": threshold, "blocked_by_threshold": blocked}, "识别成功")
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@spam_bp.post("/predict/batch")
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@jwt_required()
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def predict_batch():
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user = current_user()
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if not user:
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return fail("用户不存在", 404)
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payload = request.get_json(silent=True) or {}
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items = payload.get("items") or []
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if not isinstance(items, list) or not items:
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return fail("items 必须是非空数组", 400)
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if len(items) > 100:
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return fail("单次最多识别100条", 400)
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clf = _ensure_ready()
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rows = []
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results = []
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threshold = _adjusted_threshold(user.credit_score or 100)
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for text in items:
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content = (text or "").strip()
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if len(content) < 2:
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continue
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result = clf.predict(content)
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result["blocked_by_threshold"] = float(result["spam_probability"]) >= threshold
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rows.append(
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SpamPredictionLog(
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user_id=user.id,
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text=result["text"],
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prediction=result["prediction"],
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spam_probability=result["spam_probability"],
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ham_probability=result["ham_probability"],
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confidence=result["confidence"],
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reason_tokens=result["reason_tokens"],
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model_version=result.get("model_version", ""),
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)
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)
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results.append(result)
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if not rows:
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return fail("没有可识别的有效文本", 400)
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db.session.add_all(rows)
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db.session.commit()
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spam_count = len([item for item in results if item["prediction"] == "spam"])
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blocked_count = len([item for item in results if item["blocked_by_threshold"]])
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ham_count = len(results) - spam_count
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return ok(
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{
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"items": results,
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"summary": {
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"total": len(results),
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"spam_count": spam_count,
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"ham_count": ham_count,
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"blocked_count": blocked_count,
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"spam_ratio": round(spam_count / len(results), 4) if results else 0,
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"blocked_ratio": round(blocked_count / len(results), 4) if results else 0,
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"threshold": threshold,
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},
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},
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"批量识别完成",
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)
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@spam_bp.get("/history")
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@jwt_required()
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def my_history():
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user = current_user()
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if not user:
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return fail("用户不存在", 404)
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page = max(int(request.args.get("page", 1) or 1), 1)
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page_size = min(max(int(request.args.get("page_size", 20) or 20), 1), 100)
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pagination = (
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SpamPredictionLog.query.filter_by(user_id=user.id)
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.order_by(SpamPredictionLog.id.desc())
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.paginate(page=page, per_page=page_size, error_out=False)
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)
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return ok(
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{
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"items": [item.to_dict() for item in pagination.items],
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"total": pagination.total,
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"page": page,
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"page_size": page_size,
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}
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)
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@spam_bp.delete("/history/<int:log_id>")
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@jwt_required()
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def delete_history(log_id: int):
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user = current_user()
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if not user:
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return fail("用户不存在", 404)
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row = SpamPredictionLog.query.filter_by(id=log_id, user_id=user.id).first()
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if not row:
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return fail("记录不存在", 404)
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db.session.delete(row)
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db.session.commit()
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return ok({}, "记录已删除")
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@spam_bp.post("/feedback")
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@jwt_required()
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def save_feedback():
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user = current_user()
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if not user:
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return fail("用户不存在", 404)
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payload = request.get_json(silent=True) or {}
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text = (payload.get("text") or "").strip()
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label = NaiveBayesSpamClassifier.normalize_label(payload.get("label"))
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if len(text) < 2:
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return fail("文本至少2个字符", 400)
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if not label:
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return fail("label 必须是 spam 或 ham", 400)
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row = SpamTrainingSample(text=text, label=label, source="feedback", created_by=user.id, is_active=True)
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db.session.add(row)
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db.session.commit()
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return ok(row.to_dict(), "反馈样本已记录")
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@spam_bp.get("/model/info")
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@jwt_required(optional=True)
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def model_info():
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clf = _classifier()
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clf.load()
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info = clf.model_info()
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info["threshold"] = _threshold()
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return ok(info)
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@spam_bp.post("/train")
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@admin_required
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def train_model():
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clf = _classifier()
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samples = _active_samples()
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metadata = clf.train(samples)
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return ok(metadata, "模型训练完成")
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@spam_bp.get("/samples")
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@admin_required
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def list_samples():
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keyword = (request.args.get("keyword") or "").strip()
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label = (request.args.get("label") or "").strip().lower()
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page = max(int(request.args.get("page", 1) or 1), 1)
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page_size = min(max(int(request.args.get("page_size", 20) or 20), 1), 100)
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query = SpamTrainingSample.query
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if keyword:
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query = query.filter(SpamTrainingSample.text.like(f"%{keyword}%"))
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if label in {"spam", "ham"}:
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query = query.filter(SpamTrainingSample.label == label)
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pagination = query.order_by(SpamTrainingSample.id.desc()).paginate(page=page, per_page=page_size, error_out=False)
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return ok(
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{
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"items": [item.to_dict() for item in pagination.items],
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"total": pagination.total,
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"page": page,
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"page_size": page_size,
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}
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)
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@spam_bp.post("/samples")
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@admin_required
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def create_sample():
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payload = request.get_json(silent=True) or {}
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text = (payload.get("text") or "").strip()
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label = NaiveBayesSpamClassifier.normalize_label(payload.get("label"))
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if len(text) < 2:
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return fail("文本至少2个字符", 400)
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if not label:
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return fail("label 必须是 spam 或 ham", 400)
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user = current_user()
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row = SpamTrainingSample(text=text, label=label, source="import", created_by=user.id if user else None, is_active=True)
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db.session.add(row)
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db.session.commit()
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return ok(row.to_dict(), "样本创建成功")
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@spam_bp.put("/samples/<int:sample_id>")
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@admin_required
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def update_sample(sample_id: int):
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row = SpamTrainingSample.query.get(sample_id)
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if not row:
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return fail("样本不存在", 404)
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payload = request.get_json(silent=True) or {}
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if "text" in payload:
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text = (payload.get("text") or "").strip()
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if len(text) < 2:
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return fail("文本至少2个字符", 400)
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row.text = text
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if "label" in payload:
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label = NaiveBayesSpamClassifier.normalize_label(payload.get("label"))
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if not label:
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return fail("label 必须是 spam 或 ham", 400)
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row.label = label
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if "is_active" in payload:
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row.is_active = bool(payload.get("is_active"))
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db.session.commit()
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return ok(row.to_dict(), "样本更新成功")
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@spam_bp.delete("/samples/<int:sample_id>")
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@admin_required
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def delete_sample(sample_id: int):
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row = SpamTrainingSample.query.get(sample_id)
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if not row:
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return fail("样本不存在", 404)
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db.session.delete(row)
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db.session.commit()
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return ok({}, "样本已删除")
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@spam_bp.post("/samples/import")
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@admin_required
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def import_samples():
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payload = request.get_json(silent=True) or {}
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items = payload.get("items") or []
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if not isinstance(items, list) or not items:
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return fail("items 必须是非空数组", 400)
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user = current_user()
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created = 0
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updated = 0
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for item in items:
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text = (item.get("text") or "").strip()
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label = NaiveBayesSpamClassifier.normalize_label(item.get("label"))
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if len(text) < 2 or not label:
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continue
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row = SpamTrainingSample.query.filter_by(text=text).first()
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if row:
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row.label = label
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row.is_active = bool(item.get("is_active", True))
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row.source = item.get("source") or row.source
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updated += 1
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else:
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row = SpamTrainingSample(
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text=text,
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label=label,
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source=item.get("source") or "import",
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created_by=user.id if user else None,
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is_active=bool(item.get("is_active", True)),
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)
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db.session.add(row)
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created += 1
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db.session.commit()
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return ok({"created": created, "updated": updated}, "样本导入完成")
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