feat(rating): 结束服务、星级评价与管理员回看统计

结束会话后可提交 1-5 星与文字评价，重复提交更新原记录；管理端回看接待人与评分。

backend/app/services/sentiment.py

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+"""
+Copyright 2024-2026 ReplyPier
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+from typing import Tuple, List, Optional
+from app.core.logger import get_logger
+
+logger = get_logger(__name__)
+
+# Try to import TextBlob, fall back to basic keyword analysis if unavailable
+try:
+    from textblob import TextBlob
+    TEXTBLOB_AVAILABLE = True
+except ImportError:
+    TEXTBLOB_AVAILABLE = False
+    logger.warning("TextBlob not installed. Using basic keyword-based sentiment analysis. "
+                   "Install with: pip install textblob")
+
+
+# Basic keyword lists for fallback sentiment analysis
+POSITIVE_KEYWORDS = {
+    'thank', 'thanks', 'great', 'awesome', 'excellent', 'good', 'love',
+    'perfect', 'amazing', 'wonderful', 'helpful', 'appreciate', 'happy',
+    'pleased', 'fantastic', 'brilliant', 'nice', 'best', 'super', 'cool',
+    'resolved', 'fixed', 'works', 'working', 'solved'
+}
+
+NEGATIVE_KEYWORDS = {
+    'bad', 'terrible', 'awful', 'horrible', 'worst', 'hate', 'angry',
+    'frustrated', 'annoyed', 'disappointed', 'useless', 'broken', 'bug',
+    'error', 'fail', 'failed', 'wrong', 'issue', 'problem', 'slow',
+    'crash', 'crashed', 'stuck', 'ridiculous', 'unacceptable', 'poor',
+    'waste', 'stupid', 'pathetic', 'sucks', 'rubbish'
+}
+
+
+def _analyze_with_textblob(text: str) -> Tuple[str, float]:
+    """Analyze sentiment using TextBlob."""
+    blob = TextBlob(text)
+    polarity = blob.sentiment.polarity  # -1.0 to 1.0
+
+    if polarity > 0.1:
+        label = "positive"
+    elif polarity < -0.1:
+        label = "negative"
+    else:
+        label = "neutral"
+
+    return label, round(polarity, 4)
+
+
+def _analyze_with_keywords(text: str) -> Tuple[str, float]:
+    """Fallback keyword-based sentiment analysis."""
+    words = set(text.lower().split())
+
+    positive_count = len(words & POSITIVE_KEYWORDS)
+    negative_count = len(words & NEGATIVE_KEYWORDS)
+
+    total = positive_count + negative_count
+    if total == 0:
+        return "neutral", 0.0
+
+    # Simple scoring: range from -1 to 1
+    score = (positive_count - negative_count) / total
+
+    if score > 0.1:
+        label = "positive"
+    elif score < -0.1:
+        label = "negative"
+    else:
+        label = "neutral"
+
+    return label, round(score, 4)
+
+
+def analyze_sentiment(text: str) -> Tuple[str, float]:
+    """
+    Analyze the sentiment of a text message.
+
+    Returns:
+        Tuple of (sentiment_label, sentiment_score)
+        - sentiment_label: 'positive', 'neutral', or 'negative'
+        - sentiment_score: float from -1.0 (most negative) to 1.0 (most positive)
+    """
+    if not text or not text.strip():
+        return "neutral", 0.0
+
+    try:
+        if TEXTBLOB_AVAILABLE:
+            return _analyze_with_textblob(text)
+        else:
+            return _analyze_with_keywords(text)
+    except Exception as e:
+        logger.error(f"Error analyzing sentiment: {str(e)}")
+        return "neutral", 0.0
+
+
+def compute_session_sentiment(
+    sentiment_scores: List[float],
+    sentiment_labels: List[str]
+) -> Tuple[Optional[str], Optional[float]]:
+    """
+    Compute the overall sentiment for a session from individual message sentiments.
+
+    Args:
+        sentiment_scores: List of sentiment scores from customer messages
+        sentiment_labels: List of sentiment labels from customer messages
+
+    Returns:
+        Tuple of (overall_label, overall_score) or (None, None) if no data
+    """
+    if not sentiment_scores:
+        return None, None
+
+    avg_score = sum(sentiment_scores) / len(sentiment_scores)
+
+    if avg_score > 0.1:
+        label = "positive"
+    elif avg_score < -0.1:
+        label = "negative"
+    else:
+        label = "neutral"
+
+    return label, round(avg_score, 4)