WangShen007
20fef2b9f3
One word everywhere: ReplyPier in UI copy, package and image names, the replypier.site domains (app/api/docs/help), REPLYPIER_* settings and the --rp-* design tokens. - public widget API is window.ReplyPier with replypierId / replypierBaseUrl / replypierConfig / replypierToken, loader at webclient/replypier.min.js; both bundles rebuilt from source - NOTICE, llms.txt, CONTRIBUTING, HELP_CENTER_INFRA and the demo runtime config rewritten; the demo config ships no third-party keys - migration adds the ReplyPier managed model value to the AI model enum - local compose project, database, role, volume and Docker network carry the same name
137 lines
4.1 KiB
Python
137 lines
4.1 KiB
Python
"""
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Copyright 2024-2026 ReplyPier
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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from typing import Tuple, List, Optional
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from app.core.logger import get_logger
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logger = get_logger(__name__)
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# Try to import TextBlob, fall back to basic keyword analysis if unavailable
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try:
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from textblob import TextBlob
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TEXTBLOB_AVAILABLE = True
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except ImportError:
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TEXTBLOB_AVAILABLE = False
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logger.warning("TextBlob not installed. Using basic keyword-based sentiment analysis. "
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"Install with: pip install textblob")
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# Basic keyword lists for fallback sentiment analysis
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POSITIVE_KEYWORDS = {
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'thank', 'thanks', 'great', 'awesome', 'excellent', 'good', 'love',
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'perfect', 'amazing', 'wonderful', 'helpful', 'appreciate', 'happy',
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'pleased', 'fantastic', 'brilliant', 'nice', 'best', 'super', 'cool',
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'resolved', 'fixed', 'works', 'working', 'solved'
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}
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NEGATIVE_KEYWORDS = {
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'bad', 'terrible', 'awful', 'horrible', 'worst', 'hate', 'angry',
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'frustrated', 'annoyed', 'disappointed', 'useless', 'broken', 'bug',
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'error', 'fail', 'failed', 'wrong', 'issue', 'problem', 'slow',
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'crash', 'crashed', 'stuck', 'ridiculous', 'unacceptable', 'poor',
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'waste', 'stupid', 'pathetic', 'sucks', 'rubbish'
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}
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def _analyze_with_textblob(text: str) -> Tuple[str, float]:
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"""Analyze sentiment using TextBlob."""
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blob = TextBlob(text)
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polarity = blob.sentiment.polarity # -1.0 to 1.0
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if polarity > 0.1:
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label = "positive"
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elif polarity < -0.1:
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label = "negative"
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else:
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label = "neutral"
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return label, round(polarity, 4)
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def _analyze_with_keywords(text: str) -> Tuple[str, float]:
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"""Fallback keyword-based sentiment analysis."""
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words = set(text.lower().split())
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positive_count = len(words & POSITIVE_KEYWORDS)
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negative_count = len(words & NEGATIVE_KEYWORDS)
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total = positive_count + negative_count
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if total == 0:
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return "neutral", 0.0
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# Simple scoring: range from -1 to 1
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score = (positive_count - negative_count) / total
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if score > 0.1:
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label = "positive"
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elif score < -0.1:
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label = "negative"
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else:
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label = "neutral"
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return label, round(score, 4)
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def analyze_sentiment(text: str) -> Tuple[str, float]:
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"""
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Analyze the sentiment of a text message.
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Returns:
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Tuple of (sentiment_label, sentiment_score)
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- sentiment_label: 'positive', 'neutral', or 'negative'
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- sentiment_score: float from -1.0 (most negative) to 1.0 (most positive)
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"""
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if not text or not text.strip():
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return "neutral", 0.0
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try:
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if TEXTBLOB_AVAILABLE:
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return _analyze_with_textblob(text)
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else:
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return _analyze_with_keywords(text)
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except Exception as e:
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logger.error(f"Error analyzing sentiment: {str(e)}")
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return "neutral", 0.0
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def compute_session_sentiment(
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sentiment_scores: List[float],
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sentiment_labels: List[str]
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) -> Tuple[Optional[str], Optional[float]]:
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"""
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Compute the overall sentiment for a session from individual message sentiments.
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Args:
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sentiment_scores: List of sentiment scores from customer messages
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sentiment_labels: List of sentiment labels from customer messages
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Returns:
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Tuple of (overall_label, overall_score) or (None, None) if no data
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"""
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if not sentiment_scores:
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return None, None
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avg_score = sum(sentiment_scores) / len(sentiment_scores)
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if avg_score > 0.1:
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label = "positive"
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elif avg_score < -0.1:
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label = "negative"
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else:
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label = "neutral"
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return label, round(avg_score, 4)
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