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cf4ac41 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | import pytest
from app.services.scoring.scoring_engine import ScoringEngine
from app.services.nlp.schemas import (
SpeakerDetectionResult, ParticipantProfile, SentimentAnalysisResponse,
MessageSentiment, ToxicityAnalysisResponse, MessageToxicity,
TonalityAnalysisResponse, MessageTonality, TopicAnalysisResponse,
TopicSegment, ParticipantSentiment, ParticipantToxicity, ParticipantTonality,
SentimentTrendPoint
)
from app.services.preprocessing.schemas import PreprocessingResult
@pytest.fixture
def mock_analysis_data():
# Setup mock data for a 2-person chat
speakers = SpeakerDetectionResult(
participants=[
ParticipantProfile(name="Alice", messages_sent=10, words_total=100, dominance_score=0.6),
ParticipantProfile(name="Bob", messages_sent=5, words_total=30, dominance_score=0.4)
]
)
sentiment = SentimentAnalysisResponse(
messages=[
MessageSentiment(message_id=i, sender="Alice", score=0.5, label="positive", confidence=0.9)
for i in range(10)
] + [
MessageSentiment(message_id=i+10, sender="Bob", score=-0.2, label="negative", confidence=0.8)
for i in range(5)
],
participants=[
ParticipantSentiment(name="Alice", overall_score=0.5, pos_count=10, neg_count=0, neutral_count=0),
ParticipantSentiment(name="Bob", overall_score=-0.2, pos_count=0, neg_count=5, neutral_count=0)
],
timeline=[
SentimentTrendPoint(label="10:00", average_score=0.5, smoothed_score=0.5, volume=10),
SentimentTrendPoint(label="10:05", average_score=-0.2, smoothed_score=-0.2, volume=5)
],
summary_metrics={
"overall_sentiment": "positive",
"positivity_rate": 0.67,
"negativity_rate": 0.33,
"volatility": 0.1
}
)
toxicity = ToxicityAnalysisResponse(
messages=[
MessageToxicity(message_id=i, score=0.1, label="low", confidence=0.9)
for i in range(15)
],
participants=[
ParticipantToxicity(name="Alice", intensity_index=0.1, outburst_count=0, max_heat=0.1),
ParticipantToxicity(name="Bob", intensity_index=0.1, outburst_count=0, max_heat=0.1)
],
global_intensity=0.1
)
tonality = TonalityAnalysisResponse(
messages=[
MessageTonality(message_id=i, dryness_score=0.2, passive_aggression_score=0.1, confidence=0.8)
for i in range(15)
],
participants=[
ParticipantTonality(name="Alice", avg_dryness=0.2, avg_passive_aggression=0.1),
ParticipantTonality(name="Bob", avg_dryness=0.5, avg_passive_aggression=0.4)
]
)
topics = TopicAnalysisResponse(
segments=[
TopicSegment(id=1, start_id=0, end_id=9, label="Greetings", confidence=0.9, message_count=10, sentiment_avg=0.5),
TopicSegment(id=2, start_id=10, end_id=14, label="The Problem", confidence=0.8, message_count=5, sentiment_avg=-0.2)
],
clusters=[],
shifts=[10]
)
preprocessed = PreprocessingResult(messages=[]) # Minimal for this test
return preprocessed, speakers, sentiment, toxicity, tonality, topics
def test_scoring_engine_flow(mock_analysis_data):
engine = ScoringEngine()
result = engine.analyze(*mock_analysis_data)
assert result.overall_mood == "Wholesome & Energetic"
assert len(result.participants) == 2
alice = next(p for p in result.participants if p.name == "Alice")
bob = next(p for p in result.participants if p.name == "Bob")
# Alice should be more dominant
assert alice.dominance.value > bob.dominance.value
assert "The Driver" in alice.badges
# Bob should be drier (lower effort in the current mapping where dry=effort)
assert bob.effort_level.value > alice.effort_level.value
# Check segments
assert len(result.segments) == 2
assert result.segments[0].topic_label == "Greetings"
assert result.segments[1].topic_label == "The Problem"
# Check standout cards
assert len(result.standout_cards) > 0
print(f"\nResulting Summary: {result.overall_summary}")
for card in result.standout_cards:
print(f"Card: {card.title} -> {card.recipient}: {card.description}")
if __name__ == "__main__":
# If run directly, we can see the output
import sys
from pydantic import BaseModel
# Minimal mock objects if we don't have pytest
# (Simplified for direct run demonstration)
pass
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