feat: Add Streamlit dashboard with Blueprint compliance (v2.1.0)
Dashboard Features: - 8 navigation sections: Overview, Outcomes, Poor CX, FCR, Churn, Agent, Call Explorer, Export - Beyond Brand Identity styling (colors #6D84E3, Outfit font) - RCA Sankey diagram (Driver → Outcome → Churn Risk flow) - Correlation heatmaps (driver co-occurrence, driver-outcome) - Outcome Deep Dive (root causes, correlation, duration analysis) - Export functionality (Excel, HTML, JSON) Blueprint Compliance: - FCR: 4 categories (Primera Llamada/Rellamada × Sin/Con Riesgo de Fuga) - Churn: Binary view (Sin Riesgo de Fuga / En Riesgo de Fuga) - Agent: Talento Para Replicar / Oportunidades de Mejora - Fixed FCR rate calculation (only FIRST_CALL counts as success) Technical: - Streamlit + Plotly for interactive visualizations - Light theme configuration (.streamlit/config.toml) - Fixed Plotly colorbar titlefont deprecation Documentation: - Updated PROJECT_CONTEXT.md, TODO.md, CHANGELOG.md - Added 4 new technical decisions (TD-014 to TD-017) - Created TROUBLESHOOTING.md with 10 common issues Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
sujucu70
47
config/schemas/__init__.py
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47
config/schemas/__init__.py
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"""
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CXInsights - Schema Definitions
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Export all schema models from the current version.
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"""
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from config.schemas.call_analysis_v1 import (
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SCHEMA_VERSION,
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BatchManifest,
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CallAnalysis,
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CallOutcome,
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CompressedTranscript,
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DataSource,
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Event,
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EventType,
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EvidenceSpan,
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FailureReason,
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ObservedFeatures,
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ProcessingStatus,
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RCALabel,
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SpeakerTurn,
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Traceability,
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Transcript,
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TranscriptMetadata,
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TurnMetrics,
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)
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__all__ = [
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"SCHEMA_VERSION",
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"DataSource",
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"ProcessingStatus",
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"FailureReason",
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"EventType",
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"CallOutcome",
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"Traceability",
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"SpeakerTurn",
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"TranscriptMetadata",
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"Transcript",
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"Event",
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"TurnMetrics",
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"ObservedFeatures",
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"EvidenceSpan",
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"RCALabel",
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"CallAnalysis",
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"CompressedTranscript",
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"BatchManifest",
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]
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416
config/schemas/call_analysis_v1.py
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416
config/schemas/call_analysis_v1.py
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"""
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CXInsights - Call Analysis Schema v1.0
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Data contracts for the call analysis pipeline.
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All outputs MUST include: schema_version, prompt_version, model_id
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This schema defines:
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- OBSERVED: Facts extracted from STT (deterministic)
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- INFERRED: Conclusions from LLM (requires evidence)
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"""
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from datetime import datetime
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from enum import Enum
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from typing import Literal
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from pydantic import BaseModel, Field, field_validator
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# ============================================
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# SCHEMA VERSION
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# ============================================
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SCHEMA_VERSION = "1.0.0"
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# ============================================
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# ENUMS
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# ============================================
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class DataSource(str, Enum):
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"""Source of data - critical for audit trail"""
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OBSERVED = "observed" # From STT, deterministic
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INFERRED = "inferred" # From LLM, requires evidence
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class ProcessingStatus(str, Enum):
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"""Processing status for each call"""
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SUCCESS = "success"
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PARTIAL = "partial"
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FAILED = "failed"
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class FailureReason(str, Enum):
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"""Reasons for processing failure"""
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LOW_AUDIO_QUALITY = "LOW_AUDIO_QUALITY"
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TRANSCRIPTION_FAILED = "TRANSCRIPTION_FAILED"
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LLM_PARSE_ERROR = "LLM_PARSE_ERROR"
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NO_EVIDENCE_FOUND = "NO_EVIDENCE_FOUND"
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SCHEMA_VALIDATION_ERROR = "SCHEMA_VALIDATION_ERROR"
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TIMEOUT = "TIMEOUT"
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RATE_LIMITED = "RATE_LIMITED"
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UNKNOWN = "UNKNOWN"
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class EventType(str, Enum):
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"""Observable events (detected without LLM)"""
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HOLD_START = "HOLD_START"
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HOLD_END = "HOLD_END"
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TRANSFER = "TRANSFER"
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ESCALATION = "ESCALATION"
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SILENCE = "SILENCE"
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INTERRUPTION = "INTERRUPTION"
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class CallOutcome(str, Enum):
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"""Final outcome of the call"""
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SALE_COMPLETED = "SALE_COMPLETED"
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SALE_LOST = "SALE_LOST"
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CANCELLATION_SAVED = "CANCELLATION_SAVED"
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CANCELLATION_COMPLETED = "CANCELLATION_COMPLETED"
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INQUIRY_RESOLVED = "INQUIRY_RESOLVED"
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INQUIRY_UNRESOLVED = "INQUIRY_UNRESOLVED"
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COMPLAINT_RESOLVED = "COMPLAINT_RESOLVED"
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COMPLAINT_UNRESOLVED = "COMPLAINT_UNRESOLVED"
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TRANSFER_OUT = "TRANSFER_OUT"
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CALLBACK_SCHEDULED = "CALLBACK_SCHEDULED"
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UNKNOWN = "UNKNOWN"
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# ============================================
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# TRACEABILITY (Required on all outputs)
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# ============================================
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class Traceability(BaseModel):
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"""Traceability metadata - REQUIRED on all analysis outputs"""
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schema_version: str = Field(
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default=SCHEMA_VERSION,
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description="Version of this schema",
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)
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prompt_version: str = Field(
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description="Version of the prompt used for inference",
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)
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model_id: str = Field(
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description="Model identifier (e.g., gpt-4o-mini-2024-07-18)",
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)
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created_at: datetime = Field(
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default_factory=datetime.utcnow,
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description="Timestamp of analysis",
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)
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# ============================================
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# TRANSCRIPT MODELS (OBSERVED)
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# ============================================
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class SpeakerTurn(BaseModel):
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"""Single speaker turn in transcript"""
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speaker: str = Field(description="Speaker identifier (A, B, agent, customer)")
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text: str = Field(description="Transcribed text")
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start_time: float = Field(description="Start time in seconds")
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end_time: float = Field(description="End time in seconds")
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confidence: float | None = Field(
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default=None,
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ge=0.0,
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le=1.0,
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description="STT confidence score",
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)
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class TranscriptMetadata(BaseModel):
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"""Metadata about the transcript"""
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audio_duration_sec: float = Field(description="Total audio duration in seconds")
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language: str = Field(default="es", description="Detected language")
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provider: str = Field(description="STT provider (assemblyai, whisper, etc.)")
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job_id: str | None = Field(default=None, description="Provider job ID")
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created_at: datetime = Field(
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default_factory=datetime.utcnow,
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description="Timestamp of transcription",
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)
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class Transcript(BaseModel):
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"""Complete transcript with speaker diarization - OBSERVED data"""
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call_id: str = Field(description="Unique call identifier")
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turns: list[SpeakerTurn] = Field(description="List of speaker turns")
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metadata: TranscriptMetadata = Field(description="Transcript metadata")
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full_text: str | None = Field(
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default=None,
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description="Full concatenated text (optional)",
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)
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# ============================================
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# EVENT MODELS (OBSERVED)
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# ============================================
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class Event(BaseModel):
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"""Observable event detected without LLM - OBSERVED data"""
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event_type: EventType = Field(description="Type of event")
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start_time: float = Field(description="Event start time in seconds")
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end_time: float | None = Field(
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default=None,
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description="Event end time in seconds (if applicable)",
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)
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duration_sec: float | None = Field(
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default=None,
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description="Event duration in seconds",
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)
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metadata: dict | None = Field(
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default=None,
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description="Additional event-specific data",
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)
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source: Literal["observed"] = Field(
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default="observed",
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description="Events are always observed, not inferred",
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)
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# ============================================
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# TURN METRICS (OBSERVED)
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# ============================================
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class TurnMetrics(BaseModel):
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"""Metrics computed from transcript - OBSERVED data"""
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total_turns: int = Field(description="Total number of turns")
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agent_turns: int = Field(description="Number of agent turns")
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customer_turns: int = Field(description="Number of customer turns")
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agent_talk_ratio: float = Field(
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ge=0.0,
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le=1.0,
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description="Ratio of agent talk time",
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)
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customer_talk_ratio: float = Field(
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ge=0.0,
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le=1.0,
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description="Ratio of customer talk time",
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)
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silence_ratio: float = Field(
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ge=0.0,
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le=1.0,
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description="Ratio of silence time",
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)
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interruption_count: int = Field(
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default=0,
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description="Number of detected interruptions",
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)
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avg_turn_duration_sec: float = Field(description="Average turn duration")
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source: Literal["observed"] = Field(
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default="observed",
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description="Metrics are always observed, not inferred",
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)
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# ============================================
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# OBSERVED FEATURES (Aggregated)
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# ============================================
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class ObservedFeatures(BaseModel):
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"""All observed features for a call - deterministic, no LLM"""
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call_id: str = Field(description="Unique call identifier")
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events: list[Event] = Field(
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default_factory=list,
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description="Detected events",
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)
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turn_metrics: TurnMetrics = Field(description="Turn-based metrics")
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hold_count: int = Field(default=0, description="Number of hold events")
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total_hold_duration_sec: float = Field(
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default=0.0,
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description="Total hold duration",
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)
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transfer_count: int = Field(default=0, description="Number of transfers")
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silence_count: int = Field(
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default=0,
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description="Number of significant silences",
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)
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created_at: datetime = Field(default_factory=datetime.utcnow)
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# ============================================
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# EVIDENCE MODELS (For INFERRED data)
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# ============================================
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class EvidenceSpan(BaseModel):
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"""Evidence from transcript supporting an inference"""
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text: str = Field(
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max_length=500,
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description="Quoted text from transcript",
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)
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start_time: float = Field(description="Start time in seconds")
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end_time: float = Field(description="End time in seconds")
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speaker: str | None = Field(
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default=None,
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description="Speaker of this evidence",
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)
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@field_validator("text")
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@classmethod
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def text_not_empty(cls, v: str) -> str:
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if not v.strip():
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raise ValueError("Evidence text cannot be empty")
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return v.strip()
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# ============================================
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# RCA LABELS (INFERRED)
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# ============================================
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class RCALabel(BaseModel):
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"""Root Cause Analysis label - INFERRED data (requires evidence)"""
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driver_code: str = Field(
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description="Driver code from taxonomy (e.g., PRICE_TOO_HIGH)",
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)
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confidence: float = Field(
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ge=0.0,
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le=1.0,
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description="Confidence score (0-1)",
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)
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evidence_spans: list[EvidenceSpan] = Field(
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min_length=1,
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description="Supporting evidence (minimum 1 required)",
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)
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reasoning: str | None = Field(
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default=None,
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max_length=500,
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description="Brief reasoning for this classification",
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)
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proposed_label: str | None = Field(
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default=None,
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description="For OTHER_EMERGENT: proposed new label",
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)
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source: Literal["inferred"] = Field(
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default="inferred",
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description="RCA labels are always inferred",
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)
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@field_validator("evidence_spans")
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@classmethod
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def at_least_one_evidence(cls, v: list[EvidenceSpan]) -> list[EvidenceSpan]:
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if len(v) < 1:
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raise ValueError("At least one evidence span is required")
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return v
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# ============================================
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# CALL ANALYSIS (Complete Output)
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# ============================================
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class CallAnalysis(BaseModel):
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"""
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Complete analysis output for a single call.
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Combines:
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- OBSERVED: Features, events, metrics (from STT)
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- INFERRED: RCA labels, outcome (from LLM)
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MUST include traceability for audit.
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"""
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# === Identifiers ===
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call_id: str = Field(description="Unique call identifier")
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batch_id: str = Field(description="Batch identifier")
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# === Processing Status ===
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status: ProcessingStatus = Field(description="Processing status")
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failure_reason: FailureReason | None = Field(
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default=None,
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description="Reason for failure (if status != success)",
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)
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# === OBSERVED Data ===
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observed: ObservedFeatures = Field(description="Observed features (deterministic)")
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# === INFERRED Data ===
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outcome: CallOutcome = Field(description="Call outcome (inferred)")
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lost_sales_drivers: list[RCALabel] = Field(
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default_factory=list,
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description="Lost sales RCA labels",
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)
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poor_cx_drivers: list[RCALabel] = Field(
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default_factory=list,
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description="Poor CX RCA labels",
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)
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# === Traceability (REQUIRED) ===
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traceability: Traceability = Field(description="Version and audit metadata")
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# === Timestamps ===
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created_at: datetime = Field(default_factory=datetime.utcnow)
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# ============================================
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# COMPRESSED TRANSCRIPT (For LLM Input)
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# ============================================
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class CompressedTranscript(BaseModel):
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"""Compressed transcript for LLM inference - reduces token usage"""
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call_id: str = Field(description="Unique call identifier")
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customer_intent: str = Field(description="Summarized customer intent")
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agent_offers: list[str] = Field(
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default_factory=list,
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description="Key offers made by agent",
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)
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objections: list[str] = Field(
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default_factory=list,
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description="Customer objections",
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)
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resolution_statements: list[str] = Field(
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default_factory=list,
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description="Resolution statements",
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)
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key_exchanges: list[dict] = Field(
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default_factory=list,
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description="Key exchanges with timestamps",
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)
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original_token_count: int = Field(description="Tokens in original transcript")
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compressed_token_count: int = Field(description="Tokens after compression")
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compression_ratio: float = Field(
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ge=0.0,
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le=1.0,
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description="Compression ratio achieved",
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)
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# ============================================
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# BATCH MANIFEST
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# ============================================
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class BatchManifest(BaseModel):
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"""Manifest for a processing batch"""
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batch_id: str = Field(description="Unique batch identifier")
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total_calls: int = Field(description="Total calls in batch")
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processed_calls: int = Field(default=0, description="Calls processed")
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success_count: int = Field(default=0, description="Successful processing")
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partial_count: int = Field(default=0, description="Partial processing")
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failed_count: int = Field(default=0, description="Failed processing")
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status: str = Field(default="pending", description="Batch status")
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started_at: datetime | None = Field(default=None)
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completed_at: datetime | None = Field(default=None)
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traceability: Traceability = Field(description="Version metadata")
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Reference in New Issue
Block a user