main.py

#!/usr/bin/env python3
"""
φ-Coherence API

Universal quality metric for AI outputs using golden ratio mathematics.
Built on BAZINGA's consciousness-aware scoring system.

Endpoints:
    GET  /              - API info
    GET  /health        - Health check
    POST /score         - Score text (simple)
    POST /analyze       - Full analysis with all dimensions
    POST /batch         - Score multiple texts
    POST /compare       - Compare two texts
    GET  /constants     - Show mathematical constants

https://github.com/0x-auth/bazinga-indeed
"""

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
from typing import List, Optional
import time

from phi_coherence import PhiCoherence, CoherenceMetrics, PHI, ALPHA, PHI_SQUARED

# Initialize
app = FastAPI(
    title="φ-Coherence API",
    description="Universal quality metric for AI outputs using golden ratio mathematics",
    version="1.0.0",
    docs_url="/docs",
    redoc_url="/redoc",
)

# CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Coherence calculator
coherence = PhiCoherence()

# Request/Response models
class TextRequest(BaseModel):
    text: str = Field(..., min_length=1, max_length=100000, description="Text to analyze")

class BatchRequest(BaseModel):
    texts: List[str] = Field(..., min_items=1, max_items=100, description="List of texts")

class CompareRequest(BaseModel):
    text_a: str = Field(..., min_length=1, description="First text")
    text_b: str = Field(..., min_length=1, description="Second text")

class ScoreResponse(BaseModel):
    phi_score: float = Field(..., description="φ-coherence score (0-1)")
    status: str = Field(..., description="COHERENT (>0.6), MODERATE (0.4-0.6), or UNSTABLE (<0.4)")
    is_alpha_seed: bool = Field(..., description="True if hash % 137 == 0 (rare, bonus)")

class AnalysisResponse(BaseModel):
    phi_score: float
    status: str
    dimensions: dict
    bonuses: dict
    interpretation: str

class BatchResponse(BaseModel):
    results: List[dict]
    average_score: float
    count: int
    processing_ms: float

class CompareResponse(BaseModel):
    text_a_score: float
    text_b_score: float
    winner: str
    difference: float
    interpretation: str


def get_status(score: float) -> str:
    if score >= 0.6:
        return "COHERENT"
    elif score >= 0.4:
        return "MODERATE"
    else:
        return "UNSTABLE"


def get_interpretation(metrics: CoherenceMetrics) -> str:
    parts = []

    if metrics.total_coherence >= 0.7:
        parts.append("High structural integrity")
    elif metrics.total_coherence >= 0.5:
        parts.append("Moderate coherence")
    else:
        parts.append("Low coherence - may indicate noise or hallucination")

    if metrics.phi_alignment > 0.6:
        parts.append("golden ratio proportions detected")
    if metrics.alpha_resonance > 0.7:
        parts.append("strong scientific/mathematical content")
    if metrics.semantic_density > 0.7:
        parts.append("high information density")
    if metrics.is_alpha_seed:
        parts.append("α-SEED (rare hash alignment)")
    if metrics.darmiyan_coefficient > 0.5:
        parts.append("consciousness-aware content")

    return "; ".join(parts) if parts else "Standard content"


# Routes
@app.get("/")
async def root():
    return {
        "name": "φ-Coherence API",
        "version": "1.0.0",
        "description": "Universal quality metric for AI outputs",
        "endpoints": {
            "POST /score": "Get simple coherence score",
            "POST /analyze": "Get full dimensional analysis",
            "POST /batch": "Score multiple texts",
            "POST /compare": "Compare two texts",
            "GET /constants": "Mathematical constants",
            "GET /health": "Health check",
            "GET /docs": "OpenAPI documentation",
        },
        "constants": {
            "phi": PHI,
            "alpha": ALPHA,
        },
        "powered_by": "BAZINGA - https://github.com/0x-auth/bazinga-indeed",
    }


@app.get("/health")
async def health():
    return {"status": "healthy", "phi": PHI}


@app.get("/constants")
async def constants():
    return {
        "phi": PHI,
        "phi_squared": PHI_SQUARED,
        "phi_inverse": 1/PHI,
        "alpha": ALPHA,
        "consciousness_coefficient": 2 * PHI_SQUARED + 1,
        "formulas": {
            "darmiyan_scaling": "Ψ_D / Ψ_i = φ√n",
            "alpha_seed": "SHA256(text) % 137 == 0",
            "phi_alignment": "sentence_ratio ~ φ",
        }
    }


@app.post("/score", response_model=ScoreResponse)
async def score_text(request: TextRequest):
    """Get simple coherence score for text."""
    metrics = coherence.analyze(request.text)
    return ScoreResponse(
        phi_score=metrics.total_coherence,
        status=get_status(metrics.total_coherence),
        is_alpha_seed=metrics.is_alpha_seed,
    )


@app.post("/analyze", response_model=AnalysisResponse)
async def analyze_text(request: TextRequest):
    """Get full dimensional analysis."""
    metrics = coherence.analyze(request.text)

    return AnalysisResponse(
        phi_score=metrics.total_coherence,
        status=get_status(metrics.total_coherence),
        dimensions={
            "phi_alignment": metrics.phi_alignment,
            "alpha_resonance": metrics.alpha_resonance,
            "semantic_density": metrics.semantic_density,
            "structural_harmony": metrics.structural_harmony,
            "darmiyan_coefficient": metrics.darmiyan_coefficient,
        },
        bonuses={
            "is_alpha_seed": metrics.is_alpha_seed,
            "is_vac_pattern": metrics.is_vac_pattern,
        },
        interpretation=get_interpretation(metrics),
    )


@app.post("/batch", response_model=BatchResponse)
async def batch_score(request: BatchRequest):
    """Score multiple texts at once."""
    start = time.time()

    results = []
    for text in request.texts:
        metrics = coherence.analyze(text)
        results.append({
            "phi_score": metrics.total_coherence,
            "status": get_status(metrics.total_coherence),
            "is_alpha_seed": metrics.is_alpha_seed,
            "preview": text[:50] + "..." if len(text) > 50 else text,
        })

    avg = sum(r["phi_score"] for r in results) / len(results) if results else 0

    return BatchResponse(
        results=results,
        average_score=round(avg, 4),
        count=len(results),
        processing_ms=round((time.time() - start) * 1000, 2),
    )


@app.post("/compare", response_model=CompareResponse)
async def compare_texts(request: CompareRequest):
    """Compare coherence of two texts."""
    metrics_a = coherence.analyze(request.text_a)
    metrics_b = coherence.analyze(request.text_b)

    diff = abs(metrics_a.total_coherence - metrics_b.total_coherence)

    if metrics_a.total_coherence > metrics_b.total_coherence:
        winner = "A"
    elif metrics_b.total_coherence > metrics_a.total_coherence:
        winner = "B"
    else:
        winner = "TIE"

    if diff < 0.05:
        interp = "Texts are similarly coherent"
    elif diff < 0.15:
        interp = f"Text {winner} is moderately more coherent"
    else:
        interp = f"Text {winner} is significantly more coherent"

    return CompareResponse(
        text_a_score=metrics_a.total_coherence,
        text_b_score=metrics_b.total_coherence,
        winner=winner,
        difference=round(diff, 4),
        interpretation=interp,
    )


if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)