handler.py

"""
Text Feature Extraction — Hugging Face Inference Endpoint Handler

Extracts all 9 text features from conversation transcript:
  t0_explicit_free, t1_explicit_busy, t2_avg_resp_len, t3_short_ratio,
  t4_cognitive_load, t5_time_pressure, t6_deflection, t7_sentiment,
  t8_coherence, t9_latency

Derived from: src/text_features.py
"""

# ──────────────────────────────────────────────────────────────────────── #
# Imports from standardized modules
# ──────────────────────────────────────────────────────────────────────── #
try:
    from text_features import TextFeatureExtractor
except ImportError:
    import sys
    sys.path.append('.')
    from text_features import TextFeatureExtractor

# Initialize global extractor
print("[INFO] Initializing Global TextFeatureExtractor...")
# Preload models to avoid first-request latency in the Space runtime.
extractor = TextFeatureExtractor(use_intent_model=True, preload=True)


# ──────────────────────────────────────────────────────────────────────── #
# FastAPI handler for deployment
# ──────────────────────────────────────────────────────────────────────── #

from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from pydantic import BaseModel
from typing import Optional, List, Dict
import traceback
import numpy as np

# ──────────────────────────────────────────────────────────────────────── #
# Constants & Defaults
# ──────────────────────────────────────────────────────────────────────── #

DEFAULT_TEXT_FEATURES = {
    "t0_explicit_free": 0.0, "t1_explicit_busy": 0.0,
    "t2_avg_resp_len": 0.0, "t3_short_ratio": 0.0,
    "t4_cognitive_load": 0.0, "t5_time_pressure": 0.0,
    "t6_deflection": 0.0, "t7_sentiment": 0.0,
    "t8_coherence": 0.5, "t9_latency": 0.0,
}

app = FastAPI(title="Text Feature Extraction API", version="1.0.0")
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"], allow_credentials=True,
    allow_methods=["*"], allow_headers=["*"],
)


@app.exception_handler(Exception)
async def global_exception_handler(request: Request, exc: Exception):
    print(f"[GLOBAL ERROR] {request.url}: {exc}")
    traceback.print_exc()
    return JSONResponse(
        status_code=200,
        content={**DEFAULT_TEXT_FEATURES, "_error": str(exc), "_handler": "global"},
    )

class TextRequest(BaseModel):
    transcript: str = ""
    # Optional list of extra utterances if available
    utterances: List[str] = []
    question: str = ""
    events: Optional[List[Dict]] = None


@app.get("/")
async def root():
    return {
        "service": "Text Feature Extraction API",
        "version": "1.0.0",
        "endpoints": ["/health", "/extract-text-features"],
    }


@app.get("/health")
async def health():
    return {
        "status": "healthy",
        "intent_model_loaded": extractor.use_intent_model,
        "models_preloaded": True,
    }


@app.post("/extract-text-features")
async def extract_text_features(data: TextRequest):
    """Extract all 9 text features from transcript."""
    # Prepare inputs for TextFeatureExtractor.extract_all
    # It expects: transcript_list, full_transcript, question, events
    
    transcript_list = data.utterances
    if not transcript_list and data.transcript:
        transcript_list = [data.transcript]
    
    features = extractor.extract_all(
        transcript_list=transcript_list,
        full_transcript=data.transcript,
        question=data.question,
        events=data.events,
    )
    
    # Sanitize inputs to ensure floats
    sanitized = {}
    for k, v in features.items():
        if isinstance(v, float):
             sanitized[k] = 0.0 if np.isnan(v) or np.isinf(v) else v
        else:
             sanitized[k] = v
             
    return sanitized


if __name__ == "__main__":
    import uvicorn
    import os
    port = int(os.environ.get("PORT", 7860))
    uvicorn.run(app, host="0.0.0.0", port=port)